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Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective

The imposition and lifting of non-pharmaceutical interventions (NPIs) to avert the COVID-19 pandemic have gained popularity worldwide and will continue to be enforced until herd immunity is achieved. We developed a linear regression model to ascertain the nexus between the time-varying reproduction...

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Autores principales: Jayaweera, Mahesh, Dannangoda, Chamath, Dilshan, Dilum, Dissanayake, Janith, Perera, Hasini, Manatunge, Jagath, Gunawardana, Buddhika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261138/
https://www.ncbi.nlm.nih.gov/pubmed/34250320
http://dx.doi.org/10.1016/j.idm.2021.06.004
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author Jayaweera, Mahesh
Dannangoda, Chamath
Dilshan, Dilum
Dissanayake, Janith
Perera, Hasini
Manatunge, Jagath
Gunawardana, Buddhika
author_facet Jayaweera, Mahesh
Dannangoda, Chamath
Dilshan, Dilum
Dissanayake, Janith
Perera, Hasini
Manatunge, Jagath
Gunawardana, Buddhika
author_sort Jayaweera, Mahesh
collection PubMed
description The imposition and lifting of non-pharmaceutical interventions (NPIs) to avert the COVID-19 pandemic have gained popularity worldwide and will continue to be enforced until herd immunity is achieved. We developed a linear regression model to ascertain the nexus between the time-varying reproduction number averaged over a time window of six days (R(ts)) and seven NPIs: contact tracing, quarantine efforts, social distancing and health checks, hand hygiene, wearing of facemasks, lockdown and isolation, and health-related supports. Our analysis suggests that the second wave that emerged in Sri Lanka in early October 2020 continued despite numerous NPIs. The model indicates that the most effective single NPI was lockdown and isolation. Conversely, the least effective individual NPIs were hand hygiene and wearing of facemasks. The model also demonstrates that to mitigate the second wave to a satisfactory level (R(ts)<1), the best single NPI was the contact tracing with stringent imposition (% of improvement of R(ts) was 69.43 against the base case). By contrast, the best combination of two NPIs was the lockdown & isolation with health-related supports (% of improvement was 31.92 against the base case). As such, many health authorities worldwide can use this model to successfully strategize the imposition and lifting of NPIs for averting the COVID-19 pandemic.
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spelling pubmed-82611382021-07-07 Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective Jayaweera, Mahesh Dannangoda, Chamath Dilshan, Dilum Dissanayake, Janith Perera, Hasini Manatunge, Jagath Gunawardana, Buddhika Infect Dis Model Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu The imposition and lifting of non-pharmaceutical interventions (NPIs) to avert the COVID-19 pandemic have gained popularity worldwide and will continue to be enforced until herd immunity is achieved. We developed a linear regression model to ascertain the nexus between the time-varying reproduction number averaged over a time window of six days (R(ts)) and seven NPIs: contact tracing, quarantine efforts, social distancing and health checks, hand hygiene, wearing of facemasks, lockdown and isolation, and health-related supports. Our analysis suggests that the second wave that emerged in Sri Lanka in early October 2020 continued despite numerous NPIs. The model indicates that the most effective single NPI was lockdown and isolation. Conversely, the least effective individual NPIs were hand hygiene and wearing of facemasks. The model also demonstrates that to mitigate the second wave to a satisfactory level (R(ts)<1), the best single NPI was the contact tracing with stringent imposition (% of improvement of R(ts) was 69.43 against the base case). By contrast, the best combination of two NPIs was the lockdown & isolation with health-related supports (% of improvement was 31.92 against the base case). As such, many health authorities worldwide can use this model to successfully strategize the imposition and lifting of NPIs for averting the COVID-19 pandemic. KeAi Publishing 2021-07-07 /pmc/articles/PMC8261138/ /pubmed/34250320 http://dx.doi.org/10.1016/j.idm.2021.06.004 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
Jayaweera, Mahesh
Dannangoda, Chamath
Dilshan, Dilum
Dissanayake, Janith
Perera, Hasini
Manatunge, Jagath
Gunawardana, Buddhika
Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective
title Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective
title_full Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective
title_fullStr Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective
title_full_unstemmed Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective
title_short Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective
title_sort grappling with covid-19 by imposing and lifting non-pharmaceutical interventions in sri lanka: a modeling perspective
topic Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261138/
https://www.ncbi.nlm.nih.gov/pubmed/34250320
http://dx.doi.org/10.1016/j.idm.2021.06.004
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