The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.

The province of Sindh reported the first COVID-19 case in Pakistan on 26th February 2020. The Government of Sindh has employed numerous control measures to limit its spread. However, for low-and middle-income countries such as Pakistan, the management protocols for controlling a pandemic are not alw...

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Autores principales: Bilal Ahmed Usmani, Mustafain Ali, Muhammad Abul Hasan, Amna Rehana Siddiqui, Sameen Siddiqi, Aaron Guanliang Lim, Saad Ahmed Qazi
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spelling oai:doaj.org-article:25d1cde361ba4b9a8d28fb4cba0e84bf2021-12-02T20:19:09ZThe Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.1932-620310.1371/journal.pone.0260129https://doaj.org/article/25d1cde361ba4b9a8d28fb4cba0e84bf2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0260129https://doaj.org/toc/1932-6203The province of Sindh reported the first COVID-19 case in Pakistan on 26th February 2020. The Government of Sindh has employed numerous control measures to limit its spread. However, for low-and middle-income countries such as Pakistan, the management protocols for controlling a pandemic are not always as definitive as they would be in other developed nations. Given the dire socio-economic conditions of Sindh, continuation of province-wise lockdowns may inadvertently cause a potential economic breakdown. By using a data driven SEIR modelling framework, this paper describes the evolution of the epidemic projections because of government control measures. The data from reported COVID-19 prevalence and google mobility is used to parameterize the model at different time points. These time points correspond to the government's call for advice on the prerequisite actions required to curtail the spread of COVID-19 in Sindh. Our model predicted the epidemic peak to occur by 18th June 2020 with approximately 3500 reported cases at that peak, this projection correlated with the actual recorded peak during the first wave of the disease in Sindh. The impact of the governmental control actions and religious ceremonies on the epidemic profile during this first wave of COVID-19 are clearly reflected in the model outcomes through variations in the epidemic peaks. We also report these variations by displaying the trajectory of the epidemics had the control measures been guided differently; the epidemic peak may have occurred as early as the end of May 2020 with approximately 5000 reported cases per day had there been no control measures and as late as August 2020 with only around 2000 cases at the peak had the lockdown continued, nearly flattening the epidemic curve.Bilal Ahmed UsmaniMustafain AliMuhammad Abul HasanAmna Rehana SiddiquiSameen SiddiqiAaron Guanliang LimSaad Ahmed QaziPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0260129 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Bilal Ahmed Usmani
Mustafain Ali
Muhammad Abul Hasan
Amna Rehana Siddiqui
Sameen Siddiqi
Aaron Guanliang Lim
Saad Ahmed Qazi
The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.
description The province of Sindh reported the first COVID-19 case in Pakistan on 26th February 2020. The Government of Sindh has employed numerous control measures to limit its spread. However, for low-and middle-income countries such as Pakistan, the management protocols for controlling a pandemic are not always as definitive as they would be in other developed nations. Given the dire socio-economic conditions of Sindh, continuation of province-wise lockdowns may inadvertently cause a potential economic breakdown. By using a data driven SEIR modelling framework, this paper describes the evolution of the epidemic projections because of government control measures. The data from reported COVID-19 prevalence and google mobility is used to parameterize the model at different time points. These time points correspond to the government's call for advice on the prerequisite actions required to curtail the spread of COVID-19 in Sindh. Our model predicted the epidemic peak to occur by 18th June 2020 with approximately 3500 reported cases at that peak, this projection correlated with the actual recorded peak during the first wave of the disease in Sindh. The impact of the governmental control actions and religious ceremonies on the epidemic profile during this first wave of COVID-19 are clearly reflected in the model outcomes through variations in the epidemic peaks. We also report these variations by displaying the trajectory of the epidemics had the control measures been guided differently; the epidemic peak may have occurred as early as the end of May 2020 with approximately 5000 reported cases per day had there been no control measures and as late as August 2020 with only around 2000 cases at the peak had the lockdown continued, nearly flattening the epidemic curve.
format article
author Bilal Ahmed Usmani
Mustafain Ali
Muhammad Abul Hasan
Amna Rehana Siddiqui
Sameen Siddiqi
Aaron Guanliang Lim
Saad Ahmed Qazi
author_facet Bilal Ahmed Usmani
Mustafain Ali
Muhammad Abul Hasan
Amna Rehana Siddiqui
Sameen Siddiqi
Aaron Guanliang Lim
Saad Ahmed Qazi
author_sort Bilal Ahmed Usmani
title The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.
title_short The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.
title_full The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.
title_fullStr The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.
title_full_unstemmed The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.
title_sort impact of disease control measures on the spread of covid-19 in the province of sindh, pakistan.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/25d1cde361ba4b9a8d28fb4cba0e84bf
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