A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated < i > Campylobacter < /i > surveillance data for England and Wales

by Giovanni Lo Iacono, Alasdair J. C. Cook, Gianne Derks, Lora E. Fleming, Nigel French, Emma L. Gillingham, Laura C. Gonzalez Villeta, Clare Heaviside, Roberto M. La Ragione, Giovanni Leonardi, Christophe E. Sarran, Sotiris Vardoulakis, Francis Senyah, Arnoud H. M. van Vliet, Gordon Nichols Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5 ° (Celsius) increase in temperature within the range of 8°–15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75–80%, while the associati...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research