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      The impact of health and economic policies on the spread of COVID-19 and economic activity ☆

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          Abstract

          This paper empirically investigates the causal linkages between COVID-19 spread, government health containment and economic support policies, and economic activity in the U.S. up to the introduction of vaccines in early 2021. We model their joint dynamics as generated by a structural vector autoregression and estimate it using U.S. state-level data. We identify structural shocks to the variables by making assumptions on their short-run relation consistent with salient epidemiological and economic features of COVID-19. We isolate the direct impact of COVID-19 spread and policy responses on economic activity by controlling for demand fluctuations using disaggregate exports data. We find that health containment and economic support policies are highly effective at curbing the spread of COVID-19 without leading to a long-term contraction of economic activity.

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          Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

          Seth Flaxman, Swapnil Mishra, Axel Gandy … (2020)
          Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that-for all of the countries we consider here-current interventions have been sufficient to drive Rt below 1 (probability Rt < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions-and lockdowns in particular-have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.
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            Effect of non-pharmaceutical interventions to contain COVID-19 in China

            Shengjie Lai, Nick Ruktanonchai, Liangcai Zhou … (2020)
            Summary On March 11, 2020, the World Health Organization declared COVID-19 a pandemic 1 . The outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective 2 , but quantitative research is still needed to assess the efficacy of NPIs and their timings 3 . Using epidemiological and anonymised human movement data 4,5 , here we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimated that there were a total of 114,325 COVID-19 cases (interquartile range 76,776 -164,576) in mainland China as of February 29, 2020. Without NPIs, the COVID-19 cases would likely have shown a 67-fold increase (interquartile range 44 - 94) by February 29, 2020, with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to have prevented more infections than travel restrictions and contact reductions, but combined NPIs achieved the strongest and most rapid effect. The lifting of travel restrictions since February 17, 2020 does not appear to lead to an increase in cases across China if the social distancing interventions can be maintained, even at a limited level of 25% reduction on average through late April. Our findings contribute to an improved understanding of NPIs on COVID-19 and to inform response efforts across the World.
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              Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research

              Conor G. McAloon, Áine B. Collins, Kevin Hunt … (2020)
              Objectives The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19. Design Rapid systematic review and meta-analysis of observational research. Setting International studies on incubation period of COVID-19. Participants Searches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies. Primary outcome measures Parameters of a lognormal distribution of incubation periods. Results The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days. Conclusions The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Eur Econ Rev
                Journal ID (iso-abbrev): Eur Econ Rev
                Title: European Economic Review
                Publisher: The Authors. Published by Elsevier B.V.
                ISSN (Print): 0014-2921
                ISSN (Electronic): 0014-2921
                Publication date PMC-release: 11 March 2022
                Publication date (Electronic): 11 March 2022
                Electronic Location Identifier: 104087
                Affiliations
                [a ]Yale University, United States of America
                [b ]Federal Reserve Bank of St. Louis, United States of America
                Author notes
                [* ]Corresponding author.
                Article
                Publisher Item ID: S0014-2921(22)00034-4 Publisher ID: 104087
                DOI: 10.1016/j.euroecorev.2022.104087
                PMC ID: 8912987
                PubMed ID: 35291621
                SO-VID: 31cdc04e-6098-49ed-95f6-f9e3170e90b0
                Copyright © © 2022 The Authors. Published by Elsevier B.V.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 20 April 2021
                Date revision received : 18 January 2022
                Date accepted : 29 January 2022
                Categories
                Subject: Article

                Keywords: covid-19,health containment policies,non-pharmaceutical interventions,pandemics,economic activity
                Data availability:
                Keywords: covid-19, health containment policies, non-pharmaceutical interventions, pandemics, economic activity

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