A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales
Fig 5
Prediction of seasonal patterns for daily Campylobacter cases as done in Fig 4 for the situation when 2 variables are constant (Weather variables averaged over the past 14 days).
A) Constant relative humidity 76% and day-length 15 hours. B) Constant maximum air temperature 20°C and day-length 15 hours. C) Constant maximum air temperature 20°C and relative humidity 76%. D-E-F) Patterns for daily 14-days rolling mean for maximum air temperature, relative humidity and day-length averaged over 19 years. The shaded area represents the 25% and 75% quantiles. G-H-I) Conditional incidence vs the variable weather factors for the situation corresponding to A) B) and C) respectively.