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COVID-19 modeling based on real geographic and population data.

Emir Baysazan1, Ahmet Nihat Berker2, Hasan Mandal3

  • 1TEBIP High Performers Program, Council of Higher Education, İstanbul University, İstanbul, Turkey.

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|March 22, 2023
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Summary

This study introduces a new network model to evaluate intercity travel's impact on the COVID-19 pandemic. The model accurately simulates disease spread and mortality in Türkiye, offering insights for epidemic control.

Keywords:
COVID-19Monte Carlo simulationepidemicgeographical modelsusceptible-infected-quarantine-recovered model

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Area of Science:

  • Epidemiology
  • Computational modeling
  • Network science

Background:

  • Intercity travel significantly influences pandemic dynamics.
  • The COVID-19 pandemic spurred research into intercity connections.
  • Evaluating epidemic spread requires robust computational models.

Purpose of the Study:

  • To assess the impact of intercity connections on epidemic spread during COVID-19.
  • To introduce and validate a novel network model for pandemic simulation.
  • To analyze disease transmission patterns based on geographic and population data.

Main Methods:

  • Development of a new network model incorporating geographic neighborhood and population density.
  • Application of the model to Turkish provincial data.
  • Utilizing a Monte Carlo algorithm with a hybrid lattice model on 8802 data points.

Main Results:

  • The model predicted a peak of 8.0% active cases in Türkiye around Monte Carlo step 70, followed by a second wave.
  • Epidemic spread initiated in İstanbul and rapidly expanded between steps 60-100.
  • Simulation results demonstrated a strong correlation with actual mortality data in Türkiye.

Conclusions:

  • The developed model is highly effective for quantitatively modeling real-world COVID-19 data.
  • The model accurately estimates disease spread, deaths, and epidemic termination.
  • Geographical intercity connections and population data are crucial for epidemic modeling.