Statistical analysis for "Climate Change and Human Activities Alter the Spillover Risk of Rodent-Borne Parasitic Intestinal Nematodes"

This is the dataset for the Manuscritp "Climate Change and Human Activities Alter the Spillover Risk of Rodent-Borne Parasitic Intestinal Nematodes ". 

In Part I, we employed correlation analysis to investigate the causal relationships between climatic conditions, human activities, population parameters, and the epidemic dynamics of rodent-borne intestinal nematodes, and we constructed machine learning model to predict infection intensity

In Part II, we employed the McKendrick-von Foerster partial differential equation to investigate population dynamics and utilized the Time-Series Susceptible-Infected-Recovered (TSIR) model to simulate the epidemic dynamics of the parasite.

In Part III, we modeled the species distribution of 58 rodent species in Chinese grassland ecosystems under both current and future climate scenarios. We used species richness within each geographical grid as the initial population density. Through mathematical models, we simulated the host population dynamics and epidemic dynamics of parasite, and the parameters in these models were determined by the land-use type. We then used the machine-learning model in Part I to predict infection intensity based on these data, and ultimately integrated human population density to estimate the spillover risk of rodent-borne intestinal nematodes in each grid.