Identifying key weather factors influencing human salmonellosis: A conditional incidence analysis in England, Wales, and the Netherlands.
The Journal of infection·
- DOI
- 10.1016/j.jinf.2025.106410
- PMID
- 39824293
- PMCID
- —
- OpenAlex
- W4406410882
- Study type
- Journal article
- Publisher
- Publisher unavailable
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The conditional incidence method offers a transparent and readily applicable tool for characterizing weather-disease relationships in different geographic settings and weather scenarios, which may inform public health planning for salmonellosis prevention and control.
Structured evidence summary
Research question
The study sought to identify which weather factors most strongly influence seasonal patterns of salmonellosis incidence and to determine whether these associations are geographically generalizable.
Study design
A conditional incidence modeling approach was applied to daily reported salmonellosis cases from 2000 to 2016, combined with spatially and temporally detailed weather data from England, Wales, and the Netherlands. The model estimated disease incidence conditional on various combinations of three simultaneous weather factors selected from 14 candidates.
Population and setting
The analysis included daily salmonellosis case reports from England, Wales, and the Netherlands over a 16-year period. The model was validated across both settings despite differences in case ascertainment methods.
Main findings
Weather-simulated incidence successfully reproduced observed seasonal patterns in both countries. Key weather factors consistently associated with higher salmonellosis incidence included air temperature above 10°C, elevated relative humidity, reduced precipitation, dewpoint temperature between 7 and 10°C, and longer day length between 12 and 15 hours. Air pressure, wind speed, temperature amplitude, and sunshine duration showed minimal or no association with reported cases.
Public-health relevance
The conditional incidence method offers a transparent and readily applicable tool for characterizing weather-disease relationships in different geographic settings and weather scenarios, which may inform public health planning for salmonellosis prevention and control.
Important limitations
The summary is limited to the supplied single-article abstract and metadata. Comprehensive interpretation of model assumptions, data quality, confounding, and generalizability requires access to the full published paper.
GIDS interpretation
This paper was identified through classifier links to salmonellosis, the Netherlands, and climate and environment topics. It provides methodological context for understanding how weather conditions may relate to salmonellosis case reporting patterns but does not address real-time surveillance signals or outbreak detection.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 5 auditable classifier relationships to diseases, places, topics, and study design.