ArticleEpidemiology and infection2024
Assessment and comparison of model estimated and directly observed weather data for prediction of diarrhoea aetiology.
Article in Epidemiology and infection, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Derivation and validation of clinical prediction models for viral etiologies of acute diarrhea in North American children presenting for emergency care.medRxiv : the preprint server for health sciences · 2026Article
- Association between Weather Variables and Viral Gastroenteritis in the United States.medRxiv : the preprint server for health sciences · 2026Article
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6 authors.
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Abstract
Recent advances in clinical prediction for diarrhoeal aetiology in low- and middle-income countries have revealed that the addition of weather data to clinical data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare the use of model estimated satellite- and ground-based observational data with weather station directly observed data for the prediction of aetiology of diarrhoea. We used clinical and etiological data from a large multi-centre study of children with moderate to severe diarrhoea cases to compare their predictive performances. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, given its ease of access, directly observed weather station data is likely adequate for the prediction of diarrhoeal aetiology in children in low- and middle-income countries.
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