Stochastic Differential Equations in Global Epidemiology: Modeling Pathogen Spread via Climate Vectors
Authors
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Daniel Nelson
Oceanic Study Center
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Elia Cooper
Quantum Solutions Ltd.
https://orcid.org/0009-0000-0000-0000
Abstract
The interconnectedness of global travel and accelerating climate change has fundamentally altered the spread of infectious diseases. This paper moves beyond traditional SIR/SEIR models by introducing a framework based on stochastic differential equations (SDEs) to model the global dispersion of vector-borne pathogens. Our 'Math Earth' approach integrates large-scale climate datasets (temperature and precipitation anomalies) with global air traffic network data. This allows us to model the changing habitat suitability for vectors like the *Aedes aegypti* mosquito and predict new regions at risk for outbreaks of diseases such as dengue and Zika. The stochastic component of the model effectively captures the inherent randomness of transmission events and climate fluctuations. The findings provide a probabilistic risk map, highlighting emerging hotspots and demonstrating the power of advanced mathematical modeling in global public health preparedness and climate-resilient disease control strategies.
Copyright (c) 2026 Daniel Nelson, Elia Cooper (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.