Background Diarrhea disease is a common intestinal infectious disease, and its incidence is affected by meteorological conditions. A better understanding of its epidemiological patterns and influencing factors, together with the construction of reliable prediction models, is of great significance for precise public health prevention and control. Objective To clarify the epidemic characteristics of adult diarrhea disease in Shanghai, analyze the associations of meteorological factors and a web search index with adult diarrhea disease, and develop and compare forecasting models to support precise regional prevention and control. Methods Weekly surveillance data of adult diarrhea disease cases from the Shanghai Comprehensive Surveillance Information System for Diarrhea Diseases, together with concurrent meteorological observation data and web search index (Baidu index) data from 2014 to 2019, were collected. A distributed lag non-linear model (DLNM) was adopted to analyze the associations of multiple meteorological factors and the web search index with the number of diarrhea disease cases. By integrating meteorological factors and web search index data, three types of forecasting models were developed, including autoregressive integrated moving average (ARIMA), Random Forest, and extreme gradient boosting (Xgboost), and their predictive performances were evaluated. Results Adult diarrhea disease in Shanghai exhibited seasonal variation, with an major incidence peak in summer and winter peaks in some years. The number of cases declined annually after 2015. Mean temperature was significantly associated with the risk of diarrhea disease, and both low and high temperature exposures were associated with increased risks. The highest risk was observed at 32.8°C (RR=2.04, 95%CI: 1.62, 2.55), while the strongest effect of low temperature was observed at 0.9 °C (RR=1.53, 95%CI: 1.25, 1.88). When relative humidity exceeded 69%, the risk of diarrhea disease increased with relative humidity, reaching a peak at 81% (RR=1.20, 95%CI: 1.07, 1.35). When weekly cumulative precipitation exceeded 16 mm, the risk also increased with increasing precipitation, reaching a maximum at 105 mm (RR=1.23, 95%CI: 1.07, 1.42). The web search index was positively associated with the risk of diarrhea disease. Model prediction indicated that both the Random Forest model and the Xgboost model adequately captured the overall trend in diarrhea disease cases, with R² values generally exceeding 0.7. Notably, the Xgboost model demonstrated greater accuracy in capturing peak intensities. Conclusion Meteorological factors are associated with adult diarrhea disease in Shanghai. The web search index may serve as an auxiliary indicator for diarrhea forecasting. Machine learning models, with advantages in integrating multisource data, may provide effective predictive tools for the prevention and control of diarrhea disease.

ObjectiveTo explore the synergetic effect of temperature and PM_2.5 on cardiovascular and cerebrovascular diseases. MethodWe collected cardiovascular and cerebrovascular death cases，air pollution and meteorological data during the same period in Pudong New Area from 2013 to 2018.We used generalized additive models （GAMs） with poisson regression including non-stratification model， nonparametric bivariate response model and pollution-stratified parametric model， to assess the interaction between temperature and PM_2.5 and on the number of cardiovascular cerebrovascular and cerebrovascular disease deaths. ResultsThe exposure-response relationship between temperature and the number of cardiovascular and cerebrovascular deaths exhibited "U" type and the most comfortable temperature was 18.9 ℃. When the concentrations of PM_2.5 increased by 10 μg·m^-3， the deaths of total， male， female， ≤75 years and >75 years increased， respectively， by 0.60%（95%CI： 0.30%‒0.91%）， 0.77%（95%CI：0.34%‒1.20%）， 0.46%（95%CI：0.05%‒0.86%）， 0.66%（95%CI：0.03%‒1.30%） and 0.59%（95%CI：0.26%‒0.92%）. With the increase of PM_2.5 concentration level， the impact of temperature on cardiovascular and cerebrovascular diseases gradually increased， and the impact was the most significant when the concentration of PM_2.5 was more than 150 µg·m^-3. There were different sensitive people in different seasons. ConclusionPM_2.5 concentration levels of mild pollution and above can exacerbate the negative effects of temperature on cardiovascular and cerebrovascular diseases.