what are the underlying transmission patterns of covid 19 outbreak an age specific CORD-Papers-2022-06-02 (Version 1)

Title: What are the Underlying Transmission Patterns of COVID-19 Outbreak? An Age-specific Social Contact Characterization
Abstract: Abstract Background COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries. Methods In this work we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0-6 years old (children); 7-14 (primary and junior high school students); 15-17 (high school students); 18-22 (university students); 23-44 (young/middle-aged people); 45-64 years old (middle-aged/elderly people); and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households; (2) schools including primary/high schools as well as colleges and universities; (3) various physical workplaces; and (4) public places and communities where people can gather such as stadiums markets squares and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups for each of the four settings. By integrating the four contact matrices with the next-generation matrix we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations. Findings We focus our study on 6 representative cities in China: Wuhan the epicenter of COVID-19 together with Beijing Tianjin Hangzhou Suzhou and Shenzhen which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan the age-groups involving relatively intensive contacts in households and public/communities are dispersedly distributed. This can explain why the transmission of COVID-19 in the early stage mainly took place in public places and families in Wuhan. We estimate that Feb. 11 2020 was the date with the highest transmission risk in Wuhan which is consistent with the actual peak period of the reported case number (Feb. 4-14). Moreover the surge in the number of new cases reported on Feb. 12-13 in Wuhan can readily be captured using our model showing its ability in forecasting the potential/unconfirmed cases. We further estimate the disease transmission risks associated with different work resumption plans in these cities after the outbreak. The estimation results are consistent with the actual situations in the cities with relatively lenient control policies such as Beijing and those with strict control policies such as Shenzhen. Interpretation With an in-depth characterization of age-specific social contact-based transmission the retrospective and prospective situations of the disease outbreak including the past and future transmission risks the effectiveness of different interventions and the disease transmission risks of restoring normal social activities are computationally analyzed and reasonably explained. The conclusions drawn from the study not only provide a comprehensive explanation of the underlying COVID-19 transmission patterns in China but more importantly offer the social contact-based risk analysis methods that can readily be applied to guide intervention planning and operational responses in other countries so that the impact of COVID-19 pandemic can be strategically mitigated. Funding General Research Fund of the Hong Kong Research Grants Council; Key Project Grants of the National Science Foundation of China.
Published: 2020-04-18
Journal: EClinicalMedicine
DOI: 10.1016/j.eclinm.2020.100354
DOI_URL: http://doi.org/10.1016/j.eclinm.2020.100354
Author Name: Liu Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Gu Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gu_z
Author Name: Xia S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/xia_s
Author Name: Shi B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/shi_b
Author Name: Zhou X N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhou_x_n
Author Name: Shi Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/shi_y
Author Name: Liu J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_j
sha: ee90f05aa5a937db108c1c8f8e033e67ed2b4ef9
license: els-covid
license_url: https://www.elsevier.com/about/policies/open-access-licenses/elsevier-user-license
source_x: Elsevier; Medline; PMC
source_x_url: https://www.elsevier.com/https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 32313879
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/32313879
pmcid: PMC7165295
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7165295
url: https://www.ncbi.nlm.nih.gov/pubmed/32313879/ https://www.sciencedirect.com/science/article/pii/S2589537020300985?v=s5 https://api.elsevier.com/content/article/pii/S2589537020300985 https://doi.org/10.1016/j.eclinm.2020.100354
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Keywords Extracted from Text Content: COVID-19 people human P ij C people public/communities brown line b ii C H C P denote the 7 H S W P r r r r  C S Figure 1 Figure S1 1/7.5 individuals Figures 3(B Wuhan Shenzhen's G i G j ij ij C C C matrix A COVID-19 Plans B1-B3 matrix B B19 r 7 Figure 2 contact matrix C. Hubei ... t-th coronavirus C P B 1/min{r 1 Shenzhen. r i to 1 patients r i GDP C W S t matrix K t r S P i Shenzhen I t blue line K t are 7×7 b ii = r i /min{r 1 Let G 1 -G 7 matrix children her
Extracted Text Content in Record: First 5000 Characters:Background COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries. Methods In this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's 4 effective tools and insights to other countries or regions for their intervention planning and operational responses. 1 2 population into seven age-groups: 0-6 years old (children); 7-14 (primary and junior high school students); 15-17 (high school students); 18-22 (university students); 23-44 (young/middle-aged people); 45-64 years old (middle-aged/elderly people); and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households; (2) schools, including primary/high schools as well as colleges and universities; (3) various physical workplaces; and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups for each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations. Findings We focus our study on 6 representative cities in China: Wuhan, the epicenter of COVID-19, together with Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedly distributed. This can explain why the transmission of COVID-19 in the early stage mainly took place in public places and families in Wuhan. We estimate that Feb. 11, 2020 was the date with the highest transmission risk in Wuhan, which is consistent with the actual peak period of the reported case number (Feb. [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] . Moreover, the surge in the number of new cases reported on Feb. 12-13 in Wuhan can readily be captured using our model, showing its ability in forecasting the potential/unconfirmed cases. We further estimate the disease transmission risks associated with different work resumption plans in these cities after the outbreak. The estimation results are consistent with the actual situations in the cities with relatively lenient control policies, such as Beijing, and those with strict control policies, such as Shenzhen. Interpretation With an in-depth characterization of age-specific social contact-based transmission, the retrospective and prospective situations of the disease outbreak, including the past and future transmission risks, the effectiveness of different interventions, and the disease transmission risks of restoring normal social activities, are computationally analyzed and reasonably explained. The conclusions drawn from the study not only provide a comprehensive explanation of the underlying COVID-19 transmission patterns in China, but more importantly, offer the social contact-based risk analysis methods that can readily be applied to guide intervention planning and operational responses in other countries, so that the impact of COVID-19 pandemic can be strategically mitigated. To our knowledge, this is the first work that explicitly characterizes and quantifies the underlying transmission patterns among different populations throughout different phases of the COVID-19 outbreak. We show that the age-specific social contact patterns can accurately characterize the interactions among different groups of people, and thus provide explanations on the underlying disease transmission and associated risks in different phases of the outbreak. We analyze the situations in 6 representative cities in China. These cities are Wuhan, the epicenter of the outbreak, and Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, five cities situated in three major economic zones. This work has touched upon an important problem at a critical moment in time: COVID-19 has spread to 180 countries in 6 continents, and a deeper understanding of what may have happened in the outbreak is now overdue. Addressing this key question enables us to gain insights into the retrospective and prospective situations of the disease outbreak; this in turn will help further answer a series of questions in precision control and prevention of the disease; namely, how future risks and trends in different regions
Keywords Extracted from PMC Text: patients cijP Year-over-Year GDP friends Gi Gj GDP Shenzhen [19] CijW Figure S1 rHCH+rSCS+rWCW+rPCP S. [21]:(3)C Shenzhen's G5 Figure 4 X.-N. cijS × 1 cijH b 1/min{r 1 Varicella B are diagonal matrices Figures 3(B-F G5 (23-44 ... Z. contact matrix rP. Figure 2 thatrH+rS+rW+rP=1 Shenzhen. 23:59 B Wuhan, Apr. 1 public/communities 's Plans B1-B3 Figures 4(A CijH Parvovirus B19 [23 Wuhan [14] CijPPiPj People contact matrix C. Zhejiang Province C Y. Shi [1] × children human coronavirus J. Liu " matrix B Workplaces matrix K Hubei Province 4(B t-th CijS Shenzhen [7] matrix K COVID-19 Jing-Jin-Ji It+1=KtIt Figure 1 bii Wuhan cijW matrix A ri/min{r 1 people Gj (i, j = 1 Binti Hamzah CijP matrix
Extracted PMC Text Content in Record: First 5000 Characters:The novel coronavirus disease (COVID-19) has spread widely at the global level [1]. According to the statistics from the Johns Hopkins Coronavirus Resource Center, until Apr. 1, 2020, there have been more than 900,000 confirmed cases found in 180 countries covering 6 continents [2]. In view of the severity of the disease spread, the World Health Organization (WHO) has officially declared COVID-19 as a pandemic [3]. In order to timely mitigate the impact of COVID-19 pandemic, there is an urgent need to understand the underlying transmission patterns among different populations throughout different phases of the COVID-19 outbreak [4]. By doing so, we can provide insights into what may have happened retrospectively and what can be anticipated prospectively of the disease outbreak, so as to further address a series of important questions that follow, such as how future risks and trends in different regions may evolve, how effective can different mitigation strategies be in controlling the outbreak, and what may happen if people gradually return to schools and workplaces. More importantly, offering the methods of social contact-based risk analysis and demonstrating them in the case of COVID-19 outbreak in China can help other countries or regions in conducting similar studies and making subsequent intervention policies. We select 6 major cities in China for our study: Wuhan, Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen; their geographical locations and the disease situations (in terms of total case number from Dec. 2019 to Feb. 2020) are shown in Figure 1 . Wuhan was the epicenter of COVID-19 since Dec. 2019 [5], [6], [7]. The other 5 cities are representative in that they are situated in the three most important economic zones in China, which contribute more than 40% of the national GDP. Specifically, Beijing and Tianjin are representing the Jing-Jin-Ji (Beijing-Tianjin-Hebei) Metropolitan Region in Northern China. Hangzhou and Suzhou are the major players in the Yangtze River Delta City Cluster in Eastern China. Shenzhen is the flagship in the Greater Bay Area in Southern China. Another important reason to select these cities for our study is that the population of these cities contains a large number of migrant workers and college students from other cities or provinces. The frequent human mobility largely increases the risk of imported cases, posing great challenges to the control and prevention of COVID-19, especially when people are gradually returning to workplaces and schools in a later stage. The data used in our study include:1The daily confirmed cases from Dec. 8, 2019 to Feb. 29, 2020 in Wuhan, Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which were accessed and collected from the websites of the Health Commission of Hubei Province [8], the Beijing Municipal Health Commission [9], the Tianjin Municipal Health Commission [10], the Hangzhou Municipal Health Commission [11], the Suzhou Municipal Health Commission [12], and the Shenzhen Municipal Data Open Platform [13], respectively.2The demographic data of Wuhan [14], Beijing [15], Tianjin [16], Hangzhou [17], Suzhou [18], and Shenzhen [19]. The underlying transmission patterns of COVID-19 among different populations are difficult to characterize because they are complex and related to various observations and disease-related factors, including the number of confirmed cases, the potential risks brought by unconfirmed cases, the distribution of different case categories (indigenous/imported) in different regions/cities, the population distribution of different age-groups, the social contact patterns in different settings (e.g., households, schools, workplaces, and public places), the extent of interventions implemented in different regions/cities, etc.. To address this challenging issue in a fundamental way, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people [20,21]. Specifically, we examine the interactions in terms of the social contact patterns among the population of different age-groups. To characterize the age-specific social contact-based transmission, we divide a city's population into seven age-groups: 0-6 years old (children); 7-14 (primary and junior high school students); 15-17 (high school students); 18-22 (university and college students); 23-44 (young/middle-aged people); 45-64 years old (middle-aged/elderly people); and 65 or above (elderly people). The population in each of the seven groups has its own specific social circles, gathering places, or activity patterns. Meanwhile, we consider four representative settings of social contacts that may cause the disease spread: (1) individual households, which may lead to the transmission within families; (2) schools, including primary/high schools as well as colleges and universities, which may cause the spread among students and teachers; (3) various physical workplaces, which may affect in-office and outside workers; a
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