Abstract
The accelerating rate of global climate and environmental changes is expected to affect the distribution, frequency, and patterns of established infectious diseases, as well as the emergence and re-emergence of both new and known diseases. Salmonella is a leading cause of foodborne illnesses in Europe, accounting for nearly one in three foodborne outbreaks. The seasonal pattern observed in cases of human salmonellosis reported suggests that weather may be a relevant driver of disease. Many studies show associations of salmonellosis with weather factors, but the exact extent of this influence is still unclear. Elucidating how the disease depends on relevant weather factors provides insights into the underlying mechanisms of transmission and provides a tool to anticipate the risk when relevant weather factors are known.
This study provides new insights into the relationship between weather factors and the occurrence of salmonellosis, addressing a crucial issue in the context of climate change. By utilizing long-term, high-resolution epidemiological data from England and Wales linked with local weather data, the study offers a comprehensive phenomenological description of specific weather conditions that are related the incidence of salmonellosis. Unlike previous studies that often rely on regression models or predefined parameterizations, the methodology used in this study employs a transparent and straightforward approach to estimate disease incidence based on a wide range of 14 local weather factors linked to individual cases. A key contribution of this study is its ability to account for the simultaneous effect of up to three weather factors, providing a more holistic understanding of their combined impact on disease incidence.
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. These findings were validated both in England and Wales and the Netherlands, which encourages the application of the model in other regions with different climatic and social characteristics to gain new insights on the incidence of salmonellosis. Likewise, the methodology can be adapted to explore other environmental factors, such as land use, proximity to animal farms, or socio-economic factors, providing a more holistic understanding of disease dynamics.
The methodology used in this study, the conditional incidence, provides a robust framework to select key weather factors and exclude less relevant ones and to better understand climate-sensitive diseases and their response to climate changes. Early warning systems enhanced with weather data can improve incidence patterns predictions and tailor interventions to specific geographic areas.