the impact of non pharmaceutical interventions on sars cov 2 transmission across 130 CORD-Papers-2021-10-25 (Version 1)

Title: The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories
Abstract: BACKGROUND: 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 using data from January to 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 were). Evidence about 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. Effect sizes varied depending on whether or not we included data after peak NPI intensity. 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, 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, actions policy-makers are taking to respond to the COVID-19 pandemic. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-020-01872-8.
Published: 2/5/2021
Journal: BMC Med
DOI: 10.1186/s12916-020-01872-8
DOI_URL: http://doi.org/10.1186/s12916-020-01872-8
Author Name: Liu, Yang
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_yang
Author Name: Morgenstern, Christian
Author link: https://covid19-data.nist.gov/pid/rest/local/author/morgenstern_christian
Author Name: Kelly, James
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kelly_james
Author Name: Lowe, Rachel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lowe_rachel
Author Name: Jit, Mark
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jit_mark
sha: 4372d3356a790ac463d3e3252b6a9cd1156f017e
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: Medline; PMC
source_x_url: https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 33541353
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/33541353
pmcid: PMC7861967
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861967
url: https://doi.org/10.1186/s12916-020-01872-8 https://www.ncbi.nlm.nih.gov/pubmed/33541353/
has_full_text: TRUE
Keywords Extracted from Text Content: COVID-19 people SARS coronavirus 2 coronavirus disease 2019 Non-pharmaceutical SARS-CoV-2 NPIs R t − R t . literature-although lockdowns SI https://github.com/yangclaraliu/COVID19_ OxCGRT SI Colombia facial Figure S6 node onset-to-delay Figure S10 NPIs R t red-international org/10.1186/s12916-020 Bank plm Coronavirus disease 2019 Palestine BIC NPIs' East Asia [12] SARS-CoV-2 SI splines contexts-internal contacts humans coronavirus 2 Figure S1 GAM Fig. 4 COVID-19 Fig. 2 Fig. 5 . Djibouti UK Figure S8-9 NPIs [11] βX Figure S8 Wuhan [9 Figure S7 stay-at-home [https://github.com/ yangclaraliu/COVID19_NPIs_vs_Rt] Figure S2 EpiForecasts children Tracker EpiForecasts [https:// epiforecasts.io/ people OxCGRT Figure S4 -5) [3 Cori Fig. 1 α i Figure S4 -5 Zambia https://doi BMC Jit
Extracted Text Content in Record: First 5000 Characters:Background: 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 using data from January to 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 were). Evidence about the effectiveness of the remaining NPIs (stay-athome 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. Effect sizes varied depending on whether or not we included data after peak NPI intensity. (Continued on next page) 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, 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, 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 transmissible 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, and 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 disrupts economic activities and daily life [4] . Hence, the psychological, social, and economic cost of interventions needs to be balanced against their 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, based on assumptions about how they may change transmission between individuals in populations. 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 (see Additional file 1: Table S8 for a review) . While several countries have seen disease incidence peak and fall [38] , 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-rel
Keywords Extracted from PMC Text: Coronavirus disease 2019 Djibouti OxCGRT's [https://github.com/yangclaraliu/COVID19_NPIs_vs_Rt] βXit East Asia Colombia humans Tracker people Zambia lockdowns" Cori NPIs [11] UK Wuhan [9 SARS-CoV-2 S10–11 contacts children's S2–3 NPIs' − face vaccines" stay-at-home EpiForecasts— [12] EpiForecasts EpiForecasts [https://epiforecasts.io/] " onset-to-delay NPIs GAM SI splines − k) https://github.com/yangclaraliu/COVID19_NPIs_vs_Rt Fig. 4 OxCGRT facial Fig. 5. OxCGRT SI COVID-19 Bank SI Palestine BIC αi+∑βXit+uitwhere
Extracted PMC Text Content in Record: First 5000 Characters:Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus is easily transmissible 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, and 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 disrupts economic activities and daily life [4]. Hence, the psychological, social, and economic cost of interventions needs to be balanced against their 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, based on assumptions about how they may change transmission between individuals in populations. 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 (see Additional file 1: Table S8 for a review) [11–37]. While several countries have seen disease incidence peak and fall [38], 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 [39]. Here, we used the regularly updated Oxford COVID-19 Government Response Tracker (OxCGRT) [3] and conducted panel analysis to understand the association between policy interventions and time-varying reproduction numbers (Rt), a measure of the rate of transmission of an infectious disease in a population. We also explore whether this relationship is modulated by definitions of policy interventions, temporal lags, and population characteristics in different countries. Data on COVID-19-related NPI intensity from 1 January to 22 June 2020 was extracted on 5 July 2020 from version 5 of OxCGRT, based on the codebook version 2.2 (22 April 2020) [3]. This version contains publicly available information from 178 countries and territories on 18 NPI categories. We further divided these countries and territories into seven regions according to the World Bank classification [40]. Note that these 18 NPI categories are broad, so many specific policy interventions (e.g. facial covering mandates) are not independently coded in the database. See Additional file 1: Table S1 for further metadata. From this database, we removed (i) "miscellaneous" policies as they contained no data at the time of our data extraction; (ii) "giving international support" and "investment in vaccines" policies as they did not on face validity have a causal pathway to influence local SARS-CoV-2 transmission within the timescale of the analysis; (iii) "fiscal measures" and "emergency investment in healthcare" policies as both the start and the duration of their effect is often unclear (e.g. the announcement of an investment may be implemented weeks later; funding that is allocated may be spent over a long time); and (iv) data after 22 June 2020 because > 10% of countries and territories have missing data after this date (see Additional file 1: Figure S1) [3]. Missing data fields on or before 22 June 2020 were imputed by (a) carrying forward or backwards the next or last non-missing observation when missingness occurred at the two tails of the time-series or (b) linearly interpolating using non-missing observations when missingness does not occur at the two tails of the time series. We divided the remaining 13 policy interventions into four policy groups roughly consistent with the original database (Table 1). Most NPIs in the database are measured on ordinal scales that capture intensity (e.g. 0 = no contact tracing; 1 = limited contact tracing; 2 = comprehensive contact tracing). Since the intervals between categories are not necessarily equally spaced, we converted NPI history into binary variables under two scenarios: (i) any effort scenario: all zero records were c
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