An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong

Salihu S. Musa; Shi Zhao; Winnie Mkandawire; Andrés Colubri; Daihai He; Salihu S. Musa; Shi Zhao; Winnie Mkandawire; Andrés Colubri; Daihai He

doi:10.3934/mbe.2024327

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 10: 7435-7453. doi: 10.3934/mbe.2024327

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An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong

1.
Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, 01605, USA
2.
Department of Mathematics, University of Maryland, College Park, Maryland, 20742, USA
3.
Institute for Health Computing, University of Maryland, North Bethesda, Maryland, 20852, USA
4.
School of Public Health, Tianjin Medical University, Tianjin, 300070, China
5.
Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
6.
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China

Received: 03 March 2024 Revised: 28 August 2024 Accepted: 28 August 2024 Published: 28 October 2024

Identifying epidemic-driving factors through epidemiological modeling is a crucial public health strategy that has substantial policy implications for control and prevention initiatives. In this study, we employ dynamic modeling to investigate the transmission dynamics of pneumonic plague epidemics in Hong Kong from 1902 to 1904. Through the integration of human, flea, and rodent populations, we analyze the long-term changing trends and identify the epidemic-driving factors that influence pneumonic plague outbreaks. We examine the dynamics of the model and derive epidemic metrics, such as reproduction numbers, that are used to assess the effectiveness of intervention. By fitting our model to historical pneumonic plague data, we accurately capture the incidence curves observed during the epidemic periods, which reveals some crucial insights into the dynamics of pneumonic plague transmission by identifying the epidemic driving factors and quantities such as the lifespan of flea vectors, the rate of rodent spread, as well as demographic parameters. We emphasize that effective control measures must be prioritized for the elimination of fleas and rodent vectors to mitigate future plague outbreaks. These findings underscore the significance of proactive intervention strategies in managing infectious diseases and informing public health policies.
- plague,
- epidemiological modeling,
- reproduction number,
- epidemic
Citation: Salihu S. Musa, Shi Zhao, Winnie Mkandawire, Andrés Colubri, Daihai He. An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong[J]. Mathematical Biosciences and Engineering, 2024, 21(10): 7435-7453. doi: 10.3934/mbe.2024327

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Abstract

Identifying epidemic-driving factors through epidemiological modeling is a crucial public health strategy that has substantial policy implications for control and prevention initiatives. In this study, we employ dynamic modeling to investigate the transmission dynamics of pneumonic plague epidemics in Hong Kong from 1902 to 1904. Through the integration of human, flea, and rodent populations, we analyze the long-term changing trends and identify the epidemic-driving factors that influence pneumonic plague outbreaks. We examine the dynamics of the model and derive epidemic metrics, such as reproduction numbers, that are used to assess the effectiveness of intervention. By fitting our model to historical pneumonic plague data, we accurately capture the incidence curves observed during the epidemic periods, which reveals some crucial insights into the dynamics of pneumonic plague transmission by identifying the epidemic driving factors and quantities such as the lifespan of flea vectors, the rate of rodent spread, as well as demographic parameters. We emphasize that effective control measures must be prioritized for the elimination of fleas and rodent vectors to mitigate future plague outbreaks. These findings underscore the significance of proactive intervention strategies in managing infectious diseases and informing public health policies.

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