Fig 1.
Map of the study area with high population concentration within Guangdong, China.
Base map from Resource and Environment Science and Data Center (https://doi.org/10.12078/2023120601). Created by author, licensed under CC BY 4.0.
Table 1.
Description of meteorological factors and daily number of cases of three intestinal infectious diseases in Shenzhen, China.
Fig 2.
Temperature-dependent risk profiles of predominant intestinal infectious diseases across population subgroups.
Each panel displays temperature-response relationships, with relative risk (RR) on the Y-axes. Solid curves represent mean risk estimates, bounded by 95% confidence intervals (shaded regions).
Fig 3.
Exposure-dependent risk profiles of predominant intestinal infectious diseases across population subgroups.
Each panel displays exposure-response relationships, with relative risk (RR) on the Y-axes. Solid curves represent mean risk estimates, bounded by 95% confidence intervals (shaded regions).
Fig 4.
Cumulative risk of predominant intestinal infectious diseases in warm and cold seasons.
SS represents spring and summer, AW represents autumn and winter; Tm stands for temperature (°C) and the Z-axis represents cumulative risk.
Fig 5.
Prediction of case numbers across different age and gender groups under varying temperature and humidity conditions.
Um represents humidity (%), Tm represents temperature (°C), and log represents the base ten logarithm of the number of cases.
Table 2.
Evaluation of the random forest model early warning system in 2012-2018.
Fig 6.
Prediction results of the early warning system.