Evaluating the effectiveness of Hong Kong's border restriction policy in reducing COVID-19 infections

High connectivity and human movement limits the impact of travel time on infectious disease transmission

The speed of spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic highlights the importance of understanding how infections are transmitted in a highly connected world. Prior to vaccination, changes in human mobility patterns were used as non-pharmaceutical interventions to eliminate or suppress viral transmission. The rapid spread of respiratory viruses, various intervention approaches, and the global dissemination of SARS-CoV-2 underscore the necessity for epidemiological models that incorporate mobility to comprehend the spread of the virus. Here, we introduce a metapopulation susceptible-exposed-infectious-recovered model parametrized with human movement data from 340 cities in China. Our model replicates the early-case trajectory in the COVID-19 pandemic. We then use machine learning algorithms to determine which network properties best predict spread between cities and find travel time to be most important, followed by the human movement-weighted personalized PageRank. However, we show that travel time is most influential locally, after which the high connectivity between cities reduces the impact of travel time between individual cities on transmission speed. Additionally, we demonstrate that only significantly reduced movement substantially impacts infection spread times throughout the network.

Dynamic evolution of policy mix in response to COVID-19: Practice from China

COVID-19 is a global pandemic. In response to this unprecedented crisis, Chinese government formulated a series of policies. This research is dedicated to exploring the dynamic evolution of China's policy mix in response to COVID-19 in different crisis response stages from a network perspective. First, a three-dimensional analysis framework of "policy subject-policy target-policy instrument" was developed. Then, based on the data sets collected by textual analysis, the dynamic evolution of policy subject, policy target, policy instrument in China's policy mix in response to COVID-19 was discussed by using the method of SNA. This study concluded that the core policy subject, policy instrument, and policy target of China's response to COVID-19 changed with time. National Health Commission (NHC), Ministry of Finance (MOF), Ministry of Transport (MOT) and Ministry of Human Resources and Social Security (MHRSS) have important influences in the network of policy subjects. Other subjects are more at the edge of the network, and there are few joint issuances among policy subjects. The study also found that the core policy target was adjusted over time, with phased dynamic characteristics. At the initial stage of China's response to COVID-19, "reduce infection and mortality" and "steadily carry out economic and social work" were the core policy targets. With the COVID-19 under control, "enterprise development and work resumption" becomes a new core policy target. In addition, this study also revealed the dynamic evolution and unbalanced use of China's policy instruments in response to COVID-19 in different stages. The combination of policy instruments is mainly composed of "mandatory administration instruments" and "economic incentive instruments", and supplemented by "health promotion instruments" and "voluntary plan instruments". These findings may enrich the literature on COVID-19 policy to help researchers understand the dynamics of policy from a network perspective. Moreover, these findings may provide several valuable implications for policymakers and other countries to formulate more effective policies for epidemic response.

Effects of strict containment policies on COVID-19 pandemic crisis: lessons to cope with next pandemic impacts

The goal of the study here is to analyze and assess whether strict containment policies to cope with Coronavirus Disease 2019 (COVID-19) pandemic crisis are effective interventions to reduce high numbers of infections and deaths. A homogenous sample of 31 countries is categorized in two sets: countries with high or low strictness of public policy to cope with COVID-19 pandemic crisis. The findings here suggest that countries with a low intensity of strictness have average confirmed cases and fatality rates related to COVID-19 lower than countries with high strictness in containment policies (confirmed cases are 24.69% vs. 26.06% and fatality rates are 74.33% vs. 76.38%, respectively, in countries with low and high strictness of COVID-19 public policies of containment). What this study adds is that high levels of strict restriction policies may not be useful measures of control in containing the spread and negative impact of pandemics similar to COVID-19 and additionally a high strictness in containment policies generates substantial social and economic costs. These findings can be explained with manifold socioeconomic and environmental factors that support transmission dynamics and circulation of COVID-19 pandemic. Hence, high levels of strictness in public policy (and also a high share of administering new vaccines) seem to have low effectiveness to stop pandemics similar to COVID-19 driven by mutant viral agents. These results here suggest that the design of effective health policies for prevention and preparedness of future pandemics should be underpinned in a good governance of countries and adoption of new technology, rather than strict and generalized health polices having ambiguous effects of containment in society.

COVID-19 Vaccination is not a Sufficient Public Policy to face Crisis Management of next Pandemic Threats

This study reveals that a vast vaccination campaign is a necessary but not sufficient public policy to reduce the negative impact of Coronavirus Disease 2019 (COVID-19) pandemic crisis because manifold factors guide the spread of this new infectious disease and related mortality in society. Statistical evidence here, based on a worldwide sample of countries, shows a positive correlation between people fully vaccinated and COVID-19 mortality (r = + 0.65, p-value < 0.01). Multivariate regression, controlling income per capita, confirms this finding. Results suggest that the increasing share of people vaccinated against COVID-19 seems to be a necessary but not sufficient health policy to reduce mortality of COVID-19. The findings here can be explained with the role of Peltzman effect, new variants, environmental and socioeconomic factors that affect the diffusion and negative impact of COVID-19 pandemic in society. This study extends the knowledge in this research field to design effective public policies of crisis management for facing next pandemic threats.