Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights

David Lyver; Mihai Nica; Corentin Cot; Giacomo Cacciapaglia; Zahra Mohammadi; Edward W. Thommes; Monica-Gabriela Cojocaru; David Lyver; Mihai Nica; Corentin Cot; Giacomo Cacciapaglia; Zahra Mohammadi; Edward W. Thommes; Monica-Gabriela Cojocaru

doi:10.3934/mbe.2024247

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 4: 5604-5633. doi: 10.3934/mbe.2024247

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Research article

Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights

1.
Department of Mathematics, University of Guelph, Guelph ON N1G 2W1, Canada
2.
Laboratoire de Physique des 2 Infinis Irène Joliot Curie (UMR 9012), CNRS/IN2P3, Orsay 91400, France
3.
Institut de Physique des 2 Infinis de Lyon (UMR 5822), CNRS/IN2P3 et Université Claude Bernard Lyon 1, Villeurbanne 69622, France
4.
Sanofi, North York ON M2R 3T4, Canada

Academic Editor: Yang Kuang

Received: 22 November 2023 Revised: 20 February 2024 Accepted: 22 February 2024 Published: 16 April 2024

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so-called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use USA county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.
- clustering,
- machine learning,
- SARS-CoV-2,
- epidemiology
Citation: David Lyver, Mihai Nica, Corentin Cot, Giacomo Cacciapaglia, Zahra Mohammadi, Edward W. Thommes, Monica-Gabriela Cojocaru. Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights[J]. Mathematical Biosciences and Engineering, 2024, 21(4): 5604-5633. doi: 10.3934/mbe.2024247

Related Papers:

Abstract

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so-called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use USA county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.

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