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      The Efficacy of Lockdown Against COVID-19: A Cross-Country Panel Analysis

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          Abstract

          Background

          There has been much debate about the effectiveness of lockdown measures in containing COVID-19, and their appropriateness given the economic and social cost they entail. To the best of our knowledge, no existing contribution to the literature has attempted to gauge the effectiveness of lockdown measures over time in a longitudinal cross-country perspective.

          Objectives

          This paper aims to fill the gap in the literature by assessing, at an international level, the effect of lockdown measures (or the lack of such measures) on the numbers of new infections. Given this policy’s expected change in effectiveness over time, we also measure the effect of having a lockdown implemented over a given number of days (from 7 to 20 days).

          Methods

          We pursue our objectives by means of a quantitative panel analysis, building a longitudinal dataset with observations from countries all over the world, and estimating the impact of lockdown via feasible generalized least squares fixed effect, random effects, generalized estimating equation, and hierarchical linear models.

          Results

          Our results show that lockdown is effective in reducing the number of new cases in the countries that implement it, compared with those countries that do not. This is especially true around 10 days after the implementation of the policy. Its efficacy continues to grow up to 20 days after implementation.

          Conclusion

          Results suggest that lockdown is effective in reducing the R0, i.e. the number of people infected by each infected person, and that, unlike what has been suggested in previous analyses, its efficacy continues to hold 20 days after the introduction of the policy.

          Electronic supplementary material

          The online version of this article (10.1007/s40258-020-00596-3) contains supplementary material, which is available to authorized users.

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          Most cited references4

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          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

            Background: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective: To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary Funding Source: U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.
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              The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China

              Abstract Background With its epicenter in Wuhan, China, the COVID-19 outbreak was declared a public health emergency of international concern (PHEIC) by the World Health Organization (WHO). Consequently, many countries have implemented flight restrictions to China. China itself has imposed a lockdown of the population of Wuhan as well as the entire Hubei province. However, whether these two enormous measures have led to significant changes in the spread of COVID-19 cases remains unclear. Methods We analyzed available data on the development of confirmed domestic and international COVID-19 cases before and after lockdown measures. We evaluated the correlation of domestic air traffic to the number of confirmed COVID-19 cases and determined the growth curves of COVID-19 cases within China before and after lockdown as well as after changes in COVID-19 diagnostic criteria. Results Our findings indicate a significant increase in doubling time from 2 days (95% Confidence Interval, CI): 1.9–2.6), to 4 days (95% CI: 3.5–4.3), after imposing lockdown. A further increase is detected after changing diagnostic and testing methodology to 19.3 (95% CI: 15.1–26.3), respectively. Moreover, the correlation between domestic air traffic and COVID-19 spread became weaker following lockdown (before lockdown: r = 0.98, p < 0.05 vs. after lockdown: r = 0.91, p = NS). Conclusions A significantly decreased growth rate and increased doubling time of cases was observed, which is most likely due to Chinese lockdown measures. A more stringent confinement of people in high risk areas seem to have a potential to slow down the spread of COVID-19.
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                Author and article information

                Contributors
                vincenzo.alfano@unina.it
                salvatore.ercolano@unibas.it
                Journal
                Appl Health Econ Health Policy
                Appl Health Econ Health Policy
                Applied Health Economics and Health Policy
                Springer International Publishing (Cham )
                1175-5652
                1179-1896
                3 June 2020
                : 1-9
                Affiliations
                [1 ]GRID grid.4691.a, ISNI 0000 0001 0790 385X, Department of Structures for Engineering and Architectures, , University of Napoli Federico II, ; Naples, Italy
                [2 ]GRID grid.7367.5, ISNI 0000000119391302, Department of Mathematics, Computer Science, and Economics, , University of Basilicata, ; Potenza, Italy
                Author information
                http://orcid.org/0000-0002-4981-748X
                Article
                596
                10.1007/s40258-020-00596-3
                7268966
                32495067
                2db3bb20-54fe-41e2-ad74-e03ea48e9950
                © Springer Nature Switzerland AG 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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                Original Research Article

                Economics of health & social care
                Economics of health & social care

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