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Edwin Michael, Paul Parham & Hannah Slater
Grantham Institute for Climate Change, Dept. of Infectious Disease Epidemiology, Imperial College London, London W2 1PG
INTRODUCTION
Climate variation and change could have potentially profound implications for
infectious disease epidemiology and control, particularly in the case of the major
vector-borne and environmentally-mediated diseases. There is also increasing
recognition that integrated modelling frameworks allowing simultaneous addressing of
infectious disease population dynamics, the often non-linear interactions and evolution
of such dynamics with climate change and the association of disease spread with socioecological factors, including community adaptability and resilience to perturbations,
need to be developed if realistic assessments of the infectious disease impact of future
climate change are to be made. The major objective of this research programme is to
begin the development of such a robust modelling framework for not only determining
the risk of infectious disease emergence, spread and persistence in vulnerable
communities, but also for guiding the design of resilient adaptation or mitigation
strategies for a range of environmentally-driven infections.
Climate Change and Vector-Borne Infectious Diseases
Spatial Epidemiology and Integrated Control of Vector-Borne Diseases in Africa
Despite representing only one source of possible increases in morbidity and
mortality, changes in the severity and global distribution of vector-borne
disease transmission are thought to represent a significant biological impact.
Along with dengue and schistosomiasis, malaria is thought to be one of the
major vector-borne diseases most sensitive to changing environmental
conditions, although a considerable range of infectious diseases, including
cholera, lymphatic filariasis and tick-borne encephalitis are also likely to be
affected. Despite the sensitivity of malaria transmission to changes in
environmental variables, and in spite of being one of the biggest causes of
worldwide mortality due to infectious diseases, there is still substantial
debate as to the exact role that climate plays as a driving force for malaria
epidemics.
Malaria and lymphatic Filariasis pose the largest public health burden of all diseases
worldwide. Recently, there has been an interest in undertaking an integrated control
strategy targeting both diseases simultaneously. They often co-occur in the same regions
and in the same individuals, so interventions against one disease alone may have
consequences for the transmission of co-occurring diseases. Both diseases are vectorborne, so efforts to reduce vector populations and reduce human-vector contact could
reduce the prevalence of both diseases. It is thought that integrated control of multiple
diseases would remove duplication of effort and costs in programmes that share common
activities. The aim of this research is to develop an evidence-based framework for
assessing the health and financial implications of integrated control strategies by:
1. Developing climate-based spatial models using Bayesian and Maximum Entropy methods
to predict the distribution and prevalence of the diseases across Africa. These models may
then be used to predict how disease distribution will be affected by climate change.
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Fig. 1a. Ultimate mosquito extinction probabilities across Tanzania in April (darker areas
denote higher probabilities) and average temperature and rainfall values across the year
Developing climate-based Pathogen Transmission Models
The overall aim of this project is to develop a theoretical framework for
modelling, analysing and evaluating climate-based and environmentallylinked infectious disease models. The specific objectives are twofold:
Anopheles mosquito taking a blood meal
Elephantiasis of leg due to filariasis
Global environmental change is the general term for the wide range of environmental
issues potentially attributable to human activities. Climate change and ozone depletion, in
particular, may significantly affect human health and this research aims to establish the
effects due to infectious diseases. Historical global weather station data and the predictions
of General Circulation Models (GCMs) allow climate model validation and prediction for a
range of emission scenarios and mathematical modelling offers a powerful tool for linking
climatic variables and disease transmission.
1. To develop a clear theoretical foundation for linking climate change with
epidemic models, with a focus on the role of complex infection dynamics in
influencing pathogen population response to climate change.
2. Application to specific endemic, emerging and re-emerging infections,
including optimal cost-effective adaptation and mitigation policies.
This framework will seek to combine climate modelling with mathematical
models, as well as the socio-ecology and policy dimensions of disease
transmission. To date, we have focussed our attention on the construction of
realistic climate-based malaria transmission models that capture the effects
of rainfall and temperature, highlighting how analyses of dynamic models can
enable examination of critical issues not completely addressed to date
regarding malaria transmission response to climate change. These include
the impact on mosquito population dynamics, invasion behaviour in diseasefree regions and the effects of seasonal variability in climate variables.
Fig. 1a. Prevalence of
lymphatic filariasis in
Africa predicted via
Bayesian regression
Fig. 1b. Probability of
lymphatic filariasis
presence predicted by
a Maximum Entropy
model
0 - 0.5
0.5 - 2
2-5
5 - 10
High risk: Prob > 25%
10 - 15
15 - 20
Medium risk: Prob > 5%
20 - 30
30 - 50
Low Risk: Prob > 0.35%
50 - 75
75 - 100
Fig. 1a.
Fig. 1b.
2. Developing and applying climate-dependent mathematical models of malaria and filariasis
transmission to be used for predicting the effects of various intervention measures.
3. Undertaking a geographically-based economic evaluation of the cost-effectiveness of
potential control strategies.
4. Developing a probabilistic decision analysis framework for assessing costs and benefits
associated with potential control strategies.
CONCLUSIONS
Our results have highlighted the need and importance of both empirical spatial analysis and
climate-based disease transmission modelling for developing quantitative frameworks to
analyze and predict the impact of climate change/variation on vector-borne disease spread,
persistence and emergence. Obtaining better data on functional forms of the relationships
between key climate variables and pathogen transmission components, treatment of
stochasticity, and how best to integrate socio-ecological elements of disease risk, represent
the major next steps to advancing our aim of integratively modelling the impact of climate
change on infectious disease transmission and control.
REFERENCES
A power station burning fossil fuels
Potential environmental impacts of
climate change
Fig. 1b. Rainfall and temperature profiles, plus predicted changes in R0
values across Tanzania by 2080 under A2a and B2a emission scenarios
[1] Modelling Climate Change and Malaria Transmission. Parham, P. E., Michael, E. In Modelling Parasite Transmission and Control (In press).
[2] Modelling the Effects of Weather and Climate Change on Malaria Transmission. Parham, P. E., Michael, E. (In submission)
[3] Stochasticity and climatic seasonality in the likelihood of malaria emergence. Parham, P. E., Michael, E. (In preparation).
[4] The role of climatic variables in driving melioidosis transmission . Parham, P. E., Godfrey, E., Michael, E. (In preparation).
[5] Discrete-time modelling of malaria transmission and the dependence on climatic variables. Parham, P. E., Clapham, H., Michael, E. (In preparation)
[6] Mapping the spatial distribution of Lymphatic Filariasis in Africa using Maximum Entropy Methods. Slater, H., Michael, E. (In preparation).