Assessment of Model Estimated and Directly Observed Weather Data for Etiological Prediction of Diarrhea.
Assessment of Model Estimated and Directly Observed Weather Data for Etiological Prediction of Diarrhea.
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评估用于腹泻病因预测的模型估计和直接观测的天气数据。
DOI：
10.1101/2023.10.12.23296959
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Leung,DanielT
中科院分区：
文献类型：
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作者：
Brintz,BenJ;Colston,JoshM;Ahmed,ShariaM;Chao,DennisL;Zaitschik,Ben;Leung,DanielT
低收入和中等收入国家腹泻病因临床预测的最新进展表明，天气数据的加入提高了预测效果。然而，天气数据的最佳来源仍不清楚。我们的目的是比较模型估计的卫星和地面观测数据与气象站直接观测数据的腹泻预测。我们使用了一项大型多中心儿童腹泻研究的临床和病因学数据来比较这些方法。我们表明，在大多数地区，这两种天气条件的来源表现相似。我们的结论是，虽然模型估计数据是一种可行的、可扩展的公共卫生干预和疾病预测工具，但直接观测到的气象站数据与模型数据接近，并且易于获取，可能足以预测中低收入国家儿童的腹泻病因。
Recent advances in clinical prediction for diarrheal etiology in low- and middle-income countries have revealed that addition of weather data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare model estimated satellite- and ground-based observational data with weather station directly-observed data for diarrheal prediction. We used clinical and etiological data from a large multi-center study of children with diarrhea to compare these methods. 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, directly observed weather station data approximates the modeled data, and given its ease of access, is likely adequate for prediction of diarrheal etiology in children in low- and middle-income countries.