Hemorrhagic fever with renal syndrome (HFRS) poses a significant public health threat in China, yet its endemic distribution remains poorly defined. In this study, we collected HFRS cases and environmental factors from 2014 to 2018 in China and developed a two-stage ecological machine learning model to investigate the drivers, environment suitability, and potential risk areas of HFRS. Finally, we identified three endemic types, with Type I (HTNV-type dominant) accounting for approximately 16.96% of the total potential risk areas (468,000/2,759,000 square kilometers). Meteorological factors, crop fields, grassland, wetland, rural residential land, and normalized difference vegetation index were the main drivers in these endemic areas. Type II (SEOV-type dominant) risk areas, on the other hand, accounted for around 718,100 square kilometers (26.03%), with HFRS epidemics being primarily associated with the percentage of grassland, wetland, open woodland, rural residential land, urban construction rate, and meteorological factors. Type III(Mixed-type) endemic foci spanned large potential risk areas throughout mainland China, covering approximately 1,572,900 square kilometers (57.01% of the total potential endemic areas). Three HFRS-endemic areas in China varied in epidemic features, ecological drivers, and spatial risk areas. Targeted surveillance and intervention strategies were needed for different endemic areas to control the spread of HFRS.

Varicella is a highly prevalent infectious disease with a similar transmission pathway to coronavirus disease 2019 (COVID-19). In the context of the COVID-19 pandemic, anti-COVID-19 non-pharmaceutical interventions (NPIs) have been implemented to prevent the spread of the infection. This study aims to analyze varicella’s epidemiological characteristics and further investigate the effect of anti-COVID-19 NPIs on varicella in Xi’an, northwestern China. Based on the varicella surveillance data, search engine indices, meteorological factors from 2011 to 2021 in Xi’an, and different levels of emergency response to COVID-19 during the pandemic, we applied Bayesian Structural Time Series models and interrupted time series analysis to predict the counterfactual incidence of varicella and quantify the impact of varying NPIs intensities on varicella. From 2011 to 2021, varicella incidence increased, especially in 2019, with a high incidence of 111.69/100,000. However, there was a sharp decrease of 43.18% in 2020 compared with 2019, and the peak of varicella incidence in 2020 was lower than in previous years from the 21st to the 25th week. In 2021, the seasonality of varicella incidence gradually returned to a seasonal pattern in 2011-2019. The results suggest that anti-COVID-19 NPIs effectively reduce the incidence of varicella, and this reduction has spatiotemporal heterogeneity.