Spatiotemporal infectious disease modeling: a BME-SIR approach

Area of Science:

• Epidemiology
• Mathematical Modeling
• Computational Science

Background:

• Infectious disease spread is complex, influenced by population dynamics and inherent uncertainties.
• Accurate modeling is crucial for effective public health interventions and resource allocation.

Purpose of the Study:

• To develop a stochastic modeling framework for infectious disease spread in composite space-time domains.
• To account for multi-sourced in situ uncertainties and population migration dynamics.
• To enable real-time prediction of disease spread and associated parameters.

Main Methods:

• Stochastic modeling of disease transmission and recovery rates.
• Functional formulation of susceptible-infected-recovered (SIR) individual fractions.
• Integration of population dynamics, observation time series, and diverse data sources (hard/soft data, empirical relationships).

Main Results:

• The model effectively simulates population dynamics within and across localities.
• It successfully integrates disease representation with real-world data, including geographical and secondary information.
• Demonstrated capability for real-time prediction of disease spread under uncertainty.

Conclusions:

• The proposed stochastic approach provides a robust framework for modeling infectious disease spread.
• It enhances predictive accuracy by incorporating heterogeneous data and accounting for uncertainties.
• Validated through simulations and a real-world application using hand-foot-mouth disease (HFMD) data.