Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis
BackgroundOn September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical mo...
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1175869/full |
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| author | Yifei Ma Shujun Xu Yuxin Luo Yao Qin Jiantao Li Lijian Lei Lu He Tong Wang Hongmei Yu Hongmei Yu Jun Xie |
| author_facet | Yifei Ma Shujun Xu Yuxin Luo Yao Qin Jiantao Li Lijian Lei Lu He Tong Wang Hongmei Yu Hongmei Yu Jun Xie |
| author_sort | Yifei Ma |
| collection | DOAJ |
| description | BackgroundOn September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot.MethodsIn this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis.ResultsOf the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30–59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population.ConclusionOur model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus. |
| first_indexed | 2024-03-13T04:04:46Z |
| format | Article |
| id | doaj.art-eb9bd1451f044534bc008cf32472380a |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-13T04:04:46Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj.art-eb9bd1451f044534bc008cf32472380a2023-06-21T09:34:38ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-06-011110.3389/fpubh.2023.11758691175869Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysisYifei Ma0Shujun Xu1Yuxin Luo2Yao Qin3Jiantao Li4Lijian Lei5Lu He6Tong Wang7Hongmei Yu8Hongmei Yu9Jun Xie10School of Public Health, Shanxi Medical University, Taiyuan, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, ChinaSchool of Management, Shanxi Medical University, Taiyuan, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, ChinaSchool of Public Health, Shanxi Medical University, Taiyuan, ChinaShanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, ChinaCenter of Reverse Microbial Etiology, Shanxi Medical University, Taiyuan, ChinaBackgroundOn September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot.MethodsIn this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis.ResultsOf the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30–59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population.ConclusionOur model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1175869/fullCOVID-19epidemiological characteristicstransmission dynamicstime-varying SQEIAHR modeleffective reproduction numberhigher stringency measures |
| spellingShingle | Yifei Ma Shujun Xu Yuxin Luo Yao Qin Jiantao Li Lijian Lei Lu He Tong Wang Hongmei Yu Hongmei Yu Jun Xie Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis Frontiers in Public Health COVID-19 epidemiological characteristics transmission dynamics time-varying SQEIAHR model effective reproduction number higher stringency measures |
| title | Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis |
| title_full | Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis |
| title_fullStr | Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis |
| title_full_unstemmed | Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis |
| title_short | Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis |
| title_sort | epidemiological characteristics and transmission dynamics of the covid 19 outbreak in hohhot china a time varying sqeiahr model analysis |
| topic | COVID-19 epidemiological characteristics transmission dynamics time-varying SQEIAHR model effective reproduction number higher stringency measures |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1175869/full |
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