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Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries

To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore,...

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Autores principales: Chowdhury, Rajiv, Heng, Kevin, Shawon, Md Shajedur Rahman, Goh, Gabriel, Okonofua, Daisy, Ochoa-Rosales, Carolina, Gonzalez-Jaramillo, Valentina, Bhuiya, Abbas, Reidpath, Daniel, Prathapan, Shamini, Shahzad, Sara, Althaus, Christian L., Gonzalez-Jaramillo, Nathalia, Franco, Oscar H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237242/
https://www.ncbi.nlm.nih.gov/pubmed/32430840
http://dx.doi.org/10.1007/s10654-020-00649-w
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author Chowdhury, Rajiv
Heng, Kevin
Shawon, Md Shajedur Rahman
Goh, Gabriel
Okonofua, Daisy
Ochoa-Rosales, Carolina
Gonzalez-Jaramillo, Valentina
Bhuiya, Abbas
Reidpath, Daniel
Prathapan, Shamini
Shahzad, Sara
Althaus, Christian L.
Gonzalez-Jaramillo, Nathalia
Franco, Oscar H.
author_facet Chowdhury, Rajiv
Heng, Kevin
Shawon, Md Shajedur Rahman
Goh, Gabriel
Okonofua, Daisy
Ochoa-Rosales, Carolina
Gonzalez-Jaramillo, Valentina
Bhuiya, Abbas
Reidpath, Daniel
Prathapan, Shamini
Shahzad, Sara
Althaus, Christian L.
Gonzalez-Jaramillo, Nathalia
Franco, Oscar H.
author_sort Chowdhury, Rajiv
collection PubMed
description To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs—increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal “break” when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R(0)) of 2.2 for no intervention, and subsequent effective reproduction numbers (R) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a “schedule” of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10654-020-00649-w) contains supplementary material, which is available to authorized users.
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spelling pubmed-72372422020-05-20 Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries Chowdhury, Rajiv Heng, Kevin Shawon, Md Shajedur Rahman Goh, Gabriel Okonofua, Daisy Ochoa-Rosales, Carolina Gonzalez-Jaramillo, Valentina Bhuiya, Abbas Reidpath, Daniel Prathapan, Shamini Shahzad, Sara Althaus, Christian L. Gonzalez-Jaramillo, Nathalia Franco, Oscar H. Eur J Epidemiol Covid-19 To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs—increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal “break” when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R(0)) of 2.2 for no intervention, and subsequent effective reproduction numbers (R) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a “schedule” of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10654-020-00649-w) contains supplementary material, which is available to authorized users. Springer Netherlands 2020-05-19 2020 /pmc/articles/PMC7237242/ /pubmed/32430840 http://dx.doi.org/10.1007/s10654-020-00649-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Covid-19
Chowdhury, Rajiv
Heng, Kevin
Shawon, Md Shajedur Rahman
Goh, Gabriel
Okonofua, Daisy
Ochoa-Rosales, Carolina
Gonzalez-Jaramillo, Valentina
Bhuiya, Abbas
Reidpath, Daniel
Prathapan, Shamini
Shahzad, Sara
Althaus, Christian L.
Gonzalez-Jaramillo, Nathalia
Franco, Oscar H.
Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
title Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
title_full Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
title_fullStr Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
title_full_unstemmed Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
title_short Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
title_sort dynamic interventions to control covid-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries
topic Covid-19
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237242/
https://www.ncbi.nlm.nih.gov/pubmed/32430840
http://dx.doi.org/10.1007/s10654-020-00649-w
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