An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong
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...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-10-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024327 |
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| author | Salihu S. Musa Shi Zhao Winnie Mkandawire Andrés Colubri Daihai He |
| author_facet | Salihu S. Musa Shi Zhao Winnie Mkandawire Andrés Colubri Daihai He |
| author_sort | Salihu S. Musa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-68c9b23a112747e8962b238273d5e6a8 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-68c9b23a112747e8962b238273d5e6a82025-01-23T07:48:01ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-10-0121107435745310.3934/mbe.2024327An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong KongSalihu S. Musa0Shi Zhao1Winnie Mkandawire2Andrés Colubri3Daihai He4Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, 01605, USASchool of Public Health, Tianjin Medical University, Tianjin, 300070, ChinaDepartment of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, 01605, USADepartment of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, 01605, USADepartment of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, ChinaIdentifying 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.https://www.aimspress.com/article/doi/10.3934/mbe.2024327plagueepidemiological modelingreproduction numberepidemic |
| spellingShingle | 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 Mathematical Biosciences and Engineering plague epidemiological modeling reproduction number epidemic |
| title | An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong |
| title_full | An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong |
| title_fullStr | An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong |
| title_full_unstemmed | An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong |
| title_short | An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong |
| title_sort | epidemiological modeling investigation of the long term changing dynamics of the plague epidemics in hong kong |
| topic | plague epidemiological modeling reproduction number epidemic |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024327 |
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