Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead

Abstract

Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.

Keywords: climate; climate change; infection; mathematical model; mechanism; statistical model.

Figure 1.

Issues with linking incidence and climate drivers for immunizing infections (a) Simulated precipitation over 6 years, set to reflect bimodal peaks in each year as observed (e.g. in Kenya), with periods of low precipitation shown in grey. (b) Resulting cases for a completely immunizing infection with a two-week generation time and a low basic reproductive number (R₀) (set to R₀ = 5) simulated using an SIR transmission dynamic model as in [43]. The resulting time series shows a clear footprint where periods of low precipitation and thus low transmission correspond to outbreaks turning over (red arrows), where conversely high precipitation is associated with increases in incidence. (c) Resulting cases for an identical infection simulated using an SIR model, but with a high basic reproductive number (R₀ = 32). In this case, the resulting time series shows a much more erratic picture, with little direct indication of the impact of precipitation on cases, especially in low incidence years (i.e. no increase in cases with increases in precipitation in year 2), as a result of the dominant multi-annual period resulting from the intrinsic dynamics.

Figure 2.

Schematic of the range of modelling approaches ordered across a spectrum of increasing incorporation of mechanism. (Online version in colour.)