Abstract:What is already known about this topic?
Several outbreaks of coronavirus disease 2019 (COVID-19) occurred in Hong Kong in 2020, and the response had varied results based on the strength of policy measures and on compliance of the population.
What is added by this report?
By analyzing data of COVID-19 cases in Hong Kong, combined with the Google Mobility Trends and Oxford COVID-19 Government Response Tracker, we make recommendations for the future prevention and control … Show more
“…One of the reasons is that people's mobility hardly declined after the early stage of the COVID-19 pandemic in Hong Kong. Because people still have to obtain groceries, medicines, essential services, or go to work despite the COVID-19 control measures were still in effect, people's mobility to grocery shopping (e.g., supermarkets, food warehouses, farmers markets, specialty food shops, and pharmacies) in Hong Kong increased 14% and 25% on average during the third wave and fourth wave when compared to the baseline (set as the mean value of mobility from January 3 to February 6, 2020) ( Zhang et al, 2021 ; Google LLC 2021 ). Fig.…”
Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed.
“…One of the reasons is that people's mobility hardly declined after the early stage of the COVID-19 pandemic in Hong Kong. Because people still have to obtain groceries, medicines, essential services, or go to work despite the COVID-19 control measures were still in effect, people's mobility to grocery shopping (e.g., supermarkets, food warehouses, farmers markets, specialty food shops, and pharmacies) in Hong Kong increased 14% and 25% on average during the third wave and fourth wave when compared to the baseline (set as the mean value of mobility from January 3 to February 6, 2020) ( Zhang et al, 2021 ; Google LLC 2021 ). Fig.…”
Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed.
“…Hong Kong has a high population density, ranking fourth in the world, with approxmiately 7,060 people per km 2 in 2021. Thus, controlling respiratory infections is challenging ( 22 ). An Existing quarantine rule for the patient with COVID-19 is that if a COVID patient's family only has one toilet, then the Center for Health Protection will arrange for all family members to be quarantined in designated facilities.…”
This study aims to propose a pooling approach to simulate the compulsory universal RT-PCR test in Hong Kong and explore the feasibility of implementing the pooling method on a household basis. The mathematical model is initially verified, and then the simulation is performed under different prevalence rates and pooled sizes. The simulated population is based in Hong Kong. The simulation included 10,000,000 swab samples, with a representative distribution of populations in Hong Kong. The samples were grouped into a batch size of 20. If the entire batch is positive, then the group is further divided into an identical group size of 10 for re-testing. Different combinations of mini-group sizes were also investigated. The proposed pooling method was extended to a household basis. A representative from each household is required to perform the RT-PCR test. Results of the simulation replications, indicate a significant reduction (p < 0.001) of 83.62, 64.18, and 48.46% in the testing volume for prevalence rate 1, 3, and 5%, respectively. Combined with the household-based pooling approach, the total number of RT-PCR is 437,304, 956,133, and 1,375,795 for prevalence rates 1, 3, and 5%, respectively. The household-based pooling strategy showed efficiency when the prevalence rates in the population were low. This pooling strategy can rapidly screen people in high-risk groups for COVID-19 infections and quarantine those who test positive, even when time and resources for testing are limited.
“…We use the Oxford COVID‐19 Government Response Tracker (OxCGRT) to indicate the direct, indirect intervention and anti‐pandemic strength. The Oxford COVID‐19 Government Response Tracker (OxCGRT) from Oxford University can be used to measure Government response (Lee et al., 2021 ; Zhang, Liang, et al., 2021 ). The OxCGRT started collecting data on COVID‐19 government responses of China in late January 2021.…”
By examining China's province‐level data, this paper uses governmental intervention theory to investigate the extent to which governmental policy interventions alleviate the impact of the COVID‐19 pandemic on local economic growth. Results suggest that stronger government intervention during COVID‐19 pandemic boost the economic recovery, and the effectiveness of governmental policy interventions is contingent on pandemic severity and local economic endowment. Specifically, facilitating effect of government intervention on economic growth is effective in all provinces, and the impact of government intervention is more pronounced in the province with more diagnosed cases, a high level of marketization and fiscal income.
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