introductionto mathematical epidemiology 1. biomedicalcontext · introductionto mathematical...
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Introduction to mathematical
epidemiology
1. Biomedical context
Thomas Smith
September 2011
Biostatistics and Computational Sciences
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Epidemiology
The study of the distribution and determinants of health-related states or events in specified populations, and theapplication of this study to control of health problems
Mathematical Epidemiology
Chronic diseases
e.g. heart disease, diabetes, obesity
Frequently linear systems, often complex causal relationships
Infectious disease
e.g. pneumonia, malaria, infectious diarrhoea
Generally non-linear systems
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Chronic disease epidemiology
Risk Factor Health Outcome
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Systems in public health
Most health problems occur within complex webs of causality
1. Determinants of exposure are complex
2. Implementation of preventive interventions or treatment israrely perfect
3. Human genetics and behaviour are highly variable
Some roles of mathematical modeling
1. Understanding the drivers of the system
2. Identifying data gaps and uncertainties
3. Evaluating the potential of different interventions
4. Quantitative prediction of outcomes?
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Some disciplines and topics in public health and epidemiology
Medicine
1. Pathophysiology
2. Diagnostics
3. Therapeutics
Computational sciences
1. Statistics
2. Information science
3. Mathematics of dynamic systems
4. Demography
Social Science/Psychology
1. Perspectives of different stakeholders
2. Communication
Biology
1. Pathogen biology
2. Human biology
Intervention technologies
1. Pharmacology
2. Environmental modification
3. Biological control
4. Physical methods
Environmental Sciences
1. Environmental risk factors
Politics and economics
1. Organisation of the health system
2. Incentives and costs
3. Health and safety legislation
4. Human resource availability
Etc.
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Infectious disease epidemiology
Infection Health Outcome
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Malaria: transmission cycle
Mosquito
Liver
Blood
Livercell
Red cell
Gametocyte
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Dengue Outbreak in Cuba 1981 : health effects
DF- dengue fever
DHF- dengue haemorrhagic fever
DSS- dengue shock syndrome
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Types of pathogens
• Viruses
• Bacterial
• Fungi
• Protozoa
• Helminths
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VirusCapsid, protein spikes, genetic material (DNA, RNA)
� 100 nm � � 100 µm �
Host cell
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Bacterium –Capsule (sticky, biofilm), Pili (attach), DNA, flagellum (moving); prokaryoteMost bacteria beneficial (intestinal flora, degradation nutrients), spore forming, some pathogens
� 2 µm �
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Corynebacterium sp. Staphylococcus aureus
Micrococcus
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Parasite:Protozoa (one cell), Helminths (worms), Ectoparasite (lice, fleas, lice…),
Giardia lamblia
Malaria (Plasmodium sp.)
Schistosomiasis
Hookworm
�10 µm �
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Types of pathogens
• Directly transmitted
• Vector-borneRespiratory droplets
Dust
Contaminated water
Cercariae
Adult parasite
Egg
Fresh-water snail
urinestool
Snails Mosquitoes
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Schistosomiasis - Life Cycle
Cercariae
Adult parasite
Egg
Fresh-water snail
urinestool
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Approaches in modeling infectious diseases
• ODEs / Compartment models e.g.
S L I R infection
susceptible latent infectious recovered
pathogenesis recovery
transmission
mortality
Idt
dRIIL
dt
dILSIN
dt
dLSIN
dt
dSννµσσββ =−−=−=−= ;;)(;)(
where β is the per capita transmission rate, 1/σ is the mean latent period, µ is the per
capita mortality rate due to infection, ν is the rate of recovery from infection, N =
S+L+I+R = total population size
M.E.J. Woolhouse et al., University of Edinburgh, December 2006
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0
0
11)(
I
RepidemicP
−=
0
0.25
0.5
0.75
1
1 1.5 2 2.5 3
R 0
P(e
pid
em
ic)
2510
5
1
Primary infections, I0
May et al. (2001) Phil Trans R Soc Lond - Ser B
M.E.J. Woolhouse et al., University of Edinburgh, December 2006
Approaches in modeling infectious diseases
• Stochastic processes e.g.
– Probability of an epidemic
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Homogeneous
Nearest neighbour
Heterogeneous
M.E.J. Woolhouse et al., University of Edinburgh, December 2006
Approaches in modeling infectious diseases
• Contact network models
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ID 4
ID 2
ID 3
T=1
ID 1
T=3
increasing immunity
T=4
Malaria death
T=2
treat
new infection
Approaches in modeling infectious diseases
• Microsimulation
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Example: Malaria
– Parasite: number 1 killer
– 4 parasites: Plasmodium falciparum most deadly
– Disease: from symptomless to acute disease & death (semi-immunity possible)
– Treatment exists but: expensive, diagnosis not easy, under medical supervision, costly, … no vaccine
– 42% of world population at risk
– Ca. 300 million cases/year; ca 0.8 million death/year
– Huge economic burden (hospital admission, disease periods, death, …)
– Mosquito: Anopheles sp., large number of species
– Ecology of Anopheles sp. varies: Urban / rural; Plains, forest, mountains, …; Small – large water sites; Look closely at local condition
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Malaria: transmission cycle
Mosquito
Liver
Blood
Livercell
Red cell
Gametocyte
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Malaria: other feedback loopsAnopheles
feeding-cycle
X
Effects of health systems
Malaria
economicunderdevelopment
Anopheleslife-cycle
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Malaria endemic countries (WHO, 2005)
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Impregnated mosquito netImpregnated mosquito net