Resumen
Dysentery is still a serious global public health problem. In Chongqing, China, there were 37,140 reported cases of dysentery from 2015 to 2021. However, previous research has relied on statistical data of dysentery incidence rate data based on administrative regions, while grained scale products are lacking. Thus, an initialized gradient-boosted decision trees (IGBDT) hybrid machine learning model was constructed to fill this gap in grained scale products. Socioeconomic factors, meteorological factors, topographic factors, and air quality factors were used as inputs of the IGBDT to map the statistical dysentery incidence rate data of Chongqing, China, from 2015 to 2021 on the grid scale. Then, dysentery incidence rate grained scale products (1 km) were generated. The products were evaluated using the total incidence of Chongqing and its districts, with resulting R2 values of 0.7369 and 0.5439, indicating the suitable prediction performance of the model. The importance and correlation of factors related to the dysentery incidence rate were investigated. The results showed that socioeconomic factors had the main impact (43.32%) on the dysentery incidence rate, followed by meteorological factors (33.47%). The Nighttime light, normalized difference vegetation index, and maximum temperature showed negative correlations, while the population, minimum and mean temperature, precipitation, and relative humidity showed positive correlations. The impacts of topographic factors and air quality factors were relatively weak.