Settings of virus exposure and their implications in the propagation of transmission networks in a COVID-19 outbreak

Wong, Ngai Sze; Lee, Shui Shan; Kwan, Tsz Ho; Yeoh, Eng‐Kiong

doi:10.1016/j.lanwpc.2020.100052

Cited by 34 publications

(30 citation statements)

References 30 publications

(26 reference statements)

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“…To mitigate the spread and outbreak of COVID-19, many countries have implemented various intervention and social distancing strategies, such as quarantining suspected and confirmed cases, implementing school closures and workplace lockdowns, and reducing public transportation. The effects of different strategies have been modeled and evaluated [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] . It remains a serious challenge for public health decision-makers to identify the most effective yet timely strategies for specific regions.…”

Section: Introductionmentioning

confidence: 99%

Uncovering transmission patterns of COVID-19 outbreaks: A region-wide comprehensive retrospective study in Hong Kong

Liu¹,

Gu²,

Liu³

2021

EClinicalMedicine

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Background Given the dynamism and heterogeneity of COVID-19 transmission patterns, determining the most effective yet timely strategies for specific regions remains a severe challenge for public health decision-makers. Methods In this work, we proposed a spatiotemporal connectivity analysis method for discovering transmission patterns across geographic locations and age-groups throughout different COVID-19 outbreak phases. First, we constructed the transmission networks of the confirmed cases during different phases by considering the spatiotemporal connectivity of any two cases. Then, for each case and those cases immediately pointed from it, we characterized the corresponding cross-district/population transmission pattern by counting their district-to-district and age-to-age occurrences. By summating the cross-district/population transmission patterns of all cases during a given period, we obtained the aggregated cross-district and cross-population transmission patterns. Findings We conducted a region-wide comprehensive retrospective study in Hong Kong based on the complete data report of COVID-19 cases, covering all 18 districts between January 23, 2020, and January 8, 2021 (https://data.gov.hk/en-data/dataset/hk-dh-chpsebcddr-novel-infectious-agent). The spatiotemporal connectivity analysis clearly unveiled the quantitative differences among various outbreak waves in their transmission scales, durations, and patterns. Moreover, for the statistically similar waves, their cross-district/population transmission patterns could be quite different (e.g., the cross-district transmission of the fourth wave was more diverse than that of the third wave, while the transmission over age-groups of the fourth wave was more concentrated than that of the third wave). At an overall level, super-spreader individuals (highly connected cases in the transmission networks) were usually concentrated in only a few districts (2 out of 18 in our study) or age-groups (3 out of 11 in our study). Interpretation With the discovered cross-district or cross-population transmission patterns, all of the waves of COVID-19 outbreaks in Hong Kong can be systematically scrutinized. Among all districts, quite a few (e.g., the Yau Tsim Mong district) were instrumental in spreading the virus throughout the pandemic. Aside from being exceptionally densely populated, these districts were also social-economic centers. With a variety of situated public venues, such as restaurants and singing/dancing clubs, these districts played host to all kinds of social gathering events, thereby providing opportunities for widespread and rapid transmission of the virus. Thus, these districts should be given the highest priority when deploying district-specific social distancing or intervention strategies, such as lockdown and stringent mandatory coronavirus testing for identifying and obstructing the chain of transmission. We also observed that most of the reporte...

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Section: Introductionmentioning

confidence: 99%

Uncovering transmission patterns of COVID-19 outbreaks: A region-wide comprehensive retrospective study in Hong Kong

Liu¹,

Gu²,

Liu³

2021

EClinicalMedicine

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“…Subsequently, after tightening measures for inbound travelers and because there were more work-from-home arrangements in wave 3, the positive correlation between non–Chinese ethnicities and vulnerability to COVID-19 infection in waves 1 and 2 shifted to be negative. Entertainment venues constituted a primary exposure setting that spread COVID-19 in waves 1 and 2 ( 14 ) but ceased to be a major contributor to community vulnerability in wave 3 after these venues were closed. In wave 3, socioeconomic deprivation, poor housing, and dense household composition, as well as epidemiologic factors that facilitate viral transmission, became more key contributors to community vulnerability to COVID-19 infection.…”

Section: Discussionmentioning

confidence: 99%

Assessing Community Vulnerability over 3 Waves of COVID-19 Pandemic, Hong Kong, China

Liao¹,

Dong²,

Yuan³

et al. 2021

Emerg. Infect. Dis.

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T he coronavirus disease (COVID-19) pandemic disproportionally affects socially disadvantaged populations because of economic, social, and demographic factors, as well as their health conditions and practices (1). Identifying vulnerable communities and effectively allocating ameliorating resources to them are necessary if policy makers are to manage the effects of COVID-19. Community vulnerability indexes (CVIs) have been increasingly used to assess community social vulnerability to a pandemic using community-level socioeconomic and demographic data (2-7). In the United States, greater CVI and vulnerability in domains of minority status, household composition, housing, transportation, and disability at the county level were signifi cantly associated with greater risk of COVID-19 diagnosis (3,4). We aimed to construct a CVI more socioculturally adapted to metropolises in Asia to explain the impact of COVID-19 across more microgeographic units (i.e., districts) within a highly urbanized city, Hong Kong, China. We also analyzed the extent that CVI was correlated with the evolution of the COVID-19 pandemic in Hong Kong. The StudyHong Kong has long been regarded as an epicenter for many infectious diseases and is predisposed to severe COVID-19 impact because of its dense and rapidly aging population (8,9). Geographically, Hong Kong comprises 3 main regions, New Territories, Kowloon, and Hong Kong Island, which are further subdivided into 18 administrative districts (10). As of August 31, 2020, Hong Kong had experienced 3 waves of COVID-19 (Appendix Figure 1, https:// wwwnc.cdc.gov/EID/article/27/7/20-4076-App1. pdf), reporting 4,811 COVID-19 cases, including 89 deaths; 76.5% of cases occurred in wave 3 (11).Following methods used by the Surgo Foundation (6), we fi rst defi ned 5 domains that contributed to an overall CVI: socioeconomic status, household composition, housing condition, healthcare system, and epidemiologic factors. We included 22 indicators in the 5 domains for calculating domain CVI and overall CVI (Table 1). We fi rst ranked each indicator by district, with a higher rank indicating greater vulnerability. Then, we calculated the percentile rank of each district over each indicator using the formula of percentile rank = (rank -1)/(n -1), where n refers to total geographic units (n = 18). A higher percentile rank indicates greater relative CVI of the district over the specifi c indicator. We then summed the percentile ranks over all indicators within each domain, reranked them to calculate domain CVIs, and summed the percentile ranks of all domains to calculate an overall CVI for each district. We assumed equal weights for indicators within domains and for the 5 domains within the overall CVI because of a lack of available evidence informing a more optimized weight scheme. Finally, we categorized all districts into very high (>80%), high (60%-80%), moderate (40%-60%), low (20%-40%), and very low (<20%) vulnerability on the basis of their domain and overall

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“…Evidence shows that there could be marked heterogeneity in the characteristics of SARS-CoV-2 transmission 4 , with the majority (∼80%) of the secondary transmission caused by a very small proportion of SARS-CoV-2 infected persons, and outbreaks of COVID-19 distributed unevenly in certain settings and geographical locations 20 . Our study has found increased rates of outbreak in certain industrial sectors and English regions, and a large variation of the size of the attack rates.…”

Section: Discussionmentioning

confidence: 99%

“…COVID-19 is a highly contagious disease and can spread rapidly without effective control measures. Because of the heterogeneity characteristics of the SARS-CoV-2 transmission, COVID-19 cases are appearing in clusters in different settings 3 4 .…”

Section: Introductionmentioning

confidence: 99%

COVID-19 outbreak rates and infection attack rates associated with the workplace: a descriptive epidemiological study

Chen

Aldridge

Ferraro

et al. 2021

Preprint

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Background: A large number of COVID–19 outbreaks/clusters have been reported in a variety of workplace settings since the start of the pandemic. However, information on the rate of outbreak occurrences which helps to identify the type of workplaces that are more likely to experience an outbreak, or infection attack rates which estimates the potential extent of the virus transmission in an outbreak, has not yet been available to inform intervention strategies to limit transmission . Objectives: To link datasets on workplace settings and COVID–19 workplace outbreaks in England in order to: identify the geographical areas and workplace sectors with a high rate of outbreaks; and compare infection attack rates by workplace size and sector. Methods: We analysed Public Health England (PHE) HPZone data on COVID–19 outbreaks in workplaces, covering the time period of 18 May – 12 October 2020. The workplaces analysed excluded care homes, hospitals and educational settings. We calculated the workplace outbreak rates by nine English regions, 151 Upper Tier Local Authorities (UTLAs) and twelve industrial sectors, using National Population Database (NPD) data extracted in May 2019 on the total number of the relevant workplaces as the denominator. We also calculated the infection attack rates by enterprise size (small, medium, large) and industrial sector, using PHE Situations of Interest (SOI) data on the number of test–confirmed COVID–19 cases in a workplace outbreak as the numerator, and using NPD data on the number employed in that workplace as the denominator. Results: In total, 1,317 confirmed workplace outbreaks were identified from HPZone data, of which 1,305 were available for estimation of outbreak rates. The average outbreak rate was 66 per 100,000 workplaces. Of the nine geographical regions in England, the North West had the highest workplace outbreak rate (155/100,000 workplaces), based on 351 outbreaks. Of the UTLAs, the highest workplace outbreak rate was Blackburn with Darwen (387/100,000 workplaces). The industrial sector with the highest workplace outbreak rate was manufacturers and packers of food (1,672/100,000), based on 117 outbreaks: this was consistent across seven of the regions. In addition, high outbreak rates in warehouses were observed in the East Midlands and the North West. In total, 390 outbreaks were identified from SOI data and 264 of them allowed for estimation of attack rates. The overall median attack rate was 3.4% of the employed persons with confirmed COVID–19 at a workplace with an outbreak. Most of these outbreaks (162) had an attack rate less than 6%. However, in a small number of outbreaks (57) the attack rate was over 15%. The attack rates increased as the size of the enterprise decreased. The highest attack rate was for outbreaks in close contact services (median 16.5%), which was followed by outbreaks in restaurants and catering (median 10.2%), and in manufacturers and packers of non–food products (median 6.7%). Conclusions: Our linked dataset analysis approach allows early identification of geographical regions and industrial sectors with higher rates of COVID–19 workplace outbreaks as well as estimation of attack rates by enterprise size and sector. This can be used to inform interventions to limit transmission of the virus. Our approach to analysing the workplace outbreak data can also be applied to calculation of outbreak rates and attack rates in other types of settings such as care homes, hospitals and educational settings.

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Settings of virus exposure and their implications in the propagation of transmission networks in a COVID-19 outbreak

Cited by 34 publications

References 30 publications

Uncovering transmission patterns of COVID-19 outbreaks: A region-wide comprehensive retrospective study in Hong Kong

Uncovering transmission patterns of COVID-19 outbreaks: A region-wide comprehensive retrospective study in Hong Kong

Assessing Community Vulnerability over 3 Waves of COVID-19 Pandemic, Hong Kong, China

COVID-19 outbreak rates and infection attack rates associated with the workplace: a descriptive epidemiological study

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