A Hybrid Model for Disease Spread and an Application to the SARS Pandemic

TL;DR

This paper presents a hybrid model combining global traffic patterns and local SEIR dynamics to better simulate pandemic spread, validated through SARS data, showing improved accuracy over traditional models.

Contribution

Introduces a novel hybrid pandemic model integrating global and local factors, enhancing the realism and predictive power of disease spread simulations.

Findings

Global traffic significantly influences pandemic spread

Hybrid model outperforms traditional models in accuracy

Incorporating local density improves local spread predictions

Abstract

Pandemics can cause immense disruption and damage to communities and societies. Thus far, modeling of pandemics has focused on either large-scale difference equation models like the SIR and the SEIR models, or detailed micro-level simulations, which are harder to apply at a global scale. This paper introduces a hybrid model for pandemics considering both global and local spread of infections. We hypothesize that the spread of an infectious disease between regions is significantly influenced by global traffic patterns and the spread within a region is influenced by local conditions. Thus we model the spread of pandemics considering the connections between regions for the global spread of infection and population density based on the SEIR model for the local spread of infection. We validate our hybrid model by carrying out a simulation study for the spread of SARS pandemic of 2002-2003 using available data on population, population density, and traffic networks between different regions. While it is well-known that international relationships and global traffic patterns significantly influence the spread of pandemics, our results show that integrating these factors into relatively simple models can greatly improve the results of modeling disease spread.