237

Tropical Biomedicine 28(2): 237–248 (2011)

Aedes larval population dynamics and risk for dengue

epidemics in Malaysia

Rohani, A.1*, Suzilah, I.2, Malinda, M.1, Anuar, I.3, Mohd Mazlan, I.4, Salmah Maszaitun, M.3, Topek, O.3,Tanrang, Y.5, Ooi, S.C.5, Rozilawati, H.1 and Lee, H.L.11 Medical Entomology Unit, Institute for Medical Research, Jalan Pahang, 50588Kuala Lumpur2 Department of Statistics, Faculty of Quantitative Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah3 Jabatan Kesihatan Negeri Kedah, Jalan Sultanah, 05250 Alor Setar, Kedah4 Jabatan Kesihatan Negeri Johor, Wisma Persekutuan, Jalan Air Molek, 80590 Johor Bahru, Johor5 Jabatan Kesihatan Negeri Pahang, Tingkat 12, Wisma Persekutuan, Jalan Gambut, 25000 Kuantan, Pahang* Corresponding author email: rohania@imr.gov.myReceived 6 June 2010; received in revised form 2 December 2010; accepted 6 January 2011

Abstract. Early detection of a dengue outbreak is an important first step towards implementingeffective dengue interventions resulting in reduced mortality and morbidity. A denguemathematical model would be useful for the prediction of an outbreak and evaluation ofcontrol measures. However, such a model must be carefully parameterized and validatedwith epidemiological, ecological and entomological data. A field study was conducted tocollect and analyse various parameters to model dengue transmission and outbreak. Dengue-prone areas in Kuala Lumpur, Pahang, Kedah and Johor were chosen for this study. Ovitrapswere placed outdoor and used to determine the effects of meteorological parameters onvector breeding. Vector population in each area was monitored weekly for 87 weeks. Weatherstations, consisting of a temperature and relative humidity data logger and an automated raingauge, were installed at key locations in each study site. Correlation and AutoregressiveDistributed Lag (ADL) model were used to study the relationship among the variables. Previousweek rainfall plays a significant role in increasing the mosquito population, followed bymaximum humidity and temperature. The secondary data of rainfall, temperature and humidityprovided by the meteorological department showed an insignificant relationship with themosquito population compared to the primary data recorded by the researchers. A well fitmodel was obtained for each locality to be used as a predictive model to foretell possibleoutbreak.

INTRODUCTION

Dengue remains a serious threat for humanhealth in Southeast Asia. As an effectivedengue vaccine and anti-viral treatment arenot currently available, dengue control relieson controlling the principle vectors of thedisease, the mosquitoes Aedes aegypti andAedes albopictus. The increase of dengueworldwide is attributed to four major factors,according to the US Centers for DiseaseControl. They are: uncontrolled urbanisationand concurrent population growth resultingin poor sanitation conducive for increase ofAedes population; passive surveillance of

cases and reliance on “emergency” controlmeasures; increased air travel and traffic invirus exchange; and poor and ineffectivemosquito control approaches.

Malaysia has a good laboratory-basedsurveillance system for dengue. However,it is basically a passive system and haslittle predictive capability. Problem mayoccur if one waits for laboratory confirmationof the case before notification. Delay innotification may lead to delay in controlmeasure, which will further lead tooccurrence of outbreak, since dengueneeds optimum time management as thetransformation of dengue into severe form of

238

dengue will only take a very short period(WHO, 1985).

At present, conventional vector controlis usually conducted after the occurrence ofdengue cases, not before. Once dengue virusis introduced into a human population throughthe vectors, it is often too late to kill theinfected mosquitoes. Thus fogging after orduring the outbreak has little impact on thespread of the disease. This is aggravated bythe fact that by the time an outbreak isreported, 7-10 days would have passed beforecontrol is instituted.

Furthermore, present controlconcentrates more on adulticiding, lesser onlarviciding. As has been shown in manycountries, the larvae are actually the naturalreservoir of the dengue virus during inter-epidemic seasons and the occurrence oftransovarial transmission of dengue virus byAe. aegypti and Ae. albopictus in nature wasreported in Malaysia (Rohani et al., 1997,2007). Therefore, without actively destroyingthe larvae, the dengue virus can always finda safe haven in them. When these infectedlarvae multiply in large number underfavourable conditions, an outbreak willprecipitate.

Studies on the efficacy of sourcereduction strategy simultaneously at allrelevant levels, i.e. vector density, virustransmission dynamics, dengue infection andcost-effectiveness have not been carried out.The efficacy of a source reduction strategywill also depend on various cultural andecological factors, such as water storagepractices, rainfall patterns, temperature,population density and migration. As all thesefactors are known confounders for the dengueoutbreaks, a model employing all fourparameters of dengue transmission, namely,vector, human cases, vector infection rate andecological factor will probably be the mostaccurate in determining outbreak thresholdso that outbreak can be predicted as early aspossible.

It is against these backgrounds that newmethod, novel ideas and concept are urgentlyneeded to revamp the whole dengue controlstrategies and to check the spread of denguewhich apparently at present is unstoppable.Early detection of a dengue outbreak is an

important first step towards implementingeffective dengue interventions and reducingmortality and morbidity in humanpopulations. A dengue mathematical modelwould be useful for the evaluation of controlmeasure, to be used in decision-making.However, a mathematical model must becarefully parameterized and validated withepidemiological, ecological and entomo-logical data. A closer interaction is neededbetween mathematician, epidemiologistand entomologist in order to find betterapproaches to the measuring and inter-pretation of the dengue transmissions.Parameter estimation in the field work,localized and comprehensive analysis byusing site-specific data will greatly aid themodelling of this disease, making it morerealistic and useful.

In this study, a new model is suggestedthat emphasizes factors contributing todengue outbreak in Malaysia byconcentrating on three major aspects -entomological, epidemiological andenvironmental. This is the first attempt tomodel dengue outbreak by including thesethree major aspects and using time series,econometric automated models andjudgments approach. The outcome will leadto a better early warning system for dengueoutbreak.

MATERIALS AND METHODS

Entomological data collection

This study was conducted by simulating thefield conditions. Dengue prone areas (fiveconsecutive years of high dengue cases) inKuala Lumpur, Pahang, Kedah, and Johorwere chosen for this study. A total of 50(Taman Sejahtera, Kulim, Kedah); 40 (DesaPandan, Kuala Lumpur); 30 (Indera Mahkota2, Pahang) and 26 (Taman Perumahan Uda,Johor) ovitraps were set at the study sites.Mosquito population was estimated basedon number of larvae collected using ovitrapwhich was located outside occupied houses.Mosquito larvae collected from the ovitrapswere identified using standard taxonomickeys. Identified mosquito larvae weresegregated according to species, site, and

239

date. Vector population in each area wasmonitored weekly for 87 weeks.

A pilot study was conducted for threeweeks to determine the sample size (numberof ovitraps per locality) for each study area.During the pilot study, 20 ovitraps werelocated randomly outside occupied housesand number of larvae collected was usedto estimate the number of ovitrap neededfor the study for each locality. A trainingregarding methodology of the project suchas setting up ovitraps, mosquito collection,identification and preservation, collectingrainfall, temperature and humidity data andrelevant explanation about the project werealso conducted. The actual data collectionstarted in October 2007 until June 2009.

Meteorological data collection

Weather stations, consisting of a temperatureand relative humidity data logger and anautomated rain gauge (Onset ComputerCorporation, MA, USA) were installed atkey locations in each study site to collectprimary meteorological data. Secondary data(rainfall, temperature and humidity) wereprovided by the meteorological departmentbased on the nearest weather stations to thestudy areas.

Epidemiological data collection

The number of notified dengue casesreported in each locality during the studyperiod was obtained from the local healthauthorities. Case definitions were usedaccording to the World Health Organization(WHO) classification system and wereidentical for all the study areas. A databasewas prepared for data-entry purposes and co-ordinate storage of data. Data collection wasanonymous and data confidentiality wasguaranteed. Fogging activities carried outduring outbreak in each study area were alsorecorded.

Predictive model for dengue outbreak

The aim of this part of the research is to studyfactors contributing to dengue outbreak inMalaysia by concentrating on three majoraspects - entomological, epidemiological andenvironmental. We assessed the usefulnessof larval indices for identifying high-risk

areas for dengue virus transmission.Climatic sensitivity of the model wasdetermined by correlating week-to-weekvariations in larvae densities againstvariations in the individual climaticparameters. Statistical correlations betweenclimate parameters, vector and virustransmission dynamics were analyzed. Theformula used is as follows;

Pearson correlation coefficient,

where,

Modelling Technique

Eighty seven (87) weeks of data werecollected and partition into 2 parts. First partwas for model estimation which used the first83 data points and second part was for modelevaluation which used the last four datapoints. The Autoregressive Distributed Lag(ADL) Model was used to obtain thepredictive model, where the generalunrestricted Autoregressive Distributed Lag(ADL) model can be written in the form of,

where j is the lag length, i = 1,2,…,N, t =1,2,…,T (time periods) and yit is thedependent variable which is the total larvae.xikt is the independent variables which arerainfall, minimum- maximum temperatureand maximum humidity, εit are identicallyindependently distributed random errorswith mean zero and variance , α and ø areunknown parameter to be estimated usingOrdinary Least Squares (OLS). Lag one foreach of the variables was also included inthe initial model. The general-to-specific

240

strategy was used to simplify the model withthe help of PcGets software (Hendry &Krolzig, 2001). The models were evaluatedusing Root Mean Square Errors and is writtenas,

where et = yt – ^y t , yt is the actual observationat t, and ^y t is the forecast of yt generatedfrom the models using week 1-83 observa-tions. Thus, with the estimation period beingweek 1-83, week 84-87 data were used toevaluate the model.

RESULTS

Figure 1 shows the number of Ae. aegypti

and Ae. albopictus larvae in ovitrapscollected for 87 weeks for each study site.The results indicated that Kuala Lumpur hadthe highest percentage of Ae. aegypti whichwas 66.68%, followed by Kedah (55.21%).Johor Bharu showed the lowest number ofAe. aegypti collected, 3.04%.

Figure 2 shows total number of larvaeand amount of rainfall recorded every weekfor 87 weeks study period in Taman Sejahtera,Kulim, Kedah. From the plot and correlationanalysis, there was significant but weakrelationship (r=0.236) between the totalnumber of larvae and amount of the sameweek rainfall, but moderate (r=0.525)relationship was observed between totalnumber of larvae and rainfall data of theprevious week, indicating that current weekand previous week rain helped to increasethe number of larvae (mosquito population).Based on Table 1, the study site at InderaMahkota 2, Pahang, showed that rainfall ofthe previous week contributed to a highernumber of larvae but the amount of rain ofthe same week showed negative weakcorrelation (r=-0.188). Taman PerumahanUda, Johor showed similar results as Kedah.As for Desa Pandan, Kuala Lumpur, onlyrainfall data of the previous week showedsignificant relationship with the number oflarvae. Based on the data obtained, a small

amount of rainfall (approximately 0.1 inchesand above) was enough to encourage thenumber of larvae to increase but if there is norain for 2 to 3 consecutive weeks, the numberof larvae would be reduced.

We performed a statistical analysis toexamine the effect of maximum andminimum temperatures on the number ofmosquitoes larvae found in ovitraps (Figure3 and Table 2). Results showed that thenumber of larvae collected from the ovitrapswas negatively associated with minimumtemperature. At higher minimum (26ºC andabove) and lower minimum temperatures(21-22ºC), the number of larvae decreased.

On the other hand, positive correlationwas found between the maximumtemperature and number of larvae. The riseof maximum temperature influenced theincrement of larvae. The obvious temperaturewas at 34ºC and 35ºC where it contributed tohigh number of larvae, but at highermaximum temperature (36ºC and above), areverse association was observed. However,the temperature must be supported with theamount of rain to influence the multiplicationof larvae.

Maximum humidity was positivelyassociated with the mosquito larvaecollected for all the study sites but a reverseassociation was found with minimumhumidity (Figure 4 and Table 3). The obviousreading of humidity that influenced thenumber of larvae was 90% and above. Thehumidity also must be supported with theamount of rain to influence the multiplicationof larvae.

The secondary data of rainfall,temperature and humidity provided by themeteorological department based on thenearest weather station to the study areaswere not showing similar reading with theprimary meteorological data. Thus, nosignificant relationship was obtained betweennumber of larvae and the secondarymeteorological data. This indicates that thesecondary meteorological data were notsuitable to be used in the modeling.

The study models were obtained basedon mosquito population (number of larvae)as the dependent variables and environ-mental factors (rainfall, minimum and

241

Figure 2. Number of mosquito larvae and rainfall by week in Taman Sejahtera, Kulim, Kedah

Table 1. Correlation between numbers of mosquito larvae with rainfall andprevious week rainfall respectively according to locality

Correlation:Correlation:

LocalityLarvae and Rainfall

Larvae and PreviousWeek Rainfall

Kedah -0.236** 0.525***Pahang -0.188* 0.419***Johor -0.241** 0.536***Kuala Lumpur -0.157 0.565***

*** Sig. at 1%, ** Sig. at 5%, * Sig. at 10%

Figure 1. Total number of Ae. aegypti and Ae. albopictus collected from Kedah,Pahang, Johor and Kuala Lumpur

242

Table 2. Correlation between numbers of mosquito larvae with maximumand minimum temperature respectively according to locality

Correlation: Correlation:Locality Larvae and Minimum Larvae and Maximum

Temperature Temperature

Kedah -0.591*** 0.537***Pahang -0.809*** 0.363***Johor -0.727*** 0.228**Kuala Lumpur -0.406*** 0.237**

*** Sig. at 1%, ** Sig. at 5%, * Sig. at 10%

Figure 3. Number of mosquito larvae, maximum and minimum temperatures by week inTaman Sejahtera, Kulim, Kedah

Correlation

Larvae Pearson correlationSig.

Min temperature

-0.591***(0.000)

Max temperature

0.537***(0.000)

Figure 4. Number of mosquito larvae, maximum and minimum humidity by week in TamanSejahtera, Kulim, Kedah

*** Sig. at 1%, ** Sig. at 5%, * Sig. at 10%

Correlation

Larvae Pearson correlationSig.

Minimum humidity

0.113(0.301) – not sig.

Maximum humidity

0.320***(0.003)

243

maximum temperature, maximum humidityand lag one for each of the variables was alsoincluded in the initial model) as theindependent variables.

The first 83 weeks were used to fit theinitial models, while the remaining 4 weeks,were used for model evaluation. Afterappropriate simplification, the estimated ADL

models for each locality are shown in Table4. Previous week number of larvae (TotalLarvae _1) was included in 3 of the models(Kedah, Johor and Kuala Lumpur). In otherword if previous week larvae increase,current week larvae will also increase.Previous week rainfall (Rainfall_1) wasincluded in all of the models (Kedah, Pahang,

Table 3. Correlation between numbers of mosquito larvae with maximumand minimum humidity respectively according to locality

Correlation: Correlation:Locality Larvae and Minimum Larvae and Maximum

Humidity Humidity

Kedah -0.113 0.320***Pahang -0.102 0.851***Johor -0.152 0.760***Kuala Lumpur -0.096 0.698***

*** Sig. at 1%, ** Sig. at 5%, * Sig. at 10%

Table 4. Estimated Autoregressive Distributed Lag (ADL) Models

Variable Kedah Pahang Johor Kuala Lumpur

Constant -1892.09* -575.49 -26.11 -1013.53***Total larvae_1 (X1) -0.54*** – -0.28*** -0.31***Rainfall_1 (X2) -44.80** -9.30** -27.95*** -17.72***Min. temp. (X3) -90.40*** -42.14*** -51.52*** –Min. temp_1 (X4) -58.94*** -16.23*** – –Max. temp (X5) -52.71*** -12.69*** – –Max. temp_1 (X6) -35.16*** – – –Max. humidity (X7) -25.01** -20.01*** -15.54*** -12.71***Adj R2 -0.66 -0.85 -0.76 -0.61***

*** Sig. at 1%, ** Sig. at 5%, * Sig. at 10%- ~ these variables are not significant even at 10% significant level. Therefore it is notincluded in the equation.E.g. of writing the equation: Kedah:

^Y = -1892.09 + 0.54X1 + 44.80X2 – 90.40X3 + 58.94X4 + 52.71X5 – 35.16X6 + 25.01X7

^Y ~ Dependent variable ~ Total Larvae

Independent Variables:X1 ~ Total larvae_1 ~ previous week number of larvae or lag 1 of larvaeX2 ~ Rainfall_1 ~ previous week amount of rainfall in inchesX3 ~ Min. temp ~ Minimum temperature in degree CelsiusX4 ~ Min. temp_1 ~ previous week minimum temperatureX5 ~ Max. temp ~ Maximum temperature in degree CelsiusX6 ~ Max. temp_1 ~ previous week maximum temperatureX7 ~ Max. humidity ~ Maximum humidity

To obtain fitted values, just substitutes the X1 to X7 with the data (real values) obtained fromthe study.

244

Johor and Kuala Lumpur). Maximumhumidity was also included in all models. Thisindicates that previous week rainfall(Rainfall_1) and maximum humidity is veryimportant factors in influencing mosquitopopulation for all localities. Minimumtemperature was included in 3 models(Kedah, Pahang and Johor) but maximumtemperature was significant in 2 models

(Kedah and Pahang only). All models passthe assumptions and the values of adjustedR2 ranging from 0.61 to 0.85 which isacceptable to be used for forecasting.

Figure 5 to 8 shows the actual and fittednumber of larvae for each locality in 87weeks. The fitted values were produced bysubstituting the independent variables withtheir actual values in the models (as in Table

Figure 5. Actual number of larvae against modeled number of larvae in Taman Sejahtera, Kulim,Kedah

RMSE = 98.6

Figure 6. Actual number of larvae against modeled number of larvae in Indera Mahkota 2, Kuantan,Pahang

RMSE = 43.9

245

3) where these models are mathematicalequations. The actual and fitted values arevery close to each other and the error (RMSE)is very small ranging from 43.9 to 98.6. Thisindicates that the estimated models are goodfit models and can be used as forecastingmodels.

DISCUSSION

Using mosquito egg traps for vectorsurveillance seems to be a current trend indengue endemic countries, since this methodallows better assessment of infestationdensities than the conventionally used

Figure 7. Actual number of larvae against modeled number of larvae in in Taman Perumahan Uda,Johor

RMSE =74.2

Figure 8. Actual number of larvae against modeled number of larvae in Desa Pandan, Kuala Lumpur

RMSE = 84.0

246

methods based on the search for larvae(Morato et al., 2005). We used ovitraps, setout weekly, to monitor Ae. aegypti and Ae.

albopictus populations. Week-to-weekvariations in simulated larva densities werecorrelated against variations in the individualclimatic parameters.

With the build-up of entomological,climatologically and epidemiologicaldatabases, the next logical step would beto develop models to test hypothesesconcerning vector and disease relationshipsand the nature and processes of diseasetransmission. Mathematical models remaina powerful tool for epidemiological analysesand are likely to play a prominent role in thestudy of epidemic dengue. However, multipleintrinsic factors, both human and vector, aswell as extrinsic environmental factors suchas temperature, rainfall and humidity affectepidemic dengue virus transmission (Ooi& Gubler, 2009). Correlation and Autoregressive Distributed Lag (ADL) model wereused to study the relationship among thesevariables. In this investigation, the strongestrelationship occurs between variations inmosquito larvae densities and both previousweek rainfall and maximum humidity. Wuet al. (2007), using autoregressive integratedmoving average models, found an associationof dengue incidence in Taiwan withtemperature and relative humidity and Fockset al. (1995) using simulation approachmodels described the daily dynamics ofdengue virus transmission in the urbanenvironment in San Pedro Sula, Honduras.

The presence and abundance of Ae.

aegypti and Ae. albopictus are vital to thetransmission of dengue. Various researcheshave investigated the relationship betweendengue transmission and Aedes population,expressed as larval indices (Pontes et al.,2002; Teixeira et al., 2002). Scot & Morrison(2004) showed that traditional larval indicesin Peru were correlated with the prevalenceof human dengue incidence. Regis et al.(2008) used the egg average to identify areaswith high concentration of mosquitoes. Theyconsidered this strategy a good one to detectand prevent Ae. aegypti population outbreaksand consequently, a good measure of denguerisk.

This study found direct relationshipsbetween the mean numbers of larvaecollected by oviposition traps withtemperature. Temperature affects thepotential spread of dengue virus through eachstage in the life cycle of the mosquito. Lowertemperatures adversely effected the survivalof adult and immature Aedes mosquitoes(Lu et al., 2009) while higher minimumtemperatures might assist larvae survival.Donalisio & Glasser (2002) reported thatthe minimal temperature was the mostimportant factor in determining the levelsof vector infestation. Our results indicatedthat temperature deviation was the mostsignificant predictor for the adult populationto increase. Rising temperatures mayaccelerate the mosquito’s rates ofdevelopment and consequently, one mightexpect increases in mosquito abundance.

In this study it was found that maximumhumidity had a positive association with theAedes larvae densities but not minimumhumidity. Relative humidity influencedlongevity, mating, dispersal, feeding behaviorand oviposition of mosquitoes and rapidreplication of the virus (Hales et al., 2002).At high humidity, mosquitoes generally livelonger and disperse further. Relative humidityalso directly affects the evaporation rates ofvector breeding sites.

The study also indicated that humidity ishigh only when rainfall and temperatures arehigh and these are conditions that areconducive to breeding and survival of vectorpopulations and rapid replication of virus. Wuet al. (2007) reported that temperature andrelative humidity are the major determinantsin the fluctuation of dengue fever incidencein Kaohsiung City, Taiwan. Vezzani et al.(2004) reported that higher population of Ae.

aegypti larvae were found during periods ofhigher temperature and greater rainfall.Pontes et al. (2000) reported that the seasonalfluctuation of rainfall, Aedes larval indicesand dengue incidence showed a strongrelation in the patterns of the three series.

Precipitation is an important factor in thetransmission of DHF. All mosquitoes haveaquatic larval and pupal stages and thereforerequire water for breeding (Lindsay &Mackenzie, 1997). The study showed that

247

timing of rainfall is as important as the amountand frequency of rain. The pattern of rainfallmay also play apart. Extremely heavy rainfallmay flush mosquito larvae away frombreeding sites or kill them outright (Promprouet al., 2005). More frequent, lighter rains mayreplenish existing breeding sites andmaintain higher levels of humidity that assistin dispersal and survival of adult mosquitoes.

The study also showed that rainfall dataof the previous week showed positiverelationship with mosquito population in allstudy areas. When the amount of rainincreases, larva population also increases.These could be partially explained by lookingat entomologic indices since Aedes needs6-10 days to develop from egg to larval stage(Lee, 1992). Our findings with respect torainfall are in general agreement with thefindings of Loh & Song (2001) who reportedsimilar results for Ae. aegypti and Ae.

albopictus in Singapore. Based on this study,it may be useful in assessing and moreimportantly, responding to the risk of a denguefever outbreak in a localized area based oncurrent entomological surveillance data andrainfall data of the previous week. Such studycan confirm the possible influence of rainfallon the prevalence of dengue. Therefore,intensification of surveillance and control ofmosquitoes during the period of hightemperatures and frequent rainfall isrecommended.

However, rainfall is geographicallystochastic, meaning that data collected asclose to the area is most desirable (Williamset al., 2008). Our study of rainfall hererevealed some local variation in rainfalltotals but strong correlation with the data thatwere recorded at weather stations installedat key locations in each study site. Secondarydata provided by meteorological departmentare not showing significant relationship. Themodeling data of this study showed that theactual and fitted are closed to each otherbecause we collected our own weather dataat each locality for 87 weeks and not usingsecondary data provided by meteorologicaldepartment. This indicates that the estimatedmodels are good fit models and can be usedas forecasting models to predict dengueoutbreak. The model will be more accurate if

the respective institutions set their ownweather station at dengue prone areas.

A mosquito forecast for higher-than-normal densities in a particular region, couldmotivate health officials to alert the public tothe increased risk of acquiring dengue andincrease mosquito control efforts. A mosquitodensity forecast for a particular area,together with information on which denguevirus is circulating, as well as knowledge ofcurrent mosquito control efforts, can be acomponent of an early warning system fordengue. However, prediction model of smallscale by using localized parameters,including weather parameters, hostcondition, vector density and otherenvironmental variables was the one thatcould accurately predict the actual risk ofhuman cases. Such a finding could be appliedto assist in establishing an early warningsystem based on weather forecasts andmaking decision on public health preventionprogramme such as vector control, otherenvironmental intervention and personalprotection promotion. In conclusion, thisstudy indicated that Aedes larval populationshowed strong relation with maximumtemperature and rainfall data of the previousweek.

Acknowledgments. The authors are gratefulto the Director-General of Health, Malaysiafor permission to publish this paper. Weespecially thanked the staff of MedicalEntomology Unit of IMR, Health State Vectorwithout whose diligence and hard work underdifficult field conditions this research wouldnot have been accomplished. The study wasfunded by National Institutes of Health (06-CAM-05-01), Ministry of Health, Malaysia.

REFERENCES

Focks, D.A., Daniels, E., Haile, D.G. &Keesling, J.E. (1995). A simulation modelof the epidemiology of urban denguefever: Literature analysis. Modeldevelopment, preliminary validation, andsamples of simulation results.

Hales, S., Wet, N., MacDonald, N. & Woodward,A. (2002). Potential effect of population

248

and climate changes on global dis-tribution of dengue fever: an empiricalmodel. Lancet 360: 830-834

Hendry, D.F. & Krolzig, H.M. (2001).Automatic econometric model selection

using PcGets 1.0. Timberlake Con-sultants Ltd.

Lee, H.L. (1992). Aedes ovitrap and larvalsurvey in several suburban communitiesin Selangor, Malaysia. Mosquito-Borne

Diseases Bulletin 9(1): 9-15.Lindsay, M. & Mackenzie, J. (1997). Vector-

borne viral diseases and climate changein Australia region: major concerns andthe public health response. In: Curson P.,Guest C. Jackson E. editors. Climatechanges and human health in the Asia-Pacific region. Canberra: Australian

Medical Association and Greenpiece

International pp 47-62.Loh, B. & Song, R.J. (2001). Modeling dengue

cluster size as a functional of Aedes

aegypti population and climate inSingapore. Dengue Bulletin 25: 74-78.

Lu, L., Lin, H., Tian, L., Yang, W., Sun, J. & Lin,Q. (2009). Time series analysis of denguefever and weather in Guangzhou, China.BMC Public Health 9: 395.

Morato, V.C.G., Thavara, Y., Tawatsin, A.,Chompoorschi, D.P. & Barreto, M. (2005).Infestation of Aedes aegypti estimated byoviposition traps in Brazil. Revistade

Saude Publica 39: 553-558.Pontes, K.A., Curtis, C., Seng, S.M., Olson, J.G.

Chanta, N. & Rawlins, S.C. (2002). Theuse of ovitrap baited with hay infusion asa surveillance tool for Aedes aegypti

mosquitoes in Cambodia. Dengue

Bulletin 26: 178-184.Promprous, S., Jaroensutasinee, M. &

Jaroensutasinee, K. (2005). Climaticfactors affecting Dengue haemorrhagicfever incidence in Southern Thailand.Dengue Bulletin 29: 41-49.

Regis, L., Montero, A.M., Mello Santos, M.A.V.,Silveira, J.J.C., Furtado, A.F., Acroli, R.V.,Santos, J.M., Nakazawa, M., Carvalno,M.S. & Sauza, W.V. (2008). Developingnew approaches for detecting andpreventing Aedes aegypti populationoutbreaks: Basis for surveillance, alertand control system. Memorias Institute

Oswaldo Cruz, Riode Jeneiro103(1): 50-59.

Rohani, A., Asmaliza, S., Zainah, S. & Lee, H.L.(1997). Detection of dengue virus fromfield Aedes aegypti and Aedes albopictus

adults and larvae. Southeast Asian

Journal Tropical Medicine Public

Health 28(1): 138 – 142. Rohani, A., Zamree, I., Lee, H.L.,

Mustafakamal, I., Norjaiza, M.J. &Kamilan, D. (2007). Detection oftransovarial dengue virus in field-caughtAedes aegypti and Aedes albopictus

mosquitoes using C6/36 cell culture andreverse transcriptase-polymerase chainreaction (RT-PCR) techniques. Dengue

Bulletin 41: 47-56.Teixeira, M.G., Banetto, M.C., Costa, M.C.,

Ferreira, C.C., Vasconcelos, D.F. &Caincross, S. ( 2002). Dynamics of denguevirus circulation: a silent epidemic in acomplete urban area. Tropical Medicine

Intenational Health 7: 267-262.Scott, T.W. & Morrisson, A.C. (2004). Aedes

aegypti density and the risk of denguevirus transmission. In: Takken W., ScottT.W. editors. Ecological aspects forapplication of genetically modifiedmosquitoes. Dordrecntl (the Nethelands):Kluwer Academic Publishers. 187-206pp.

Vezzani, D., Velazquez, S.M. & Chweigmann,S. (2004). Seasonal pattern of abundanceof Aedes aegypti (Diptera: Culicidae) inBuenos Aires City, Argentina. Memorias

Institute Oswaldo Cruz 99: 351-356.WHO. (1985). Viral haemorrhagic fevers.

General research and recommendationfor viral haemorrhagic fevers. WorldHealth Organization. Geneva.

William, C.R., Jhonson, P.H., Long, S.H.,Rapley, L.P. & Ritchie, S.A. (2008). Rapidestimation of Aedes aegypti populationsize using simulation modeling withmodel approach to calibration and fieldvalidation. Journal Medical Entomology

45(6): 1173-1179.Wu, P.C., Guo, H.R., Lung, S.C., Lin, C.Y. & Su,

H.J. (2007). Weather as an effectivepredictor for occurrence of dengue feverin Taiwan. Acta Tropica 103: 50-57.