Abstract
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
associations with rainfall and wind-speed were weaker.
The approach is able to examine multiple factors and model how complex trends
arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in
May-June and its spatial variability. This transparent and straightforward approach
leads to accurate predictions without relying on regression models and/or postulating
specific parameterisations. A key output of the analysis is a thoroughly
phenomenological description of the incidence of the disease conditional on specific local
weather factors. The study can be crucially important to infer the elusive mechanism of
transmission of campylobacteriosis; for instance, by simulating the conditional incidence
for a postulated mechanism and compare it with the phenomenological patterns as
benchmark. The findings challenge the assumption, commonly made in statistical
models, that the transformed mean rate of infection for diseases like campylobacteriosis
is a mere additive and combination of the environmental variables.