COVID-19 Modeling Based on Real Geographic and Population Data

TL;DR

This paper introduces a new network model using real geographic and population data to simulate COVID-19 spread between cities, demonstrating high efficiency in predicting epidemic dynamics in Turkey.

Contribution

The study presents a novel model incorporating actual geographic and population data for more accurate COVID-19 epidemic simulation.

Findings

Model accurately estimates COVID-19 deaths and spread

High efficiency in real-world epidemic data modeling

Effective in predicting epidemic termination

Abstract

Intercity travel is one of the most important parameters for combating a pandemic. The ongoing COVID-19 pandemic has resulted in different computational studies involving intercity connections. In this study, the effects of intercity connections during an epidemic such as COVID-19 are evaluated using a new network model. This model considers the actual geographic neighborhood and population density data. This new model is applied to actual Turkish data by the means of provincial connections and populations. A Monte Carlo algorithm with a hybrid lattice model is applied to a lattice with 8,802 data points. Results show that this model is quantitatively very efficient in modeling real world COVID-19 epidemic data based on populations and geographical intercity connections, by the means of estimating the number of deaths, disease spread, and epidemic termination.