Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model
Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-03-01
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| Series: | Infectious Disease Modelling |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042721000907 |
| _version_ | 1797203413930344448 |
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| author | Shanshan Feng Xiao-Feng Luo Xin Pei Zhen Jin Mark Lewis Hao Wang |
| author_facet | Shanshan Feng Xiao-Feng Luo Xin Pei Zhen Jin Mark Lewis Hao Wang |
| author_sort | Shanshan Feng |
| collection | DOAJ |
| description | Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%. |
| first_indexed | 2024-04-24T08:18:57Z |
| format | Article |
| id | doaj.art-247a1b1a37574769b6697aca68ccc370 |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-24T08:18:57Z |
| publishDate | 2022-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-247a1b1a37574769b6697aca68ccc3702024-04-17T02:00:48ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272022-03-0171212230Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network modelShanshan Feng0Xiao-Feng Luo1Xin Pei2Zhen Jin3Mark Lewis4Hao Wang5Department of Mathematics, North University of China, Taiyuan, Shanxi, 030 051, ChinaDepartment of Mathematics, North University of China, Taiyuan, Shanxi, 030 051, ChinaCollege of Mathematics, Taiyuan University of Technology, Shanxi, Taiyuan, 030 024, ChinaComplex System Research Center, Shanxi University, Taiyuan, 030 006, Shanxi, China; Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan, 030 006, Shanxi, China; Corresponding author. Complex System Research Center, Shanxi University, Taiyuan, 030 006, Shanxi, China.Department of Mathematics and Statistics Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1, Canada; Department of Biological Sciences, University of Alberta, Edmonton, Alberta, T6G 2E9, CanadaDepartment of Mathematics and Statistics Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1, Canada; Corresponding author.Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%.http://www.sciencedirect.com/science/article/pii/S2468042721000907COVID-19Social networkQuarantineSocial distanceClustering coefficient |
| spellingShingle | Shanshan Feng Xiao-Feng Luo Xin Pei Zhen Jin Mark Lewis Hao Wang Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model Infectious Disease Modelling COVID-19 Social network Quarantine Social distance Clustering coefficient |
| title | Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model |
| title_full | Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model |
| title_fullStr | Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model |
| title_full_unstemmed | Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model |
| title_short | Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model |
| title_sort | modeling the early transmission of covid 19 in new york and san francisco using a pairwise network model |
| topic | COVID-19 Social network Quarantine Social distance Clustering coefficient |
| url | http://www.sciencedirect.com/science/article/pii/S2468042721000907 |
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