Identifying key weather factors influencing human salmonellosis: A conditional incidence analysis in England, Wales, and the Netherlands

Objectives: This study aimed to improve the understanding of seasonal incidence pattern observed in salmonellosis by identifying the most influential weather factors, characterising the nature of this association, and assessing whether it is geographically restricted or generalisable to other locations. Methods: A novel statistical model was employed to estimate the incidence of salmonellosis conditional to various combinations of three simultaneous weather factors from 14 available. The analysis utilised daily salmonellosis cases reported from 2000 to 2016 along with detailed spatial and temporal weather data from England and Wales, and the Netherlands. Results: The incidence simulated from weather data effectively reproduced empirical incidence patterns in both countries. Key weather factors associated with increased salmonellosis cases, regardless of geographical location, included air temperature (>10 ⁰C), relative humidity, reduced precipitation, dewpoint temperature (7–10 ⁰C), and longer day lengths (12–15 h). Other weather factors, such as air pressure, wind speed, temperature amplitude, and sunshine duration, showed limited or no association with the empirical data. The model was suitable for the Netherlands, despite a difference in case ascertainment. Conclusions: The conditional incidence is a simple and transparent method readily applicable to other countries and weather scenarios that provides a detailed description of salmonellosis cases conditional on local weather factors.