the impact of non pharmaceutical interventions on sars cov 2 transmission across 130 CORD-Papers (Version 1)

Title: The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories
Abstract: Introduction: Non-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). However empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure international travel restrictions economic measures and health system actions on SARS-CoV-2 transmission in 130 countries and territories. Methods: We used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission with data from January - June 2020. First we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number (Rt) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in Rt levels of NPI intensity time-varying changes in NPI effect and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs. Results: There was strong evidence for an association between two NPIs (school closure internal movement restrictions) and reduced Rt. Another three NPIs (workplace closure income support and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity while two NPIs (public events cancellation restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g. restrictions on 1000+ people gathering were not effective restrictions on <10 people gathering was). Evidence supporting the effectiveness of the remaining NPIs (stay-at-home requirements public information campaigns public transport closure international travel controls testing contact tracing) was inconsistent and inconclusive. We found temporal clustering between many of the NPIs. Conclusion: Understanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering time-dependent variation in effects and differences in NPI intensity. However the effectiveness of school closure and internal movement restrictions appears robust across different model specifications taking into account these effects with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many although not all the actions policy-makers are taking to respond to the COVID-19 pandemic.
Published: 2020-08-12
DOI: 10.1101/2020.08.11.20172643
DOI_URL: http://doi.org/10.1101/2020.08.11.20172643
Author Name: Liu Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Morgenstern C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/morgenstern_c
Author Name: Kelly J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kelly_j
Author Name: Lowe R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lowe_r
Author Name: CMMID COVID Working Group
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cmmid_covid_working_group
Author Name: Jit M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jit_m
Author Name: Klepac, Petra
Author link: https://covid19-data.nist.gov/pid/rest/local/author/klepac_petra
sha: 8465991de36a1cbc3d152909ad98ba8ee7aeaada
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2020.08.11.20172643 http://medrxiv.org/cgi/content/short/2020.08.11.20172643v1?rss=1
has_full_text: TRUE
Keywords Extracted from Text Content: coronavirus disease 2019 SARS coronavirus 2 R t SARS-CoV-2 Non-pharmaceutical COVID-19 people NPIs NPIs -both contact https://doi.org/10.1101/2020.08.11.20172643 doi Coronavirus disease 2019 solid line medRxiv preprint SI R t EpiForecasts contacts medRxiv humans OxCGRT SI NPIs medRxiv preprint Table 1 R t NPIs Figure 4 EpiForecast coronavirus 2 plm facial children onset-to-delay SARS-CoV-2 people medRxiv preprint Figure 5 lockdowns medRxiv preprint Table 2 medRxiv preprint NPI appendix Bank lines NPIs' https://github.com/yangclaraliu/COVID19_NPIs_vs_Rt medRxiv preprint Figure S2 i α i Dashed vertical lines Caribean stay-at-home medRxiv preprint Figure S5 greens medRxiv preprint Figure 1 medRxiv preprint Figure S7 Cori BIC Tracker medRxiv preprint Figure S3 Wuhan (9 medRxiv preprint Table S1 medRxiv preprint A t−k COVID-19 OxCGRT SI splines GAM Geneva Katelijn Vandemaele
Extracted Text Content in Record: First 5000 Characters:Introduction: Non-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 . However, empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure, international travel restrictions, economic measures, and health system actions on SARS-CoV-2 transmission in 130 countries and territories. Methods: We used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission with data from January -June 2020. First, we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number ( R t ) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in R t , levels of NPI intensity, time-varying changes in NPI effect and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs. Results: There was strong evidence for an association between two NPIs (school closure, internal movement restrictions) and reduced R t . Another three NPIs (workplace closure, income support and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity, while two NPIs (public events cancellation, restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g., restrictions on 1000+ people gathering were not effective, restrictions on <10 people gathering was). Evidence supporting the effectiveness of the remaining NPIs (stay-at-home requirements, public information campaigns, public transport closure, international travel controls, testing, contact tracing) was inconsistent and inconclusive. We found temporal clustering between many of the NPIs. Conclusion: Understanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering, time-dependent variation in effects and differences in NPI intensity. However, the effectiveness of school closure and internal movement restrictions appears robust across different model specifications taking into account these effects, with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many although not all the actions policy-makers are taking to respond to the COVID-19 pandemic. Coronavirus disease 2019 (COVID- 19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus is easily transmittable between humans, with a basic reproduction number around 2-4 depending on the setting (1, 2) . To date, no vaccine or highly effective pharmaceutical treatment exists against COVID-19. Countries have used a range of non-pharmaceutical interventions (NPIs) such as testing suspected cases followed by isolation of confirmed cases and quarantine of their contacts, physical distancing measures such as schools and workplaces closures, income support for households affected by COVID-19 and associated interventions, as well as domestic and international travel restrictions (3) . These interventions aim to prevent infection introduction, contain outbreaks, and reduce peak epidemic size so that healthcare systems do not become overwhelmed. However, these interventions come at a cost. Testing and contact tracing require laboratory and public health resources to be successful at scale, government subsidies affect national budgets, while physical distancing interferes with economic activities (4) . Hence, the psychological, social, and economic cost of interventions needs to be balanced against the potential effectiveness in reducing SARS-CoV-2 spread. Modelling studies suggest that travel restrictions (5, 6) , contact tracing and quarantine (7, 8) and physical distancing (9,10) may delay SARS-CoV-2 spread. However, the effectiveness of such interventions depends on factors such as societal compliance (e.g., the extent to which people reduce their daily contacts following government restrictions) that are difficult to prospectively measure. Empirical evidence about the effectiveness of specific policy interventions has been limited (11) (12) (13) . While several countries have seen disease incidence peak and fall (14) , ascribing changes in transmission to particular interventions is difficult since countries tend to impose combinations of policy changes at different levels of stringency in close temporal sequence. Several global databases of COVID-19-related policy interventions have been compiled (15) . Here, we used the regularly updated Oxford COVID-19 Government Response Tracker (OxCGRT) (3) and conducted panel analysis to understand the association between
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