<scp>GIS</scp> ‐Based Platform for Near Real‐Time Prediction of Mosquito Distribution

ABSTRACT Mosquitoes are a major public health threat as carriers of numerous pathogens, making it essential to understand their distribution to prevent and control the spread of vector‐borne diseases effectively. This paper presents a platform that incorporates dynamic datasets from online sources with static data from reliable open repositories to predict and visualize mosquito distribution, particularly in low‐resource settings where traditional trapping methods may be unavailable. The dynamic datasets contain near real‐time weather data from the Open Weather API, while the static datasets mainly cover other key factors such as elevation, vegetation, and built settlements. The platform employs a PostgreSQL database to manage these datasets and leverages the MaxEnt model within ArcGIS Pro for near real‐time predictions of mosquito prevalence. Further, an automated Python‐scripted workflow is proposed to seamlessly transfer data between the PostgreSQL database and MaxEnt. The outcomes are shared through an ArcGIS Online dashboard, providing stakeholders with an interactive tool to monitor mosquito occurrences. This innovative platform offers a dynamic and effective solution for public health officials to control the spread of vector‐borne diseases.