Grantham Institute for Climate Change, Dept. of Infectious Disease Epidemiology, Imperial College London, London W2 1PG

1. 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 socio-ecological 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. • Spatial Epidemiology and Integrated Control of Vector-Borne Diseases in Africa • 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 vector-borne, 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: • 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. • Developing and applying climate-dependent mathematical models of malaria and filariasis transmission to be used for predicting the effects of various intervention measures. • Undertaking a geographically-based economic evaluation of the cost-effectiveness of potential control strategies. • Developing a probabilistic decision analysis framework for assessing costs and benefits associated with potential control strategies. Climate Change and Vector-Borne Infectious Diseases 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. 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 environmentally-linked infectious disease models. The specific objectives are twofold: 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 disease-free 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 Fig. 1a. Ultimate mosquito extinction probabilities across Tanzania in April (darker areas denote higher probabilities) and average temperature and rainfall values across the year Fig. 1a. Fig. 1b. 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. 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. High risk: Prob > 25% Medium risk: Prob > 5% Low Risk: Prob > 0.35% Fig. 1b. Rainfall and temperature profiles, plus predicted changes in R0 values across Tanzania by 2080 under A2a and B2a emission scenarios Potential environmental impacts of climate change A power station burning fossil fuels Climate Change and the Spread of Infectious Diseases Edwin Michael, Paul Parham & Hannah Slater Grantham Institute for Climate Change, Dept. of Infectious Disease Epidemiology, Imperial College London, London W2 1PG REFERENCES [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).