Climate and immunity as drivers of interannual variability of human West Nile vrus cases

Background/Question/Methods

The extent to which climate change will influence vector-borne disease risk is an area of active debate. Non-climatic factors such as temporal changes in the abundance or immunity of host populations could be more important than climate as drivers of interannual fluctuations in disease incidence. Furthermore, although numerous studies show that temperature can alter vector and parasite life histories and vital rates, the unimodal nature of these relationships suggest that the net effect for disease risk can be challenging to predict. Sporadic epidemics of human West Nile Virus cases have occurred since its initial introduction to the United States in 1999, yet little is known about what drives interannual fluctuations in the number of human neuroinvasive cases of West Nile virus.  Regional analyses over short time-scales have suggested potentially positive effects of temperature and mixed effects of precipitation on West Nile cases, but no studies have yet explored interannual fluctuations in cases across the contiguous United States over the last 15 years.  We analyzed temporal patterns in the number of human neuroinvasive cases in the United States since 1999 to determine the relative influence of climate and other host factors on disease risk

Results/Conclusions

The dominant temporal pattern was one of exponential decay in the number of human cases over time.  This is likely due to an initial accumulation of immunity in the highest risk pool of the human population. Interestingly, there were substantial deviations from this exponential decay pattern in several regions.  After removing the effect of time-since-introduction on the number of cases, we found a positive relationship between temperature and the number of cases in several regions, particularly clustered around the Midwest.  Precipitation did not influence temporal patterns in West Nile cases, even when analyzed separately by region.  These results suggest that accounting for multiple factors that drive transmission can be important for detecting underlying climate effects, which could vary regionally according to differences in land use, vector, or host populations.