Abstract
The significance of the environment within the One Health approach for addressing emerging health challenges is gaining widespread recognition. The observed seasonal pattern in the incidence of certain infectious diseases, such as salmonellosis, prompted to investigate weather as a potential driver of cases and to question its ability to predict incidence. Salmonella is an important causative agent of diarrhoeal disease worldwide with an estimated 80.3 million cases annually. In Europe, Salmonella is frequently involved in foodborne outbreaks, accounting for almost one in three foodborne outbreaks. This thesis investigates the main meteorological factors associated with human salmonellosis and characterized their role in contributing to disease. The overarching objective of the thesis is to improve preparedness of salmonellosis informed from meteorological data.
The conditional incidence model is a statistical approach to estimate the incidence of salmonellosis based on weather conditions by stratifying 17 years of historical data from England and Wales. One of the novelties of this method lies in the comprehensive exploration of the effect of 13 weather factors with high spatial-temporal linkage and stratified in different combinations. The robustness of the methodology was validated by comparing the model outcomes with reported laboratory-confirmed data, as well as its application in a different location (i.e., the Netherlands). The model was also applied to investigate the impact of eggs and chicken on disease ocurrence under different temperatures, and to assess the effect of climate change on the seasonality of salmonellosis.
Air temperature (>10⁰C), relative humidity, precipitation (dry conditions), dewpoint temperature (7-10⁰C), and day length (12-15h) were identified as key weather factors associated with salmonellosis, irrespective of geographical location. In a future climate scenario marked by rising temperatures, this conditional incidence suggests an increase in the overall number of cases with a less marked seasonal pattern. The presented research expands our understanding of the widely accepted but under-investigated role of weather and climate in modulating disease. Additionally, this thesis contributes to generating hypotheses on the mechanisms through which weather contributes to disease, establishing a foundation for future predictive tools applicable to other environmentally-driven diseases.