Climate and infectious diseases: a dynamical perspective
The latest IPCC report has strongly confirmed that the climate is changing. The present and future impact of climate change on infectious disease dynamics remains an important, but still controversial subject. Malaria is a major example of a public health burden around the tropics with the potential to significantly worsen in response to climate change; temperature as a limiting factor for the pathogen, and temperature and rainfall play a crucial role in determining the population dynamics of its mosquito vector. Similar concerns apply to other vector-borne and water-borne diseases, particularly given the existing evidence for the role played by climate at seasonal and inter-annual time scales (e.g. ENSO). This workshop will explore the boundaries of current knowledge and in particular will examine what role theory and mathematical (epidemiological) models can play in advancing the understanding and prediction of the coupling between two highly nonlinear phenomena: climate and infectious disease dynamics.
While dynamical models have contributed significantly to our general understanding of the population dynamics of infectious diseases, most often their application has been to assume stationary environmental conditions and examine the constraints that climate places on the geographical distribution of vector borne diseases. However, most infectious diseases are highly dynamic with outbreaks that vary in size from year to year, including intermittent epidemics, the emergence of new pathogens, and exacerbation in the prevalence of old ones. Thus, there is an urgent need to better understand the dynamics of infectious disease outbreaks and their response to climate variability; this needs to be developed in the context of longer-term environmental change.
The workshop will be organized around the following main topics: (1) the consideration of dynamical approaches, vs. static ones, to address the effects of climate change in both time and space; (2) the relevant spatial and temporal scales of coupling of climate and epidemiological models for constructing both early-warning systems and future scenarios; and (3) the application of mathematical models and theory to address the synergy between climate change and other different aspects of human-induced change: the evolution of drug resistance, changing patterns of land-use, and socio-economic conditions.
The understanding and forecasting of these patterns requires an ecological perspective that builds on the long-history of mathematical models for infectious disease dynamics, but expands these efforts in several important ways to develop our ability to: (1) interface these models with existing data on both disease and environment, (2) consider different temporal scales of change to address the dynamics of outbreaks together with longer-term trends, and (3) bridge the different organizational scales of within and between host dynamics.
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