How successful Bangladesh is in controlling the coronavirus pandemic?

Abstract

Background The reported number of COVID-19 patients increases on average along with the increased laboratory tests in Bangladesh implying a possibility of the spread of deadly coronavirus being out of control. Contrary to that, the government claims that it controls the spread of coronavirus through undertaking stringent policy measures. This different scenario leads this study on whether these measures have any positive impact on controlling the pandemic. Results The results show that simulated number of patients (without policy measures) surpassed the actual number of patients (with policy measures) from the first week of July 2020 which may provide a signal about the positive impact of policy measures taken by the government. Conclusion This study concludes that policy measures taken by the government are useful to some extent in controlling the coronavirus pandemic. As this pandemic lingers, people may lose their patience to stay at home. Consequently, some of the policies need further correction and change.

The COVID-19 basic reproductive ratio using SEIR model for the Middle East countries and some other countries for two stages of the disease

“…Some models take the mortality into accounts such as the SIRD model (Susceptible cases of the pandemic individual, Infectious cases of the pandemic individual, Recovered cases of the pandemic individual and Deceased cases of the pandemic individual), and others take the vaccines into accounts such as the SIRV model (Susceptible cases of the pandemic individual, Infectious cases of the pandemic individual, Recovered cases of the pandemic individual and Vaccinated cases of the pandemic individual). The first derivation of epidemiologic models was by Kermack ( 1927 ), Kamara et al ( 2020 ) found analytical solution for post death transmission model for Ebola epidemics, Khan et al ( 2020 ) discussed the effects of underlying morbidities on the occurrence of deaths for the new coronavirus disease patients, Lifshits and Neklyudova ( 2020 ) discussed mortality rate in Russian regions for the new coronavirus disease, Adedire and Ndam ( 2021 ) applied a model of dual latency compartments for the transmission dynamics of the new coronavirus disease in Nigeria, Neto et al ( 2020 ) discussed the effect of the new coronavirus disease with the fourth industrial revolution, Osemwinyen and Diakhaby ( 2015 ) discussed the transmission of Ebola disease with the models, Rejaur-Rahman et al ( 2020 ) applied geospatial modelling for the new coronavirus disease, Akanda and Ahmed ( 2020 ) discussed the controlling of the disease in Bangladesh regions, Roy et al ( 2020 ) discussed the spatial predication using ARIMA (autoregressive integrated moving average) model, Santosh ( 2020 ) discussed the predication models for the new coronavirus disease with unexploited data, Kadi and Khelfaoui ( 2020 ) and Lounis and Al-Raeei ( 2021 ) applied the models for the spreading of the new coronavirus disease for Algeria, Aabed and Lashin Maha ( 2020 ) applied analytical study of the factors that influence the new coronavirus disease spread, Ali et al ( 2020 ) discussed the linkage between PM 2.5 levels and the new coronavirus disease spread and its implications for socioeconomic circles, Al-Raeei ( 2020a , b , 2021 ) found the indicators of the new coronavirus disease for different location countries over the worldwide, Bhadra et al ( 2020 ) discussed the spreading of the new coronavirus disease with mortality in Indian regions, Fang et al ( 2020 ) applied ARIMA model for Russian regions, Gao et al ( 2007 ) applied SIR model with pulse vaccination and distributed time delay, Gupta et al ( 2020 ) discussed the effects of geographical factors to the new coronavirus disease outbreak in India, Zhu et al ( 2019 ) investigated the spreading process of the epidemics on multiplex networks by incorporating fatal properties, and Aidoo et al ( 2021 ) discussed the effects of the weather on the spreading of the new coronavirus disease in Ghana. In th...…”

Stigma and Discrimination Against COVID-19 Positive Patients in Bangladesh: A Comparative Qualitative Study of Non-Vaccinated and Vaccinated Patients

“…One of the measures that gained signi cant acceptance involved the implementation of a stay-at-home (often called lockdown) order for workplaces nationwide on March 16, which remained in effect until May 30, 2020 (Akanda & Ahmed, 2020). From March 16, 2020, until September 11, 2021, all educational institutions were closed to avoid face-to-face interactions (Akhter et al, 2021;Roberton et al, 2020).…”

Assessing the Dynamic Outcomes of Containment Strategies against COVID-19 under Different Public Health Governance Structures: A Comparison between Pakistan and Bangladesh

“…Masud et al used the SEIQR (Susceptible-Exposed-Infectious-Isolated-Recovered) model to understand and discuss the COVID-19 outbreak scenarios in Bangladesh [ 39 ]. Akanda et al examined the pandemic dynamics and the effectiveness of policy measures in Bangladesh by using the SEIRD (Susceptible-Exposed-Infectious-Recovered-Death) model [ 40 ]. Truelove et al used a stochastic SEIR model to simulate COVID-19’s outbreak and spread in refugee camps in Bangladesh and beyond [ 41 ].…”