A stochastic framework for predicting epidemiological risk areas using the Ornstein-Uhlenbeck process: Software and supplementary material

Understanding the spatiotemporal distribution of infection risk is fundamental in the epidemiology of infectious diseases, as it allows for the identification of key parameters influencing disease transmission. Insights into the spatiotemporal drivers of epidemic dynamics are essential for developing improved strategies for disease prevention. This study introduces a predictive framework based on the Ornstein-Uhlenbeck stochastic process to estimate the spatial and temporal distribution of infectiousness originating from a primary case. The proposed model captures the dynamics of secondary infections and their impact on spatial dispersion, primarily driven by a diffusion mechanism of the Chapman type. This diffusion mechanism induces the phenomenon of segregation by incorporating behavioral or cognitive aspects of susceptible individuals. We calculate critical epidemiological metrics, including the basic reproduction number, the probability density function of generation time, and the m..., All the data used to obtain the results are uploaded. The scripts were developed in Python language. Figures generated from the scripts in Python.
A zip file with all the scripts is also included., , # A stochastic framework for predicting epidemiological risk areas using the Ornstein-Uhlenbeck process: Software and supplementary material

[https://doi.org/10.5061/dryad.hmgqnk9rr](https://doi.org/10.5061/dryad.hmgqnk9rr)

## Description of the data and file structure

The scripts that generate the figures in the article are described in Phyton. Each case and variables are described in the related executable code (see Code/Software below).

## Sharing/Access information

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## Code/Software

The executables are in Python language and described as follows:

1. Run the file figure1_article.py to generate the results of Figure 1 (see figure \"aedes.pdf\" *).
2. Run the file figure2_article.py to generate the results of Figure 2 (see \"mosquitos 1.pdf\" *).
3. Run the file figure3_article.py to generate the results of Figure 3 (see \"mosquitos 2.pdf\" *).
4. Run the file figure4_article.py to generate the results of Figure 4 (see \"mosquitos 3.pdf\" *).
5. Run the file R_0.py to gener...