REVIEW Open Access

Quantitative analyses and modelling tosupport achievement of the 2020 goals fornine neglected tropical diseasesT. Déirdre Hollingsworth1*, Emily R. Adams2, Roy M. Anderson3, Katherine Atkins4, Sarah Bartsch5,María-Gloria Basáñez3, Matthew Behrend6, David J. Blok7, Lloyd A. C. Chapman1, Luc Coffeng7, Orin Courtenay1,Ron E. Crump1, Sake J. de Vlas7, Andy Dobson8, Louise Dyson1, Hajnal Farkas1, Alison P. Galvani9, Manoj Gambhir10,David Gurarie11, Michael A. Irvine1, Sarah Jervis1, Matt J. Keeling1, Louise Kelly-Hope2, Charles King11, Bruce Y. Lee5,Epke A. Le Rutte7, Thomas M. Lietman12, Martial Ndeffo-Mbah9, Graham F. Medley4, Edwin Michael13,Abhishek Pandey9, Jennifer K. Peterson8, Amy Pinsent10, Travis C. Porco12, Jan Hendrik Richardus7, Lisa Reimer2,Kat S. Rock1, Brajendra K. Singh13, Wilma Stolk7, Subramanian Swaminathan14, Steve J. Torr2, Jeffrey Townsend9,James Truscott3, Martin Walker3, Alexandra Zoueva15 and NTD Modelling Consortium

Abstract

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminatedisease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals forneglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyseswhich aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease,visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphaticfilariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: theimportance of epidemiological setting on the success of interventions; targeting groups who are at highest risk ofinfection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoirfor infection,. The results also highlight the challenge of maintaining elimination ‘as a public health problem’ whentrue elimination is not reached. The models elucidate the factors that may be contributing most to persistence ofdisease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall thiscollection presents new analyses to inform current control initiatives. These papers form a base from which furtherdevelopment of the models and more rigorous validation against a variety of datasets can help to give moredetailed advice. At the moment, the models’ predictions are being considered as the world prepares for a finalpush towards control or elimination of neglected tropical diseases by 2020.

Keywords: Modelling, Elimination, Neglected tropical diseases, Transmission, Chagas disease, Visceral leishmaniasis,Kala-azar, Human African trypanosomiasis, Leprosy, Soil-transmitted helminths, Schistosomiasis, Lymphatic filariasis,Onchocerciasis, Trachoma, Mass drug administration, Preventive chemotherapy

* Correspondence: deirdre.hollingsworth@warwick.ac.uk1University of Warwick, Coventry CV4 7AL, UKFull list of author information is available at the end of the article

© 2015 Hollingsworth et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Hollingsworth et al. Parasites & Vectors (2015) 8:630 DOI 10.1186/s13071-015-1235-1

BackgroundNeglected tropical diseases (NTDs) continue to create sig-nificant levels of suffering and morbidity throughout thetropical world. They affect over a billion people and accom-pany HIV/AIDS, tuberculosis and malaria as the classic‘diseases of poverty’ [1]. Considerable evidence suggeststhat NTDs place major constraints on economic develop-ment in most tropical countries [2]. The potential for large-scale improvements in health equity by tackling these dis-eases has been recognised in recent years by large-scale in-vestment in controlling them. In January 2012, the WorldHealth Organization (WHO), laid out a roadmap for con-trolling the burden of morbidity of neglected tropical dis-eases [3]. This included goals for achieving control, localelimination “as a public health problem”, or reduction indisease burden to low levels by 2020. The London Declar-ation on NTDs, signed in 2012, demonstrated the supportof the pharmaceutical industry, governments and non-governmental agencies for the achievement of these goalsfor ten diseases. Of these, one, Guinea worm, was targetedfor eradication. The remaining nine, lymphatic filariasis,leprosy, human African trypanosomiasis, blinding trach-oma, schistosomiasis, soil-transmitted helminthiasis, Cha-gas disease, visceral leishmaniasis, and onchocerciasis(Table 1) were targeted for control or “elimination as a pub-lic health problem.”. Elimination as a public health problemis defined differently for each disease, with individual dis-ease goals set in accordance with the epidemiology of eachdisease. Elimination as a public health problem as definedby WHO does not necessarily require a break in transmis-sion, rather a dramatic cut in disease incidence orprevalence.In the wake of the London Declaration a need has been

identified for epidemiological modelling to aid controlpolicy design and evaluation. Although the epidemio-logical modelling of NTDs has a long history [4, 5], it hasbeen limited by both a lack of interest from funders andlimited epidemiological data on which to base the models.In order to address this need, an international team of epi-demiological modellers were brought together to form theNTD Modelling Consortium. Members of the consortiumwere asked to provide quantitative analyses to support theNTD control efforts by

� validating current strategies,� suggesting more impactful strategies,� evaluating new tools as they arise from on-going

studies,� providing guidance on what the ‘end game’, beyond

the 2020 goals, might look like.

Alongside this core project, the methods and modelsdeveloped by members of the consortium have the po-tential to

� help countries understand whether they are on-trackto WHO goals and, if not, how long and what strat-egies are needed to get there

� give countries guidance on when and how to bestcheck on progress

� provide guidance on certification of elimination

There would also be opportunities for extending NTDmodels to include cost effectiveness and provide toolsfor policy at a local level, depending on the quality ofthe models and available data.Importantly, for each of the diseases in this core re-

search (Table 1), the research team includes two or threemodelling groups per disease, to provide scientific ro-bustness through investigating the same questions usinga variety of approaches, mirroring other modelling con-sortia. The NTD Modelling Consortium is unusualamongst existing modelling consortia because it crossesa number of epidemiologically distinct infections, withdifferent types of etiological agents and modes of trans-mission (Table 1). This diversity of diseases studied andthe range of research groups and approaches allows theconsortium to exploit similarities between diseases, suchas vector-borne dynamics or the impact of mass drugadministration (MDA), broadening the scientific basefrom which the analyses are motivated. In addition theresearch teams can work together to address commonproblems such as clarity on definitions and sharing goodquality data. The group are also discussing differentmethodologies and techniques for model validation, test-ing and comparison.The first analyses of these nine diseases by this re-

search team has been presented as a collection in Para-sites and Vectors (http://www.parasitesandvectors.com/series/ntdmodels2015) The analyses range from develop-ing completely new models of diseases for which the epi-demiology is still highly uncertain to bringing togethermodels with a long history in order to achieve consensuson best strategies to achieve the 2020 goals. This paperreviews these results with the aims of

– Introducing the collection to non-modellers– Introducing the collection to modellers from related

fields– Highlighting the key new policy insights– Providing an overview across papers in the same

disease– Providing an overview across diseases

The main part of this paper takes the reader throughthe analyses disease by disease, starting with diseasesthat are being treated through preventive chemotherapy(PCT) (lymphatic filariasis, onchocerciasis, schistosomia-sis, soil transmitted helminthiasis and trachoma)

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Table 1 Summary of the nine neglected tropical diseases studied in these papers, where elimination refers to elimination as a public health problem. Data sources: WHO

Name Transmission Global picture Interventions WHO target for 2020

Preventive chemotherapy (PCT) diseases, controlled by mass drug administration (MDA) programmes

Lymphatic filariasis (elephantiasis) Worm transmitted by mosquito Tropical and subtropical countries in Africa,Asia, the Western Pacific, the Caribbean andSouth America

Annual/biannual MDA (ivermectin,albendazole and DEC), vector controlthrough insecticide-treatedbed nets or spraying

Global elimination

Onchocerciasis (river blindness) Worm transmitted by black fly Primarily occurs in tropical sub-SaharanAfrica (99 % of cases)

MDA (ivermectin) and vector control Country elimination

Schistosomiasis (bilharzia) Intestinal worm, water-bornetransmission with snail intermediatehost

Affect at least 240 million people worldwide.Most commonly found in Africa, as well asAsia and South-America

MDA (praziquantel) to school-agechildren and high-risk adults, alongwith WASH and possible snailcontrol

Regional and countryelimination

Soil-transmitted helminthiasis (roundworm,whipworm, hookworm)

Intestinal worms transmitted via soilcontaminated with fecal matter

Over 1 billion people affected, particularlyin sub-Saharan Africa, India and SoutheastAsian countries

MDA (albendazole, mebendazole)treatment of school-aged children.Treatment of pre-school agedchildren and women of childbearingage is also recommended.

75 % coverage with(bi)annual PCT

Blinding trachoma Bacterial infection transmitted by flies,fingers and fomites.

84 million active cases globally. MDA (azithromycin) and surgery,along with improved hygiene

Global elimination

Intensified disease management (IDM) diseases, controlled by increased diagnosis and management of cases

Chagas disease Protozoan transmitted by triatomines(kissing bugs)

8 million infected in the Americas, 10,000deaths per year.

Spraying with indoor residual insecticides,housing improvements.

Regional elimination

HAT (sleeping sickness), Gambian form Protozoan transmitted by tsetse fly <4000 new cases in 2014 Treatment, active/mass screening andvector control with tsetse targets.

Global elimination

Leprosy Bacterium with unclear mode oftransmission: contact or droplet likely

200,000 new diagnoses per year, >80 %from India, Brazil and Indonesia

Early diagnosis and treatment Global elimination

Visceral leishmaniasis (kala-azar) in the Indiansub-continent

Protozoan transmitted by sand fly 200,000–400,000 cases annually, 80 % inIndian sub-continent.

Indoor residual spraying of insecticides,insecticide-treated bed nets, active casedetection, rapid diagnosis and treatment

Regional elimination

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followed by the intensified disease management (IDM)diseases (Chagas disease, the Gambian form of humanAfrican trypanosomiasis, leprosy and visceral leishman-iasis in the Indian sub-continent). These disease-specificsections are followed by a discussion of general lessonslearned and next steps.

Preventive chemotherapy diseasesPreventive chemotherapy and transmission control(PCT) is the main strategy for control of onchocerciasis,lymphatic filariasis, schistosomiasis, soil-transmitted hel-minthiasis and trachoma. The strategy involves regularprovision of preventive treatment (in the form of massdrug administration (MDA) campaigns) to entire popu-lations or targeted risk groups (e.g., schoolchildren). Thisstrategy reduces disease progression in treated individ-uals and prevents transmission of infection to others.Mass drug administration (MDA) programmes are rap-idly expanding, although important questions remain.For example, will the planned MDA programmes be suf-ficient to achieve elimination in all epidemiologicalsettings? To what extent is successful elimination jeopar-dized by low coverage and systematic non-adherence?When, and on the basis of what criteria, can MDA besafely interrupted [6]? Several of the modelling analyseshighlight the importance of groups who systematically,or semi-systematically do not access MDA programs insustaining transmission. This potential for underminingprogram success is particularly acute if groups of thepopulation who are most at risk through their behav-iours (e.g., those who most frequently go to the river)are also those who are most difficult to access throughan MDA campaign. The results support previous ana-lyses that increased coverage, across different agegroups, or through general coverage, may be more im-portant than frequency of treatment.

Lymphatic filariasisBackgroundLymphatic filariasis (LF) is caused by a group ofmosquito-borne filarial nematodes (Wuchereria bancrofti(responsible for 90 % of cases), Brugia malayi or Brugiatimori) and can lead to chronic morbidity, such aslymphedema, which is associated with pain, severe dis-ability and resulting social stigmatisation [7–9]. About1.2 billion people are at risk of LF in tropical andsubtropical countries in Africa, Asia, the WesternPacific, the Caribbean and South America. The GlobalProgramme to Eliminate Lymphatic Filariasis (GPELF)was launched in 2000, aiming to eliminate the disease asa public health problem by 2020 by mass drug administra-tion (MDA). In areas co-endemic with onchocerciasis, thecombination of drugs used in MDA is ivermectin (IVM)and albendazole (ALB), whereas diethylcarbamazine

(DEC) and ALB are used in other endemic regions. Thecurrent MDA strategy is to have yearly treatment at 65 %coverage of the total population for at least 5 years,followed by regular transmission assessments to identifywhether transmission has been broken. Morbidity man-agement will accompany initiation of MDA programmes.A number of countries have reached the targets of

stopping MDA and interrupting transmission, whileothers have scaled up their treatment programmes andare getting close to these targets, by reducing the risk ofinfection for hundreds of millions of people [10]. How-ever, there are still large numbers of affected popula-tions, who are predominantly in sub-Saharan Africa, andunlikely to receive the minimum 5 rounds of treatmentby 2020. In such areas, adjusted strategies may beneeded to accelerate elimination.

Modelling approachesThree distinct models have been used to evaluate the2020 goals in a number of key settings [11–13]. Allmodels capture heterogeneity in individuals’ exposure,whilst there exist differences in assumed acquired im-munity and filarial worm biology. The model by Irvineet al. is an individual-based microsimulation. Model pre-dictions were tested by fitting to the age-profile of infec-tion in a survey prior to (Kenya) [14] and during anintervention (Sri Lanka) [15] and predicting forward thesimulated microfilariae (mf) intensity distribution andprevalence in subsequent years was compared and foundto be in good agreement to the data, but there were dis-crepancies in ICT prevalence.Jambulingam et al. used the established individual-

based, stochastic microsimulation model, LYMFASIM,taking into account variability in immunity, transmissionpotential and individual efficacy of MDA. The modelwas fitted to age-specific, longitudinal data describingthe impact of integrated vector management on the in-tensity of Wuchereria bancrofti infection in Pondicherry,India [16].Singh et al. [12] used a deterministic and age-

structured model of genus-specific LF transmission. Themodel was calibrated using 22 pre-control settings fromAfrica, South East Asia and Papua New Guinea. Fittingwas performed in a Bayesian melding framework to mfage-prevalence in a pre-control setting.

Policy implicationsIrvine et al. identify a number of key areas that are im-portant to address with regards to an eliminationprogramme (Fig. 1a) [11]. Over a five-year timeline,twice-yearly annual MDA at 65 % coverage was found tobe the most effective of all strategies considered. How-ever, if twice-yearly MDA is not feasible, then an MDAprogramme combined with vector control (VC) can also

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have a similarly high probability of success in all settings.Annual MDA at 80 % coverage with no VC was foundto be only effective in low and medium settings (lessthan 15 % mf prevalence) and annual MDA at 65 %coverage was found to be only effective for lower en-demic settings (less than 10 % mf prevalence). A numberof systematic adherence issues were found impact thesuccess of a programme such as individuals who are notaccessing the intervention also having higher risk of in-fection; use of long lasting insecticidal nets (LLINs) be-ing correlated with adherence to MDA for an individual;and systematic compliance to MDA.The model of Singh et al. indicates heterogeneity in

local transmission and extinction dynamics vary greatlybetween settings (Fig. 1c) [12]. They showed that time-lines to parasite elimination in response to the currentMDA and various proposed MDA strategies with vectorcontrol also varied significantly between the study sites.Assessment of annual bite rates without the presence ofvector control highlighted that very low prevalence is re-quired to achieve true elimination because the subse-quent probability of recrudescence is very high (between69 and 100 %). Including VC, however, markedly reducesthe duration of interventions required to achieve elimin-ation as well as decreasing the risk of recrudescence.Jambulingam et al. use their model to investigate the

required duration of MDA to achieve elimination and toassess how low the microfilaraemia and antigenaemiaprevalence must be to ensure elimination (Fig. 1b) [13].The required number of treatment rounds for achievingelimination was found to depend strongly on local trans-mission conditions (reflected in baseline endemicity) andachieved coverage. For instance, in low endemic settingsas few as 2 rounds might be sufficient if coverage is as

high as 80 %, while annual MDA may have to continuefor >10 years in high endemic areas if coverage is as lowas 50 %. The study also shows that the critical thresholdsused as endpoints for MDA, will be dependent on localtransmission conditions: in low-transmission settings(low baseline endemicity) more residual infection mayremain than in high-transmission settings (high baselineendemicity), because the relatively low biting rate in theformer prevents resurgence of infection.Although different modelling approaches were used,

all models agree that timelines to LF elimination will de-pend on the epidemiological conditions and achievedcoverage. These findings have important implications foron-going elimination programmes that should be takeninto account in monitoring and evaluation. Transmissionassessment surveys should ideally be targeted at the siteswith the highest transmission intensity and lowest cover-age: once elimination is achieved in these settings, itshould also be achieved in other settings where condi-tions are more favourable for elimination.

Knowledge gaps and next stepsAll three LF models have been fitted against mf preva-lence data stratified by age. The use of mf and circulat-ing filarial antigen (CFA) intensity measurements, wheresuch studies are available, would greatly enhance the fitof the models to provide further insight into key under-lying assumptions on exposure and immunity heterogen-eity. A more direct comparison of the models forparticular settings would further establish the systematicuncertainty between the models.All three models need to be quantified and validated

against disease prevalence by incorporating knowledgeon disease dynamics and progression. This can help with

Fig. 1 Schematic of LF results. The results include: a) highlighting that heterogeneity in human exposure and intervention greatly alters time toelimination by Irvine et al. [11]; b) a description of the association between antigenaemia and the presence of adult worms by Jambulinga et al.[13]; and c) a Bayesian fitting methodology of a deterministic model including information on model inputs and outputs by Singh et al. [12]

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setting new targets to reach the goal of LF eliminationas a public health problem and identify aspects that needto be addressed to achieve this target. Models have to bemade user friendly with minimum inputs/outputs for ap-plication in decision making and evaluation byprogramme managers [17].

OnchocerciasisBackgroundHuman onchocerciasis is a disease caused by the filarialnematode Onchocerca volvulus and transmitted by black-fly vectors. Chronic infection can lead to skin disease, vis-ual impairment and eventually blindness. It occursprimarily in tropical sub-Saharan Africa but some foci alsoexist in Yemen and Latin America. In recent decades, thedisease burden of onchocerciasis has been greatly reducedby the Onchocerciasis Control Programme in West Africa(OCP, 1974–2002), the African Programme for Onchocer-ciasis Control (APOC, 1995–2015) and the Onchocercia-sis Elimination Program for the Americas (OEPA, 1991-present).In the Americas, OEPA has successfully interrupted

transmission in most foci through 6- or 3-monthly massdrug administration (MDA) of ivermectin [18–23]. Annualor biannual ivermectin distribution has also eliminated on-chocerciasis from several African foci [24, 25], althoughelsewhere transmission is on going despite prolonged MDA[26, 27]. In view of this evidence, the World HealthOrganization (WHO) set ambitious targets for the elimin-ation of onchocerciasis, which is to be achieved by 2015 inthe Americas and Yemen, by 2020 in selected Africancountries, and by 2025 in 80 % of African countries [3, 28].

Modelling approachesThe individual-based microsimulation model, ONCHO-SIM [29, 30] and the population-based deterministicmodel EPIONCHO [31–33] have been developed

independently at Erasmus MC and Imperial CollegeLondon respectively.A comparative modelling study is presented which ex-

plores the level of agreement between EPIONCHO andONCHOSIM in estimates of the required durations toeliminate onchocerciasis. After harmonization of key in-put assumptions, predictions were made for a range ofepidemiological settings (from mesoendemic to veryhighly hyperendemic or holoendemic) and program-matic (annual or biannual MDA at variable levels ofpopulation coverage) characteristics.Simulation endpoints were defined by two criteria: (1)

the duration of MDA required to reduce the mf preva-lence below a threshold of 1.4 % (this is the provisionaloperational threshold for treatment interruptionfollowed by surveillance (pOTTIS); and (2) the durationof MDA required to drive the parasite to local elimin-ation. This was determined by reaching the transmissionbreakpoint in EPIONCHO and by a high (99 %) prob-ability of stochastic fadeout in ONCHOSIM.

Policy implicationsBoth EPIONCHO and ONCHOSIM indicate that elim-ination of onchocerciasis is feasible in mesoendemic set-tings by annual MDA with ivermectin alone (Fig. 2). Themodels’ predictions regarding the feasibility of elimin-ation in settings with higher endemicity are more diver-gent, however, with ONCHOSIM being more optimisticthan EPIONCHO. Both models agree that neither an-nual nor bi-annual MDA will achieve elimination inholo-endemic areas within a reasonable timeframe.Therefore, in highly endemic settings alternative inter-vention strategies should be considered.More work is needed to validate the mf prevalence

threshold used as endpoint for MDA. Results from theONCHOSIM simulations indicate that, the 1.4 % thresh-old was too low for lower endemicity settings, resulting

Fig. 2 Schematic of onchocerciasis results. The results include a comparison of a stochastic individual-based model (ONCHOSIM) and a deterministicpopulation-based model (EPIONCHO) and an investigation into the impact of systematic non-adherence in different endemicity settings by Stolk et al. [71]

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in unnecessary long continuation of MDA. The oppositeis true at higher endemicity settings; the time requiredto reach the pOTTIS is shorter than the time requiredto drive the parasite population to elimination. In prac-tice, the decision to stop is made for entire areas, withvarying endemicity levels at baseline. The critical thresh-old should be set low enough to ensure elimination inthe sites with highest transmission.

Knowledge gaps and next stepsDifferences between EPIONCHO and ONCHOSIM inthe projected infection dynamics and required durationsto reach elimination will be further investigated to fullyunderstand the strengths and weakness of the two con-trasting modelling approaches. Ultimately a process ofcomparison, validation and refinement-followed byfinescale locale projections will help to reach consensuson optimising intervention strategies to reach the globalhealth communities’ elimination objectives across Africa.In order to perform these analyses, the researchers willrequire access to similar datasets from long-term pro-grammes. Through testing both model predictionsagainst these data, there can be increased confidence inthe predictions on how altered strategies can be used toincrease the probability of elimination.

SchistosomiasisBackgroundSchistosomiasis, or bilharzia, is caused by the adultworms and eggs of trematode flatworms of the genusSchistosoma. The adult worms live in the blood vesselswhere the females release eggs which are then passedout of the body in urine or faeces. In freshwater theseeggs then infect snails, which subsequently release lar-vae which pass into the skin during contact with water.Eggs released in the body cause inflammation and scar-ring of internal organs, leading to negative develop-mental outcomes for children and adult pathology.Highest prevalence is seen in children, who are targetedfor school-based deworming, which aims to controlmorbidity. At-risk adults are also often targeted, how-ever, the target of eliminating transmission may requireadditional interventions, including water sanitation andhygiene (WASH) as well as snail control.Current WHO guidelines define broad prevalence

bands to indicate how school-age treatment shouldproceed. Models can be used to investigate the impact ofthis approach and update the guidelines to give them astronger scientific underpinning. However, it is expectedthat current WHO control recommendations will needto be substantially revised based on the WHA shift to-ward 2020 elimination goals. The findings of presentmodelling efforts, and the use of further ad hoc model-based projections for different treatment scenarios, will

be able to inform the development of the next gener-ation of more evidence-based WHO policy recommen-dations for schistosomiasis control.

Modelling approachesModelling has been used to address many of the oper-ational questions around frequency and needed coverageof schistosomiasis treatment, but until now, rarely beenused to directly assess and predict the impact of PCT-MDA control programmes.The basic aims were to fit two existing models to

available detailed data for each parasite species, and todetermine the likely long-term impact of current select-ive or MDA control programs to identify optimum anti-helminthic treatment schedules to control schistosomeinfection. The models sought to define these schedulesfor low, medium and high transmission settings.Two modelling approaches are proposed in the

current issue: one of them employs mean worm burdenformulation for age-structured populations [34], anotherone is based on stratified worm burden setup. Bothmodelling approaches incorporate the essential featuresof in-host biology, such as worm mating and density-dependent fecundity. The principal difference betweenmodels lies in their underlying assumptions: the hypoth-esized “negative binomial” worm burden distribution[35], and the assumption-free “dynamic” worm strata(with prescribed patterns of egg release) [36].Anderson et al. [35] reconstructed the global trend in

MDA coverage from the mean of national coveragedata across endemic countries. This trend was then ex-tended to estimate the likelihood of reaching the 2020coverage target. These treatment estimates were thenused to project changes in average worm burdens up toand beyond 2020.Gurarie et al. [34] based their analysis on earlier cali-

brated models of Kenyan communities, and newer datasets from the SCORE study in Mozambique. The shortterm analysis assessed prevalence reduction underSCORE regimens through the year 2020. The long termanalysis explored feasibility of specific target reductionover 30-year period under different control scenarios.

Policy implicationsLong-term control predictions of two model types diddiffer in several respects. Specifically, the key ingredientsof this model, as employed in its analysis and simula-tions, follow MDA impact on basic reproduction num-ber, R0, and whether transmission breakpoints (resultingfrom the underlying assumptions on worm distribution)can be reached. Anderson et al. thus predict that persist-ent long term MDA control can bring about eliminationof Schistosoma mansoni transmission (Fig. 3b), but thiswas not the case for Gurarie et al. (Fig. 3a). The

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stratified worm burden systems in the model of Gurarieet al. suggest that breakpoints may not exist or theycould be too low to be practically relevant (see [34]). Animportant implication of control analysis by Gurarie etal. is that MDA alone may not bring about elimination,or sustained low-level infection, even under moderate-to-low transmission intensity. Any successful end-gamestrategy will require additional interventions, includingsnail control, environmental and behavioral modifica-tions related to exposure, sanitation, possibly with theaid of vaccines.Anderson et al. predict that the current trend in MDA

coverage, extrapolated to 2020, will have a major impacton Schistosoma mansoni burdens overall, with reduc-tions of around 85 % by 2020 and elimination within thefollowing decade in low transmission settings. Sensitivityanalysis suggests that some coverage of adults is essen-tial to achieve elimination but little is to be gained intransmission blocking by treating young children (pre-school aged children). However, higher levels of adultcoverage show diminishing returns in terms ofeffectiveness.Of note, the two groups’ models agreed regarding the

need to achieve high levels of treatment coverage withmore frequent drug delivery (at least annual) for best ef-fect, particularly in high transmission settings. The on-going research will elucidate some of these issues,

including the value of mixed interventions, and help tofurther develop optimal control strategies.

Knowledge gaps and next stepsResults from validation against re-infection data suggestthat other mechanisms are necessary to accurately re-produce the age profile of infection after treatment. Akey difficulty is being able to resolve the influence ofage-dependent force of infection and immune responsemechanisms. Considerable inroads into the under-standing of this complex area have already been made[37, 38]. Combining these approaches with high qualityre-infection data should allow the contributions of dif-ferent mechanisms to be more thoroughly teased out.However, an essential component will be the availabilityof high-quality longitudinal re-infection data, ideally atthe individual level, which is proving difficult to obtain.The interpretation of raw data is hampered by issues

with current diagnostic techniques. Models of helminthtransmission are based around representations of wormnumbers within hosts, but the connection betweenworm burdens and the output of egg-counting diagnos-tic techniques, such as Kato-Katz, are not well charac-terised, although it is known that sensitivities can bequite low. Antigen and antibody based techniques prom-ise more sensitive techniques, but lose the quantitative

Fig. 3 Schematic of schistosomiasis results. The results include: a) an assessment of the potential success of MDA in different scenarios using adeterministic modelling framework by Gurarie et al. [36]; and b) an investigation into the feasibility of elimination using an age-structureddeterministic model by Anderson et al. [35]

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nature of the egg counts and will require careful calibra-tion of the models [39].The schistosomiasis researchers will continue to study

the impact of school-based and community based inter-ventions on both S. mansoni and S. haematobiumthrough more detailed analysis of epidemiological stud-ies, addressing the urgent need for these models to betested against multiple settings. They will also considerthe effect of WASH and snail control, where such dataare available. The aim will be to provide guidance onwhich areas will need which interventions for controland elimination.

Soil-transmitted helminthiasisBackgroundGlobally, over 1 billion people are infected with soil-transmitted helminths (STH). The three major STH spe-cies targeted for control are Ascaris lumbricoides(roundworm) and Trichuris trichiura (whipworm), bothof which tend to exhibit highest prevalence and intensityamongst children, and hookworm (Necator americanusand Ancyclostoma), which tends to have highest preva-lence and intensity amongst adults.In recognition of the STH disease burden, the WHO

has set the target of implementing annual or semi-annual MDA for pre-school- (preSAC) and school-agedchildren (SAC) and women of childbearing age (WCBA)in endemic areas with an overall coverage of at least75 % by 2020. The associated parasitological goal is toachieve <1 % prevalence of moderate-to-heavy intensityinfection in these target populations (and thus preventmost morbidity). However, given that current programsmostly target preSAC and SAC, the feasibility of control-ling STH by 2020 with current strategies can be ques-tioned, in particular for hookworm, which is mostabundantly present in adults.The WHO goals and treatment guidelines do not dif-

ferentiate among the individual species that make up theSTH group, but categorise the treatment approach pri-marily in terms of overall STH prevalence. In terms oflife-cycle and natural history within the host, this is areasonable assumption, although behaviour outside thehost differs but it ignores the significant quantitative dif-ferences between species. Additionally, the guidelinesonly consider a narrow range of responses to STHprevalence (no treatment, annual or biannual treatment).This is motivated by a desire to directly and cost-effectively reduce morbidity in children, who are a keyrisk group. However, it ignores the possible long-termbenefits of an approach that could reduce the contribu-tions of the whole community to transmission, therebyleading to transmission break and cessation of annual orbi-annual treatment altogether.

The three species within STH have significant differ-ences in age-intensity profiles, worm fecundity, and re-sponse to treatment. The qualitative range profilesindicate different distributions of worm burdens as wellas different forces of infection by age for the three spe-cies. Further differences between species are indicatedby large differences in worm burden and the characteris-tics of worm fecundity between species, as indicated byworm expulsion studies. A further key difference in thecontext of chemotherapeutic control strategies is the re-sponse of the three species to treatment with the stand-ard anthelminthic drugs, albendazole and mebendazole:While these drugs are highly effective against Ascarisand, to some extent, hookworm, efficacy against Tri-churis is much lower, which could have an effect on thechoice of control strategy.

Modelling approachesIn this collection there are two models addressing con-trol and elimination of the different soil-transmitted hel-minths. Coffeng et al. presented WORMSIM, anindividual-based model for control by 2020 [40]. WithWORMSIM, the researchers synthesised relevant avail-able information on hookworm biology, and capture het-erogeneities in transmission and MDA participation.The model predictions were compared to longitudinalparasitological data in WCBA from Vietnam spanningfive years, collected pre-control and during PC. For vary-ing levels of pre-control endemicity, the researchers pre-dicted the impact of currently recommended MDAstrategies, as well as the impact of more intense strat-egies (higher frequency and coverage of MDA), healtheducation and improved access to WASH, and system-atic non-participation of individuals in MDA programs.The approach of Truscott et al. was to use a determin-

istic age-structured model to describe the dynamics ofthe parasites within the host population and the impactof increasing levels of MDA coverage [41]. Stochastic in-dividual based models were also constructed by Truscottet al. but the average predictions were identical to thedeterministic model and hence the main focus in theirpaper is on the deterministic outcomes. The same basicmodel structure is employed for each of the STH spe-cies, reflecting the very similar life-cycles of the threespecies, but the parameterization in each case is basedon species-specific data taken from baseline age profilesand expulsion studies. As a result, the dynamics of themodel in response to MDA is quite different for eachspecies. The accuracy of the model in describing theevolution of worm burden under MDA was tested forAscaris against longitudinal baseline and reinfectiondata. Model results are in broad agreement with thedata, with some discrepancies in individual age groups.To drive the changes in worm burden up to and beyond

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2020, a long-term trend in MDA coverage was con-structed to drive control and, potentially, elimination ofparasites. The trend was based on WHO records of aver-age national coverage in SAC and pre-SAC in endemiccountries, interpolated forward in time to meet the pro-posed 2020 goals or 75 % in SAC and pre-SAC. The datasuggests that current trends in MDA coverage are ap-proximately in line with reaching the stated goals by2020. Both models employed in this study are amenableto the implementation of multiple forms of MDA, tar-geting multiple helminth species using different drugs.Detailed sensitivity analyses for parameter uncertaintywere performed as were validation studies using reinfec-tion data post chemotherapy using parameter estimatesderived independently form the reinfection data.

Policy implicationsThe predictions by WORMSIM [40] confirm that toachieve control of hookworm morbidity, women ofchildbearing age have to be targeted with PC (Fig. 4b).Furthermore, Coffeng et al. conclude that to achievecontrol in highly endemic areas, the drug albendazoleshould be preferred over mebendazole, and possibly add-itional interventions such as health education and im-proved access to WASH are needed (Fig. 4a). They alsoillustrate how systematic non-participation to PC under-mines programme effectiveness, even during high-frequency PC.Results from Truscott et al. [41] show that the impact

of the recent and planned increases in MDA coveragewill depend strongly on species. For Ascaris, worm bur-den across the host population is reduced by 70 % by

2020, leading to elimination within the following decadeif coverage levels are maintained. The reduced efficacyof albendazole against Trichuris mitigates the effect oftreatment against the species, achieving only a 44 % re-duction in worm burden with no possibility of elimin-ation with continued target levels of coverage. Forhookworm, the MDA is even less effective, due to thebulk of worm burden (>70 %) being in adults who areoutside the treatment regime.The implications are that the treatment response to

STH needs to be tuned to reflect the dominant speciesin a given area. Where that species is Trichuris or hook-worm, approaches beyond treatment of SAC may needto be considered, particularly where transmission is high.For hookworm, some degree of treatment of adults willbe necessary to significantly reduce burden or achieveelimination. For Trichuris, a higher efficacy drug ormore frequent treatment could potentially be highly ef-fective at reducing worm burden.

Knowledge gaps and next stepsAs for schistosomiasis (above), the predictions of the im-pact of age-based deworming programmes depend onthe assumptions of the contribution of different agegroups to transmission and of acquisition of infectionthrough a shared exposure to the ‘infective pool’. Theyalso highlight the challenges of interpreting Kato Katz,although, unlike schistosomiasis, historic studies of therelationship between egg output and adult worm burdenmake the problem slightly less acute.Next steps for these groups are to extend their model

validation to more species and multiple settings, and to

Fig. 4 Schematic of STH results. The schematic includes results from: a) a deterministic transmission model by Truscott et al. applied to Ascaris,Trichuris and hookworm [41]; and b) a stochastic, individual based model of hookworm transmission by Coffeng et al. [40]

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do a more systematic model comparison of their predic-tions to quantitative guidance on thresholds for differenttreatment coverage.

TrachomaBackgroundTrachoma remains the world’s leading cause of infec-tious blindness [42]. Repeated ocular infection with thebacterium Chlamydia trachomatis leads to episodes ofconjunctival inflammation. With repeated infection thisinflammation can progress to scarring. The resultantscarring leads to the in-turning of eyelashes, known astrachomatous trichiasis (TT) which abrade the cornealsurface of the eye, ultimately resulting in blindness [43].It is currently estimated that 84 million individuals areliving with active disease, where the highest burden ofinfection is concentrated in young children [42]. Inaddition, 1.2 million people are estimated to be blind asa result of infection [42]. While there has been somesuccess at controlling trachoma infection, it remains en-demic in 50 countries.The WHO aims to control infection and eliminate

trachoma as a public health problem by 2020 [43]. Tohelp achieve this, the WHO supports the implementa-tion of the SAFE strategy: Surgery for trichiasis, Anti-biotics for treatment, and Facial cleanliness andEnvironmental improvements to reduce the probabil-ity of transmission [43]. Effective control relies on thesuccessful implementation of antibiotic treatment aswell as long term reductions in the overall level oftransmission. The decision to declare that trachomahas been controlled within a community or whether ornot further antibiotic treatment is required is based on theprevalence of trachomatous inflammation–follicular (TF)in children aged 1–9 years [43]. However, it is possiblethat other surveillance data sources, such as trachomatousinflammation-intense (TI) prevalence or the detection ofactive chlamydial infection through PCR may provideadditional information on the dynamics of transmissionwithin the population [44]. This can help to assesswhether sustained control is being achieved or whether in-fection is remerging.

Modelling approachesTwo distinct models were developed to address two keyareas in trachoma transmission control and surveillance.The developed model by Gambhir and Pinsent [45] was adeterministic susceptible, infected, susceptible (SIS) trans-mission model, which was age-structured and followed indi-viduals from their first infection to their last (a ‘ladder ofinfection’), and accounted for the development of immunitywithin the population as the number of infections experi-enced increased. This model assessed the impact of multipleannual rounds of MDA and the implementation of F and E

on the long-term transmission dynamics of infection, withinthree different transmission settings. In addition, the shortand medium-term impact on the effective reproductionnumber, Re, was also assessed within each transmission set-ting, as a measure of the potential for post-treatment infec-tion rebound.Liu et al. based their model on a stochastic SIS process

[44]. The model was a hidden Markov process of infec-tion at the community level, and numerical evaluation ofthe Kolmogorov forward equations enabled straightfor-ward likelihood fitting based on clinical trial data fromthe Niger arm of the Partnership for the Rapid Elimin-ation of Trachoma (PRET) study. Model fitting utilizedseveral observations, including PCR data, the clinicalsign TF, and the clinical sign TI. Because TF guides pol-icy and intervention, we produced forecasts of future ob-servations of TF, thereby evaluating model predictionson a test set separate from the training set. Both TI andlaboratory infection tests led to moderate, but not sig-nificant, improvement in forecasting the future level ofinfection within the community and that including adelay in TF recovery improves forecasting.

Policy implicationsGambhir et al. suggest that a combination of MDA andreductions in the overall level of transmission withinboth high and low transmission communities would en-sure that long-term control of transmission could beachieved (Fig. 5a). These control measures result in theoverall number of infections experienced by an individ-ual in the community at any point in time becominglower than prior to the introduction of the interventions.However, the rapid and dramatic reductions in transmis-sion that may occur due to these interventions may re-sult in a slower acquisition of immunity to infection.This may mean that although individuals are getting in-fected less frequently, when they do they have a higherinfectivity and are infectious for longer. To monitorthese potentially adverse outcomes it may be importantto collect infection samples from a sub-section of theadult population, as well as young children to ensurethat reductions in population level immunity are notoccurring.Liu et al. designed a model to assess which data

sources are more informative for predicting the futurestate of infection in a community (Fig. 5b). They sug-gested that TF data alone was just as informative forforecasting the future level of infection in the commu-nity as when TF, TI and PCR data were combined. If ap-plied to data from particular settings, the model can beused to determine which regions are likely to attain thegoals, and if not which additional interventions may benecessary in order to achieve them. If regions are identi-fied as requiring fewer resources than anticipated, these

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resources could be moved to areas less likely to attaingoals.

Knowledge gaps and next stepsA number of different model structures need to be com-pared and statistically validated, to assess which is themost suitable structure going forwards. For example, isan SIS model without age structure sufficient to capturethe overall observed dynamics of infection? While indi-viduals are no longer PCR positive, but are still TF posi-tive, is it possible for them to become re-infected at thispoint? High resolution data will also help to disentanglethe relationship and the time spent PCR and TF positive,and help with the explicit modelling of both of thesestages. In addition, more longitudinal data will help toassess trends in transmission over time that have oc-curred as a result of different interventions. Much abouttrachoma remains poorly understood, and will probablyremain unknown as we eradicate the disease. Modelsneed to be validated and calibrated in collaboration withthe International Trachoma Initiative (ITI) to makemore global projections on the feasibility of the 2020goals and where additional resources may or may not beneeded. Yet for any model, an argument can be madethat something, possibly important, should be added toit; validation through prediction can, in large part, re-solve such issues - telling us whether our models areadequate to guide elimination campaigns.

Intensified disease management diseasesA number of neglected tropical diseases are controlledby increased diagnosis and management of cases (inten-sified disease management, IDM). The four IDM dis-eases in this study are Chagas disease, the Gambianform of human African trypanosomiasis, leprosy globallyand visceral leishmaniasis on the Indian sub-continent.Whilst these diseases cause significant morbidity andmortality, the disease courses are quite long, the epi-demic growth rate is slow and the transmission is usu-ally highly focal. They are often associated withdisadvantaged populations and hard-to-reach groups.Given this concentration of disease in populations withpoor access to care, and the potentially long time pe-riods over which their disease course and dynamicsoccur, these diseases have been difficult to study and soquantitative estimates of key parameters are scarce. Inthe model analyses of these diseases the authors haveaimed to provide novel estimates of key parameters andprovide both qualitative and quantitative insights on thedynamics of these infections and their consequences forcontrol.

Chagas diseaseBackgroundChagas disease (etiological agent Trypanosoma cruzi) isthe most important zoonotic vector-borne disease in theAmericas, with an estimated 8 million people infected,ten thousand deaths per year and a disease burden, as

Fig. 5 Schematic of trachoma results. The schematic includes results from: a) a transmission model including consideration of immunity byGambhir et al. [45]; and b) a statistical analysis of the most informative data for forecasting trends in prevalence by Liu et al. [44]

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estimated by Disability-Adjusted Life Years (DALYs), of7.5 times that of malaria [46]. Chagas disease is endemicin Latin America, and has been steadily spreading toother parts of the world including North America,Europe, and Australia [47]. Estimates suggest that over 8million people are infected, but since many cases go un-detected, the actual number of infections may be higher.A study estimated the global annual burden to be $627 ·46 million in healthcare costs and 806,170 DALYs [48].However, since Chagas can result in chronic heart dis-ease after years of being asymptomatic [46, 47], much ofthe costs of Chagas disease occur years into the future.Therefore, currently infected individuals are expected tocost $7 · 19 billion per year and $188 · 80 billion through-out their lifetimes [48]. Transmission mainly occurs viathe triatomine bug [47] (also known as the “kissingbug”), which can acquire the T. cruzi parasite by taking ablood meal from an infected mammal. Transmissionfrom vector to human occurs when a T. cruzi- infectedtriatomine defecates (usually during or immediately aftertaking a blood meal) on an uninfected human, deposit-ing the parasite on the skin. The bitten person often fa-cilitates the parasite entering the bloodstream byrubbing or scratching the bite area and smearing thebug faeces into the bite or other areas with ready accessto the bloodstream such as the eyes or mouth. Less fre-quently, transmission can occur through blood transfu-sion, congenital transmission (from infected mother tofetus), and organ donation [47]. Transmission can alsooccur orally through the ingestion of food contaminatedwith infected triatomine bug faeces and laboratory acci-dents [47]. Currently the main Chagas disease controlmethods are triatomine bug control, protecting foodfrom contamination, and screening blood and organs forT. cruzi. Vector control methods include insecticidespraying, bed nets, and fixing the cracks in buildings(e.g., improved housing). Vaccines and other medicationsare under development [49–51].The 2020 goals call for the interruption or reduction

of transmission across all routes, and an increase in thenumber of patients under treatment. A major challengein achieving these goals is not what to do, but how to doit on a wide enough scale to reach a sufficient propor-tion of those infected or at risk. The two strategies forinterrupting vector-borne T. cruzi transmission arespraying indoor residual insecticides (IRS) and housingimprovements. IRS must be applied on a regular basis toavoid re-infestation, and this has led to insecticide resist-ance in some triatomine species. Housing improvementscan be effective, but they are disruptive and expensive.Thus, a central question is how often and for how longmust these strategies be carried out to eliminate trans-mission, and which factors in the transmission scenarioaffect these efforts?

Modelling approachesThe modelling approach of Peterson et al. [52] was toexamine the effect of synanthropic animals on T. cruzitransmission and prevalence in humans, and how animalpresence affects the efficacy of vector control. Animalsare important to consider because in most Chagas-endemic settings, there are numerous pets, livestock,and vermin that not only serve as food sources for tria-tomine bugs, but are also competent T. cruzi hosts. Thusan important question is whether it is necessary to targetanimals for Chagas control, as the current strategies onlytarget the vector.Peterson et al. focused their efforts on using models to

test hypotheses on human-vector-animal interactions.This qualitative analysis showed that it is likely that ani-mals do amplify transmission to humans in the absenceof any vector control measures, because of their role asadditional food sources for the bugs leads to increases inthe vector population size (Fig. 6). However, if vectorcontrol measures are carried out that prevent the vectorpopulation from growing in the presence of animals,then animals can have a beneficial effect, even withoutreducing the vector population to zero, due to “diluting”the bites of the remaining vectors. This effect is then in-tensified if the animals are only food sources for thebugs and not competent T. cruzi hosts, which is the casefor poultry or any other bird species.

Policy implicationsThese analyses highlight the importance of applying vec-tor control to reduce total vector numbers, rather thantemporarily reducing vector biting on humans. In differ-ent epidemiological settings, the most appropriate vectorcontrol method may be different. In particular, the avail-ability of alternative animal populations for food sourceswhich will enable the triatomine bugs to recover rapidlyfollowing spraying, can undermine the control efforts.These results also highlight the importance of entomo-logical studies in endemic areas to understand the bitingpatterns of the triatomine bugs and how these are af-fected by changing densities of humans and otheranimals.

Knowledge gaps and next stepsA number of substantial knowledge gaps still existregarding the transmission dynamics of Chagas disease,its prevalence and incidence in many countries, thepotential points of intervention, the best ways to diag-nose, monitor, and treat Chagas disease, and the impactand value of different control measures. Modelling ef-forts can help address these important gaps and guidecurrent and future data collection efforts and interven-tion development and testing. An example of a subse-quent modelling effort is to extend an initial model that

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allowed an investigation of how animals impact the forceof infection. The researchers now plan to in parallel de-velop the transmission models and to use other data toinform the models on the force of infection by age andthe basic reproduction number, R0. An important sourceof information on the dynamics of Chagas in differentareas will be age prevalence data from a variety of set-tings. Some of these data are prior to any form of inter-vention, which should allow estimation of the basicreproductive number. The availability of both pre- andpost- intervention serologies will allow estimation of theimpact of control measures and the additional efforts re-quired to break transmission to humans. By estimatingthe force of infection for different regions and munici-palities, researchers can examine the scale of the prob-lem in a truly comparable way across Chagas-endemicareas.

Human African trypanosomiasis, Gambian formBackgroundHuman African trypanosomiasis (HAT) is a parasiticvector-borne disease spread by tsetse (Glossina spp) andis fatal without treatment. There are two distinct forms,Rhodesian and Gambian HAT, with the Gambian formendemic in West and Central Africa and responsible foralmost all (>95 %) HAT cases. Efforts to control the dis-ease have led to a large reduction in the burden of dis-ease, with reported cases falling from around 38,000 in1998 to less than 4000 in 2014 [53]. Consequently, it isnow targeted for elimination as a public health problem,

defined as less than 1 case per 10,000 people per year, in90 % of endemic foci by 2020 [54]. There are two stagesof HAT disease and treatment is stage-specific.Three main methods of intervention can be used in

HAT-endemic areas:

1. Those infected with HAT will usually seek treatmentby self-presentation at medical facilities when symp-toms worsen, although this may not be until stage 2disease.

2. Many endemic areas have active/mass screeningcampaigns to detect and treat both stage 1 and 2cases.

3. Vector control using tsetse targets has been shownto substantially reduce tsetse population sizes [54].However, vector control is not currently used in allendemic areas.

Modelling approachesIn recent analyses, two research groups have independ-ently addressed the feasibility of the WHO goal of elim-ination as a public health problem by 2020 undercurrent strategies using mechanistic mathematicalmodels [55, 56]. Both models used differential equationsto quantify stage 1 and 2 disease in humans, tsetse infec-tion and possible animal reservoirs (Fig. 7). Pandey et al.also capture possible human population-level heterogen-eity in exposure to tsetse bites and participation inscreening.

Fig. 6 Schematic of Chagas results. The schematic describes a new transmission model for Chagas disease used to analyse the consequences ofvarying standard assumptions about the transmission cycle by Peterson et al. [52]

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The model of Pandey et al. was fitted to 2008–13prevalence data from humans, non-human animals andtsetse within the Boffa HAT focus in Guinea, wheremass screening and treatment have been combined withvector control (Fig. 7b) [54]. Annual tsetse control usingtiny tsetse targets is modelled using a function which re-flects a decline in their effectiveness throughout the year.Fitting of the model to the trial data was used to esti-mate key parameters on the underlying level of trans-mission and the impact of vector control measures. Thecalibrated model was used to estimate the achievabilityof the 2020 goal under the scenarios of vector controlalone, or vector control combined with biennial or an-nual screening under 2013 coverage levels. The model’sprojections accounted for the impact of the 2014–5Ebola epidemic on HAT control efforts.In a related approach, Rock et al. used data from

two health zones, Yasa-Bonga and Mosango, inBandundu province of the Democratic Republic ofCongo (DRC), one of the highest incident areas ofGambian HAT (Fig. 7a). Bandundu has screeningcampaigns, but, partly due to its size, has not yet im-plemented a vector control programme. The modelwas fitted to 13 years of case data to estimate theunderlying levels of transmission and the effectivenessof current screening campaigns. The expected time toelimination as a public health problem was predictedfor a range of hypotheses for human heterogeneityunder two levels of active screening: the highest levelachieved (in 2009); and the mean level observedbetween 2000 and 2012.

Policy implicationsEach modelling study included an analysis of the achiev-ability of the 2020 goals in the setting analysed. Pandeyet al. predict that annual implementation of vector con-trol, at the same level attained in 2013, has at least a77 % probability of eliminating HAT as a public healthproblem in Boffa by 2020. If biennial screening or an-nual screening is conducted alongside vector controlthen the probability of elimination by 2020 increases toover 90 %.While there is evidence that active screening and treat-

ment in Yasa-Bonga and Mosango have led to a 52–53 %reduction in new infections over 15 years, Rock et al. pre-dict that the region is unlikely to meet the eliminationgoal until 2059–2091 under the highest level of currentactive detection and treatment. Incorporating human het-erogeneity in the model improves the fit to observed data;the best model fit is obtained when humans who are moreexposed to tsetse bites are assumed to never participate inactive screening. Results suggest that current activescreening campaigns could be further improved by target-ing high-risk individuals and those who have previouslynot taken part in screening.

Knowledge gaps and next stepsNeither of these analyses were able to rule out the possi-bility of an animal reservoir for infection due to the na-ture of the available data. Pandey et al’s analysis suggeststhat vector control is efficacious irrespective of a reser-voir, but in the presence of a reservoir, interventionstrategies must be maintained, even after elimination, to

Fig. 7 Schematic of HAT results. The results include a) quantitative estimates of the level of heterogeneity in human exposure and screeningparticipation by Rock et al. [56]; and b) an assessment of strategies combining both human screening and tsetse control by Pandey et al. [55]

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prevent HAT re-emerging as a public health problem.Future modelling work utilising data on trypanosomeprevalence in animals and tsetse host preference shouldenable better determination of the role of animals in dis-ease transmission.The modelling results highlight the level of geographic

heterogeneity of HAT burden and the variety of inter-vention strategies currently used. Whilst some areas,such as Boffa, are on track to meet the 2020 goal, otherregions may need to strengthen their existing strategieswith complementary measures. In particular, Yasa-Bongaand Mosango are hard-to-reach regions with high inci-dence. Consequently, they are likely to be amongst thehardest areas in which to achieve elimination.Moving forward it will also be important to examine

how spatial heterogeneity in both transmission and in-terventions at a local level may impact disease incidencewithin a larger geographical area. To achieve this it willbe crucial to have good estimates of demography, popu-lation sizes and, ideally, movements at a local level to in-form models which include analyses of the spatialdistribution of cases.

LeprosyBackgroundLeprosy, or Hansen’s disease, is an infectious diseasecaused by the bacterium Mycobacterium leprae. Trans-mission is believed to occur through close contact withan infected person, but the route of transmission re-mains largely undefined, and it would appear that only asmall proportion of people that are exposed will eventu-ally develop the disease [57]. Leprosy is diagnosed basedon clinical signs and treated with multidrug therapy(MDT). Leprosy control rests on early diagnosis andtreatment, which is thought to prevent both transmis-sion and progression to leprosy-related disability.Worldwide, more than 200,000 new leprosy cases are

detected and reported annually from 121 countries [58].This number has been fairly stable in the past 8 years,suggestive of ongoing transmission. Together, India,Brazil and Indonesia account for 81 % of all new cases,and only 13 countries reported more than 1000 newcases in 2014. Recently, WHO has formulated ‘roadmaptargets’ for leprosy [3]. The targets set for the period2015–2020, are: (1) global interruption of transmissionor elimination by 2020, and (2) reduction of grade-2 dis-abilities in newly detected cases to below 1 per millionpopulation at global level by 2020.

Modelling approachesThe three analyses in the collection use distinct modellingand statistical approaches to assess progress of leprosycontrol programmes in different settings. Blok et al. [59]used a stochastic individual-based model SIMCOLEP to

assess the feasibility of achieving global elimination ofleprosy by 2020. SIMCOLEP simulates the life histor-ies of individuals, the natural history of infection withM. leprae, and the transmission of leprosy in a popula-tion structured in households. Leprosy control in-cludes passive detection and treatment. Householdmembers of a detected case can be subjected to con-tact tracing. The model was fitted to the leprosy situ-ation, including control, in India, Brazil and Indonesiaon national and sub-national levels using data fromthe National Leprosy Elimination Program (India),SINAN database (Brazil), and Netherlands LeprosyRelief (Indonesia). Using the fitted model, future pro-jections were made of the leprosy incidence, assumingcontinuation of leprosy control programs.Linear mixed-effects regression models were used by

Brook [60] to investigate the relationship between lep-rosy case detection rate at the district level and severalstate-level regressors: the incidence of tuberculosis, BCGvaccination coverage, the fraction of cases exhibitinggrade 2 disability at diagnosis, the fraction of cases inchildren, and the fraction of cases which were multiba-cillary. Districts reported to have been targeted for en-hanced case finding showed evidence of an increase incase detection. However, substantial unexplained differ-ences between districts were seen (both in terms of newcase detection rate and trend). Moreover, the overall rateof decrease was very small, controlling for the enhancedcase finding.Crump and Medley [61] developed a back-calculation

approach to investigate the infection dynamics of lep-rosy. The model allows for varying effort or effectivenessof diagnosis in different time periods. Publicly availabledata from Thailand were used to demonstrate the resultsthat can be obtained as the incidence of diagnosed casesfalls [62]. Estimates of the incidence of new infectionsand clinical cases were obtained by year, as well as esti-mates of diagnostic efficacy. The method also providesshort-term forecasting of new case detection by diseasetype, including disability status.

Policy implicationsBlok et al. showed that although elimination at nationallevel is predicted by 2020, leprosy will still remain aproblem at sub-national level (Fig. 8a). These high-endemic regions have multi-million populations inwhich rapid progress of leprosy control, even if con-ducted optimally, will not be achieved soon. The au-thors conclude that ongoing transmission of M. lepraewill make global elimination of leprosy unlikely by2020. Further control measures are needed to achievethe goals [59].The analysis of new case detection rates from India by

Brook et al. suggests an endemic disease in very slow

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decline, with heterogeneity across state and districtlevels (Fig. 8b). Active case finding was associated with ahigher case detection rate, but not rapid leprosy control.Finer geographic resolution would improve analysis andbolster evidence-based policy assessment. Objective sur-veys may have a role to play in leprosy program evalu-ation, in view of differences in case reporting and inactive case finding efforts.Crump and Medley found that Bayesian back-

calculation shows great potential to provide estimates ofnumbers of individuals in health/infection states that areas yet undiagnosed (Fig. 8c). This has the potential toprovide valuable information for those managing orevaluating control programmes. The methodology isdriven by available data, and provides an impetus forbetter reporting in that results can be quickly fed backto programs.

Knowledge gaps and next stepsThere is relatively little known about leprosy with anydegree of certainty. The long delay between infectionand disease means that current diagnoses are a poormeasure of current infection. Further modelling workmay help to address this and also highlight areas wheredata collection would be valuable.Blok et al. plan to include grade 2 disabilities and con-

sider intervention programmes targeting contacts of leprosypatients; such as chemoprophylaxis, immunoprophylaxisand an anticipated diagnostic test for sub-clinical cases.Brook et al. plan to use their statistical modelling to informa stochastic model to explore the use of targeted surveysand the effect of sustained active case detection. The back-calculation model of Crump and Medley will be further de-veloped to consider gender and age. The three groups willbe working with national and regional data of variableendemicity.

Visceral LeishmaniasisBackgroundVisceral leishmaniasis (VL) is caused by chronic infec-tion with protozoan Leishmania parasites and is spreadby infected sandflies. Annually, more than 80 % of the200,000–400,000 global cases of symptomatic disease,and an estimated 15,000–30,000 deaths occur on theIndian sub-continent (ISC) [63]. There, VL is caused byLeishmania donovani, is spread by a single sandfly spe-cies, Phlebotomus argentipes, and is considered to besolely anthroponotic. VL, also known as kala-azar (KA),has been targeted by the WHO for elimination as a pub-lic health problem on the ISC, defined as less than 1new case per 10,000 people per year at sub-district level,by 2017. Existing interventions focus on reducing trans-mission, mainly by reducing vector population densitiesthrough indoor residual spraying (IRS) with long-lastinginsecticides (DDT and synthetic pyrethroids) and promptdiagnosis and treatment.Individuals that develop KA, show symptoms of pro-

longed fever, anaemia, weight loss and spleen and liverenlargement, and usually die without treatment. Mostindividuals recover following successful treatment,though a small proportion (2–10 % on the ISC) go on todevelop post-kala-azar dermal leishmaniasis (PKDL), anon-fatal dermatological condition characterised by anodular or papular skin rash. However, the majority ofindividuals infected with the parasite are asymptomatic,but may be infected for many years; it is unclear if indi-viduals ever completely lose infection and how longimmunity lasts for those who develop it.

Modelling approachesTo address the question of whether the 2017 VL elimin-ation target can be met with current interventions, it is ne-cessary to obtain robust estimates of key epidemiological

Fig. 8 Schematic of leprosy results. The results include: a) a transmission model fitted to national and regional data from India, Brazil and Indonesia topredict future trends in leprosy incidence by Blok et al. [59]; b) statistical modelling of regional case detection data from India by Brook et al. [60]; andc) a back-calculation method to investigate underlying infection dynamics and predict future incidence by Crump and Medley [61]

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parameters and to assess how uncertainties in transmissionaffect the efficacy of different interventions. These issueshave been tackled in separate analyses by two researchteams [64, 65].Chapman et al. [65] used statistical analyses to assess

the risk of progression to KA based on serology test re-sults, and a probabilistic model to estimate key parame-ters in the natural history of VL. Their model is fitted todata from a detailed epidemiological study conducted inthree highly endemic villages in Bangladesh between2002 and 2004, at which time no control interventionsother than antimonial treatment and untreated bed netuse were in place in the region. By fitting to the annualserology (rK39 antibody and leishmanin skin test) testresults and KA onset and treatment dates from thestudy, the researchers estimate the duration of asymp-tomatic infection, the duration of immunity and theproportion of asymptomatic individuals that progressto KA.Le Rutte et al. [66] describe the quantification of VL

transmission between humans and sandflies on the ISCwith 3 deterministic age-structured models. The princi-pal source of infection to sandflies remains unknown,and Le Rutte et al. test three hypotheses for the sourcein their models - namely (1) asymptomatic infections,(2) re-activation of infection after recovery from initialinfection, or (3) PKDL. All 3 models are parameterisedwith age-structured data from the KalaNet study, whichconsists of annual prevalence of infection (PCR), detect-able immune responses (DAT) and incidence of VL inhighly endemic clusters in India and Nepal as well as thepercentage prevalence of infected sandflies in Nepal.The inclusion of age-structure in the models allows fordetailed fitting and age-related heterogeneity in sandfly

exposure. With these models they predict the impact ofcurrent interventions on VL incidence to estimate thefeasibility of achieving the 2017 elimination target forthe ISC. Predictions are made for three levels of VL en-demicity and for optimal and sub-optimal IRS effective-ness, which may vary due to quality of implementationand vector resistance to DDT.

Policy implicationsThe statistical analyses by Chapman et al. show that in-dividuals who initially have high antibody levels aremore likely to progress to KA than individuals with lowor moderate antibody levels, and that those who sero-convert to high antibody levels have an even higherchance of developing KA (Fig. 9a). These findings sug-gest that individuals at high risk of progressing could beidentified by screening, so that their infectious periodand onward transmission could be reduced with im-proved access to treatment and targeted IRS. The fittingof the probabilistic model to the data gave estimates of147 days (95 % CI 130–166 days) for the average dur-ation of asymptomatic infection and 14.7 % (95 % CI12.6–20.0 %) for the proportion of asymptomatic indi-viduals progressing to KA - much longer and higher es-timates than those reported previously [66], suggestingthat asymptomatic individuals may contribute signifi-cantly to transmission.The models of Le Rutte et al. show that the predicted

impact of IRS differs per model variant, depending onwhether asymptomatics, re-activated infections or PKDLcases constitute the main reservoir of infection (Fig. 9b).Further, the feasibility of achieving elimination of VL onthe ISC strongly depends on pre-IRS endemicity and theeffectiveness of IRS itself. Based on the assumption that

Fig. 9 Schematic of VL results. The results include: a) new estimates of epidemiological parameters by Chapman et al. [64]; and b). a qualitativeinvestigation of the impact of different life history assumptions on transmission dynamics and intervention efficacy by Le Rutte et al. [65]

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cases of asymptomatic infection are the main reservoir(due to high numbers, and despite low infectivity to-wards the sandfly) and IRS is implemented optimally,the authors predict that VL may be eliminated in lowand moderately endemic areas within six years of thestart of IRS. For highly endemic areas and areas withsub-optimal IRS, additional interventions may berequired.

Knowledge gaps and next stepsThe relative infectivity of individuals in different dis-ease stages is currently not known and thus neither istheir contribution to transmission. Ongoing xenodiag-nostic studies and additional longitudinal data on theprevalence of infection in sandflies during interventionswill inform the transmission models regarding the mostlikely reservoir of infection, and enable the implemen-tation of an appropriate model structure in anindividual-based model by Le Rutte et al. In regionswhere it is predicted that the target of <1 VL case per10,000 capita will not be reached, additional interven-tions may be required; the impact of these (such as apotential vaccine) will be explored by Le Rutte et al.To aid estimation of the relative contributions of dif-

ferent disease groups to transmission, spatial and tem-poral variation in VL risk will be included in theprobabilistic model of Chapman et al. Fitting this modelto other longitudinal datasets will provide more robustestimates of the different disease stage durations andproportion of individuals progressing to disease, and anindication of the extent to which these parameters de-pend on endemicity and other risk factors. This workwill be used to inform the development of future trans-mission models of VL for assessing the efficacy of differ-ent interventions.

DiscussionThe publications in this collection bring together a var-iety of different approaches to provide novel quantitativeanalyses that can inform policy development on the con-trol and elimination of nine NTDs. For the PCT diseasesexisting and novel models have been brought together toassess the impact of current strategies, identify areaswhere they need to be adjusted and provide consensusinsights on likely coverage needs and program duration(Table 2). For the IDM diseases, new models andmethods have been developed and key parameters (suchas the incubation period or proportion of infectionsaccessing care) have been estimated (Table 3). In bothareas, these are important steps forward. These analysesalso identify the need for further work, as well as morerigorous model comparison and testing against more ex-tensive datasets. Across the diseases, there are a numberof common themes that emerge:

The importance of epidemiological settingsAs expected, the details of an epidemiological setting, interms of baseline prevalence, heterogeneities in risk byage and across the population and in terms of programimplementation, are crucial in determining program suc-cess. The analyses of the PCT helminthiases in particularhighlight that, in areas with different transmission rates,even with the same helminth (and vector), very differentcombinations of interventions are required to achievecontrol or elimination. As these models are developedfurther and linked more closely with programmatic ac-tivities, there are opportunities to better develop inter-ventions aligned to local conditions.The importance of epidemiological setting means that

because these diseases are spatially heterogeneous, sam-pling for the impact of control is non-trivial, and low re-gional levels of infection may not be indicative of lowtransmission across an area (as illustrated by sub-national data for leprosy). A spatially heterogeneoustransmission landscape (as is the case for NTDs) com-bined with some level of inevitable heterogeneity in howinterventions are delivered and received is likely to leadto further heterogeneities in the levels of transmissionfollowing years of interventions. This may result in ‘hot-spots’ where additional interventions are required,. Al-though it may be difficult to identify or predict all hotspots, the modelling can demonstrate how the presenceof hot spots contributes to heterogeneity and the needto adapt responses when such a location is detected.

Heterogeneities in risk and heterogeneities in access to careA number of the analyses in this collection includemodels of both heterogeneities in risk of exposure and,importantly, access to care. Heterogeneities in transmis-sion risk are more easily identified for helminth infec-tions due to heterogeneities in pathogen load. Forvector-borne infections there is also the possibility ofmeasuring heterogeneities in exposure to insect bites. Asdemonstrated for helminth infections, two settings withsimilar prevalence but with very different levels of het-erogeneity in risk may require quite different levels ofinterventions. In addition to these biological variations,particular behaviours can increase risk, whether it ischildren having higher exposure to STH, or adult malespossibly having higher exposure to HAT. These will leadto differential impact of the available interventions.These analyses have also highlighted that where high-

risk groups are additionally less able to access care, orwhere there are other semi (or fully) systematic biases inaccess to interventions, this can have a large impact onthe success of a programme. When the coverage rate isassumed to randomly reach any person with equalchance, the interpretation can conceal the fraction of apopulation that systematically misses the intervention.

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Table 2 Summary of modelling techniques used, PCT diseases

Paper Model Fitted to datafrom:

Predictionstested?

Technical advances Model accounts for Next steps

Vector/environmentdynamics

Heterogeneity in risk Access to interventions

Lymphatic filariasis

Irvine et al. Stochasticindividual

Kenya and SriLanka

Yes Heterogeneity in transmissionand extinction dynamicsgreatly affects time toelimination

Deterministicvector dynamics.Single pool ofvectors

Gamma distributedrisk in exposure

Spectrum of access torepeat rounds of MDAand vector control, withcross-correlations withrisk

Fit the model tointervention data andunderstand transmissiondynamics at lowdensities

Jambulingam etal.

Stochasticindividual

35 villages inIndia.

Yes Association betweenantigenaemia and presence ofadult worms.

Deterministicvector dynamics.single pool ofvectors

Age dependent,gamma distributedexposure

Individual’s treatmentcompliance is semi-systematic

Estimating vectorinfection thresholds.Estimating probability ofelimination

Singh et al. Deterministic Data from 22villages fromAfrica, SouthEast Asia, andPapua NewGuine

No Bayesian fitting includinginformation about modelinputs and outputs

Deterministicvector dynamics.single pool ofvectors

Negative binomialdistribution ofworms

Random Estimating thresholds fortrue elimination. Furtherunderstanding ofparameter uncertainty

Onchocerciasis

Stolket al.

ONCHOSIM Stochastic Cameroon Yes Bringing the two modelstogether and understandingdifferences in predictions

Deterministicvector dynamics.Single pool ofvectors

Age dependent,gamma distributedrisk

Age-dependentprobability of receivingtreatment. Lifelongcompliance factor

The two models givedifferent Mf intensitiesand prevalences afterMDA, which needs tobe investigated further

EPIONCHO Deterministic Cameroon Yes Single vectorcompartment

Age- and sex-specific exposure toblackfly

Compliant and non-compliant groups

Schistosomiasis

Anderson et al. Deterministic Kenya Yes Using an age-structured modelto assess the feasibility of elim-ination, and comparing modelpredictions to reinfection data

Environmentalreservoir, constantdecay rate. Noexplicitconsideration ofsnail dynamics

Variability inexposure as afunction of age.Negative binomialdistribution ofworms

Treatment reducedworms by a givenfraction in a givenproportion of individuals,equivalent to randomtreatment

Better modelling oftransmission by age,immunity, worm mating.Stochastic model

Gurarie et al. Deterministic Kenya Yes Investigating MDA success indifferent scenarios using amodelling framework

Snail transmissioncompartments

None A fraction of adult wormsare killed by eachtreatment

Consideration of snaildynamics.

Soil-transmitted helminthiasis

Coffeng et al.(hookworm only)

Stochastic,,individual

Vietnam Yes Developed WORMSIM, a newgeneralised framework formodelling transmission andcontrol of helminths

Environmentalreservoir

Gamma distributedtotal egg output,two scenarios: highor low variation inhost susceptibility

Participation is eitherrandom, fully systematicor a mix.

Lifespan of eggs in theenvironment, MDAcoverage over differentage groups

Truscott et al. Deterministic

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Table 2 Summary of modelling techniques used, PCT diseases (Continued)

India (Ascaris),St Lucia(Trichuris) andZimbabwe(hookworm)

Yes (Ascarisonly)

Fitting against multipletreatment rounds data

Pool ofenvironmentalinfective material,exponential decay

Negative binomialdistribution ofworms in individuals

All individuals have aprobability of receivingtreatment

Understanding spatialand age heterogeneity,systematic non-compliance

Trachoma

Gambhir et al. Deterministic Tanzania andGambia

No Including MDA interventionsinto the modelling framework

None None A subset of the infectedgroup are moved to thesusceptible compartment

Validating againstmultiple datasets, bettermodelling of immunity

Liu et al. Stochasticcompartmental

Niger No Constructing a stochastictransmission model includingdifferent ways of modellingeach observation by fitting toTF only or to TF, TI and PCR

None None All individuals have aprobability of receivingtreatment

Further fitting tointervention data.

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Table 3 Summary of modelling techniques used, IDM diseases

Paper Model Fitted todata from:

Predictionstested?

Technical advances Model accounts for Next steps

Vector/environmentdynamics

Heterogeneity in risk Access to interventions

Chagas disease

Petersonet al.

Deterministic Parametervalueswere setaccordingto theliterature

No Formulating a transmissionmodel and analysing theconsequences of varyingstandard assumptions on thetransmission cycle

Deterministicvector dynamicswith animal hostsin some modellingscenarios

None Not applicable - vector control only Develop twoindependenttransmission models.Estimation ofchanges intransmission rates

Human African trypanosomiasis, Gambian form

Pandey etal.

Deterministic Boffa,Guinea

Yes Data cannot identify whetherthere is an animal reservoir. Butin the presence of animalreservoir, there is high risk of re-emergence of HAT as publichealth problem.

Includes tsetse andanimalcompartments

None All individuals have a probability ofreceiving treatment

Evaluating 2020 goalin other foci andimpact ofheterogeneity inhuman exposure totsetse.

Rock et al. Deterministic Bandundu,DRC

No Data supports the existence ofan unscreened, high-risk popu-lation, but cannot identifywhether there is an animalreservoir

Includes tsetse andanimalcompartments

High risk and low riskhuman compartments

Randomly participating and non-participating human compartments

Projecting impact ofvector control in DRC

Leprosy

Blok et al. Stochasticindividual

India, BrazilandIndonesia

Yes Applied SIMCOLEP to predictfuture leprosy incidence inIndia, Brazil and Indonesia

Not applicable Susceptibility: 20 % ofpopulation is susceptible;Type of leprosy: MB vs PB;Contact structure: generalpopulation vs withinhouseholds

All individuals that have beendiagnosed with leprosy receive MDTtreatment. Probability of beingdiagnosed is determined by passivecase detection delays and possibleactive case finding activities.

Assess whichadditionalinterventions areneeded to meet thegoals

Brook etal.

Statistical 604analyticdistricts inIndia

No Enhanced active case findingwas associated with a highercase detection rate

Not applicable Not applicable Not applicable Developindependentstochasticcompartmentaltransmission model

Crump &Medley

Statistical Thailand Yes Back-calculation can estimatethe number of undiagnosedcases from diagnosed incidencerates

Not applicable Not applicable Not applicable Consideration ofgender and age.Analysis of othercountries.

Visceral leishmaniasis in the Indian sub-continent

Chapmanet al.

Statistical Bangladesh No Estimating durations ofasymptomatic and symptomaticinfection

Not applicable Proportional hazardsmodel for different riskfactors including age, sexand bed net use

Not applicable Developing atransmission model.

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Table 3 Summary of modelling techniques used, IDM diseases (Continued)

Le Rutteet al.

Deterministic India andNepal(KalaNet)

Yes Developed three modelstructures, each with a differentreservoir of infection, all fittingthe data.

Vector population,deterministic.

Age-dependent sandflyexposure.

All individuals have a probability ofreceiving diagnosis, treatment, andvector control (IRS).

Implement bestmodel structure instochastic individualbased model. Exploreeffect of additionalinterventions.Added heterogeneity in sandfly

exposure.

Applied models to predictfuture VL incidence withcurrent interventions.

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Models that include systematic factors in coverage areuseful for relating to the practical realities of implemen-tation, and thus help inspire operational improvementsthat reach the specific subpopulations previously at highrisk for infection.Modellers can characterise these heterogeneities in

some settings, but of course not for all settings at alltimes. Given limited data, the modellers have been ableto estimate some of the parameters that govern this vari-ation in these settings, and have presented the sensitivityof their results to these underlying parameters.

Challenges of elimination as a public health problem versus“true” eliminationThe first formal definitions of the public health targetsfor infectious disease were defined at a multi-disciplinary conference [67]. Since then the definitionshave become somewhat corrupted: what is now com-monly termed as “elimination” or “elimination as a pub-lic health problem” is more formally defined as control:“Reduction of disease incidence, prevalence, morbidityor mortality to a locally acceptable level as a result of de-liberate efforts. Continued intervention measures are re-quired to maintain the reduction”. The reason for theslippage in terminology is, as recognised at the conference,that political motivation to achieve elimination has to bedeveloped and maintained. The current situation is poten-tially dangerous: most of the targeted NTDs are ap-proaching “elimination”, but the models indicate thatcontinued intervention is required to remain at the levelsreached. The experience with leprosy indicates [68] that ifachieving “elimination” results in a reduction in controlefforts, at best progress is stalled and at worst disease willrebound. We need now to consider redefining the targetsto be closer to true elimination: “Reduction to zero of theincidence of infection caused by a specified agent in a de-fined geographical area as a result of deliberate efforts.Continued measures to prevent re-establishment of trans-mission are required.” Modelling can help define thesenew targets.

Next stepsTesting model predictions and model comparisonOne of the strengths of this research project is the scien-tific robustness that comes from having independentmodelling groups using different methods to address thesame problems and the opportunities for testing predic-tions from multiple models. This has been most notablyfor HIV and malaria [69, 70] and there are lessons to belearned from the successes of these projects. For NTDsthere has been some, limited, testing of model pre-dictions against epidemiologic or programmatic data(Tables 2 and 3). This needs to be extended quiteconsiderably in the next phase of this research

project. By providing data from initial time pointsand asking the modellers to predict later timepoints, we will gain a better understanding of howthe data informs parameter estimation and of par-ticular weaknesses or strengths in the models. Thiswill improve confidence in the model outputs.Given the independent approaches within this research

project and in the wider NTD modelling community, itis necessary to bring these results together and provideconsensus information, whether through informal sum-maries (presented here), or through more rigorousmethods. Possible approaches to arriving at consensusanswers to the consortium’s research questions include:

1) analysis of the individual model projections,discussion on the differences and the possible causesof those differences and agreement on the mostlikely projection through discussion: Modelcomparison

2) arriving at a consensus model, through discussionon the strengths and weaknesses of each group’sapproach for given geographical locales. This modelwill then be refitted to the baseline data andprojected forward: Consensus Model building

3) mathematically combining the forecasts of eachmodel through e.g., averaging. The cone ofuncertainty for the forecasts is delineated by theupper and lower forecasts of each group. This is theapproach of the international panel on climatechange’s (IPCC) global surface temperatureprojections: Ensemble Forecasting

Each of these approaches has positives and negatives,which require further discussion. The joint onchocercia-sis paper in this collection has brought together twomodelling approaches which have been used for manyyears, and is gradually developing an understanding ofwhat particular aspects of these models have generateddifferent estimates of the number of rounds of MDA re-quired to achieve particular targets [71]. This is aprocess of investigation, and through future model test-ing against multiple-timepoint programmatic data, a fur-ther quantitative assessment of the appropriate sets ofassumptions and parameter sets can be made.The development of a consensus model may be seen

as a desirable aim from some stakeholders who wouldlike a single answer to policy questions for very sound,practical reasons. However, built into this project is therecognition of the fact that different model assumptionsand choices on how they are implemented can give dif-ferent results and by using these different approaches weimprove the scientific robustness of our conclusions. In-deed, arguably, for the diseases for which there has beenvery little previous modelling, independent analysis of

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the very few datasets which are available has led to agreater range of model assumptions than joint workingwould have generated, which builds more scientificrobustness.Ensemble forecasting, bringing together different

models and weighting their output, is the current state-of-the-art in climate forecasting, and has been done tosome extent in epidemiological modelling, but theweighting of the different models is challenging.In the short term we hope to progress in our under-

standing of the different outputs of these modelsthrough carefully managed model comparison in orderto provide consensus guidance on the key policyquestions.

DataAs with all epidemiological modelling, there is a needfor the models to be informed by high quality clinicaland epidemiological data. The research and implementa-tion community has been very supportive of this workso far, and there will be a greater number of re-analysesof old data, as well as analyses of new data, in the future.Part of our role is to improve access to these data forother modellers both now and in the future. We are cur-rently collating a catalogue of the data that is used inour studies, and aim to facilitate access to these data forother modelling groups. It is important to rememberthat there are limited datasets currently available formodelling NTDs, and we should not be complacent thatif we have modelled the few datasets available that wehave a full understanding of the dynamics of these dis-eases. In particular, the models are very poor at replicat-ing the behaviour of systems at low prevalence due tothe high variability in potential outcomes. This will be aparticular challenge for the future.Model-informed data collection is a desirable outcome

of this work, as it will broaden our understanding of theepidemiology [72, 73] and improve control. Some groupsare actively seeking out such studies or are involved inthe design of studies with these goals in mind, such asthe Tumikia study in Kenya [74], which investigates thepossibility of interrupting STH transmission thoughMDA. There are a number of similar activities acrossthe nine NTDs.The interpretation of raw data is sometimes hampered

by issues with current diagnostic techniques. For ex-ample, models of helminth transmission are usuallybased around representations of worm numbers withinhosts, but the connection between worm burdens andthe output of egg-counting diagnostic techniques, suchas Kato-Katz, or microfilarial counts are not well charac-terised, although it is known that sensitivities can bequite low. Newer diagnostics may provide more sensitivemethods, but the quantification of load may be lost. It is

therefore essential that the models are informed by theindividual-level data on the relationship between differ-ent diagnostics, as well as tested against population-levelintervention data using these diagnostics, not only todata using older methods. Any clinical or field trial of adiagnostic is an opportunity to work with the study de-signers to ensure that key variables are collected measur-ing model parameters linking the detection characteristicsto immunology and with multiple diagnostic methods.The additional study data may come at no added cost oradditional funds may be required for collaboration on abroadened scope. Timing is critical as many of the NTDsdrop in incidence and research focus may shift elsewhere.At the same time data are more critical to providing a use-ful degree of certainty in the projections of low transmis-sion levels.For the IDM diseases, diagnostics are often poor at

identifying active infection, and interpreting case datarequires an understanding of the underlyling ‘effort’ indetecting cases. For these diseases it is important thatanalyses of such data are informed through close discus-sions with those who collected or collated the data. Thequantification of underlying trends in incidence fromcase data requires a good understanding of the incuba-tion period and the likely pathway from onset of illnessto care, and how this varies by setting an by, for ex-ample, age, sex and socio-economic setting. It may bethat this will never be quantifiable, and therefore inde-pendent measures of exposure, such as serological sur-veys, will be needed to assess program success and,importantly, evaluate local elimination.

Practical utility of models for research and public healthcommunityFor many of the papers we have released the codeunderlying the models. The remaining groups have alsocommitted to releasing their code within the nextmonths. The aim is to release the models in a formatthat expert epidemiological modellers can use now andin the future. This is to ensure that the work presentedhere is repeatable science, and that others can build onthe work initiated here.There is an admirable increasing trend for epidemio-

logical model code to be realised and this generates someinteresting points of discussion. Many of the models havebeen built for the analyses published in the collection andare subject to continuing development. They are alreadybeing altered to incorporate new intervention tools as theyemerge such as the triple drug for lymphatic filariasis andoral stage-independent drugs for HAT, in order to simu-late possible impact before they are rolled out.Publishing the model code increases our collective re-

sponsibility to foster the acquisition of technical skillsfor anyone seeking to learn to use them [75]. The

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configuration of the models and the preparation of inputdata require knowledge of internal model structure anda large amount of statistical data processing if the modelis to be adapted to any specific setting. Simply makingeducational resources known can efficiently guide newmodel users to the appropriate classes, lectures, litera-ture, etc. We hope that the release of these models willstimulate opportunities for more collaborations andknowledge sharing, particularly with researchers in en-demic countries. The value of the time invested in theformal and informal collaborations that will arise fromthem must be regarded as precious.Of course, any model can be inadvertently misused giv-

ing misleading outputs and, as they have been released inits current form they need expert use. The original devel-opers of the models currently lack the capacity for tech-nical support ordinarily provided by a commercialsoftware company, and the code should not be viewed asbeing produced for that level of use. The question still re-mains whether these models should be made available forlocal policy decision by development of more user-friendly interfaces, and also whether modelling expertise isrequired at that level [75]. For the moment, most of thesemodels are not yet sufficiently validated to provide thatlocal level of precise policy development, but through in-creased model testing and comparison that may becomepossible in future, provided they are sufficiently informedby, and tested against, the right data.

ConclusionThis collection of research papers represents an importantstep forward for the evidence base for control and elimin-ation of NTDs. They highlight settings where the 2020goals, and even true elimination, are likely to be achievedusing the current strategies. They also indicate that thereare likely to be additional combinations of interventions re-quired in other settings. These results do not provide theevidence for dramatic changes in policy, but can guidethinking and provide indications of ways forward whichcan be tested in future studies and analyses. The overarch-ing messages of the models are highlight the importance of

� heterogeneity in risk of infection (and reinfection)and identifying which groups may maintaintransmission as overall levels are reduced.

� heterogeneity in access to and acceptability ofinterventions, and possible systematic or semi-systematic patterns in any lack of coverage.

� considering transmission rates when consideringstrategies and endpoints

� clarity on the end goal of these programs and thedevelopment of strategies to maintain the gainsachieved through elimination as a public healthproblem.

Through continuing collaboration across this team ofmodellers and their partners these researchers aim toprovide further quantitative analyses which will assistthe global effort to reduce the burden of NTDs towardsthe 2020 goals and beyond.

AbbreviationsALB: Albendazole; APOC: African programme for onchocerciasis control;DALY: Disability-adjusted life years; DEC: Diethylcarbamazine;DRC: Democratic Republic of Congo; GPELF: Global programme to eliminatelymphatic filariasis; HAT: Human African trypanosomiasis; IDM: Intensifieddisease management; IRS: Indoor residual spraying; ISC: Indian subcontinent;IVM: Ivermectin; KA: Kala-azar; LF: Lymphatic filariasis; LLIN: Long-lastinginsecticidal nets; MDA: Mass drug administration; MDT: Multidrug therapy;NTDs: Neglected tropical diseases; OCP: Ochocerciasis control programme inWest Africa; OEPA: Onchocerciasis elimination program for the Americas;PCT: Preventive chemotherapy diseases; PKDL: Post-kala-azar dermalleishmaniasis; PRET: Partnership for the rapid elimination of trachoma;SAC: School-aged children; SIS: Susceptible-infected-susceptible model;SWB: Stratified worm burden model; STH: Soil-transmitted helminths;TF: Trachomatous inflammation-follicular; TT: Trachomatous trichiasis;VC: Vector control; VL: Visceral leishmaniasis; WASH: Water, hygiene andsanitation; WCBA: Women of childbearing age; WHA: World health assembly;WHO: World Health Organization.

Competing interestsRoy M. Anderson is a Non-Executive Director of GlaxoSmithKline (GSK). Glax-oSmithKline played no role in study design, data collection and analysis, de-cision to publish, or preparation of the manuscript.

Authors’ contributionsTDH conceived the publication, all authors contributed text and read thefinal manuscript. TDH, LACC, REC, LD, HF, MAI, SJ, KSR designed the structureand formatting for the content. Major contributors by disease sectionlymphatic filariasis: TDH, SJDV, MAI, LKH, EM, LR, BKS, WS, SS; onchocerciasis:MGB, SJDV, WS, MW; schistosomiasis: RMA, DG, CK, JT, AZ; soil-transmittedhelminthiasis: RMA, LC, SJDV, JT, AZ; trachoma MG, TL, AP; chagas: SB, AD,APG, BYL, MNM, JKP; human African trypanosomiasis: KA, APG, MJK, MNM,AP, KSR, SJT, JT; leprosy: DJB, REC, SJDV, APG, TML, GFM, MNM, TCP, JHR; vis-ceral leishmaniasis: TDH, ERA, LACC, LC, OC, SJDV, LD, SJ, EALR, GFM. The il-lustrations were designed by KSR in consultation with all authors. All authorsread and approved the final manuscript.

AcknowledgementsMembers of the NTD Modelling Consortium 2020 goals team: MosesBockarie, Fengchen Liu, Lee Worden. The authors would also like to thankSerap Aksoy, Caryn Bern, Simon Brooker, Don Bundy, Aya Caldwell, KatieGass, Angi Harris, Julie Jacobson, P Jambulingam, Randee Kastner, PatrickLammie, Aryc Mosher, Rachel Pullan and Will Starbuck for helpful discussionsand support in this research. The authors gratefully acknowledge funding ofthe NTD Modelling Consortium by the Bill and Melinda Gates Foundation inpartnership with the Task Force for Global Health, by Novartis Foundationand by Children’s Investment Fund Foundation (UK) (“CIFF”). The views,opinions, assumptions or any other information set out in this article aresolely those of the authors.

Author details1University of Warwick, Coventry CV4 7AL, UK. 2Liverpool School of TropicalMedicine, Liverpool L3 5QA, UK. 3Imperial College London, London W2 1PG,UK. 4London School of Hygiene and Tropical Medicine, London WC1E 7HT,UK. 5Johns Hopkins Bloomberg School of Public Health, Baltimore, MD21205, USA. 6Bill and Melinda Gates Foundation, Seattle, USA. 7ErasmusUniversity Medical Center, 3015 CE Rotterdam, Netherlands. 8PrincetonUniversity, New Jersey, NJ 08544, USA. 9Yale University, New Haven, CT06520, USA. 10Monash University, Melbourne, VIC 3800, Australia. 11CaseWestern Reserve University, Cleveland, OH 44106, USA. 12University ofCalifornia, San Francisco, San Francisco, CA 94143, USA. 13University of NotreDame, South Bend, IN 47556, USA. 14Vector Control Research Centre,Pondicherry 605006, India. 15Children’s Investment Fund Foundation, LondonW1S 2FT, UK.

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Received: 25 November 2015 Accepted: 1 December 2015

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