Skip to content
naulon
Not checkedNot checked for notices yet.

Prediction of Epidemics Trend of COVID-19 in Bangladesh

Raguib Hassan, Abu Sayem Dosar, Joytu Kumar Mondol and 5 more

Frontiers in Public Health · 2020

doi:10.3389/fpubh.2020.559437 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

Background: Amid a critical and emergent situation like the coronavirus disease (COVID-19) pandemic related to extreme health and economic repercussions, we used and presented the mathematical modeling like susceptible–infectious–recovered (SIR) to have a numerical demonstration that can shed light to decide the fate of the scourge in Bangladesh. To describe the idea about the factors influencing the outbreak data, we presented the current situation of the COVID-19 outbreak with graphical trends.Methods: Primary data were collected and analyzed by using a pre-created Google Survey form having a pre-set questionnaire on the social distancing status of different districts. Secondary data on the total and the daily number of laboratory tests, confirmed positive cases, and death cases were extracted from the publicly available sources to make predictions. We estimated the basic reproduction number (R◦) based on the SIR mathematical model and predicted the probable fate of this pandemic in Bangladesh.Results: Quarantine situations in different regions of Bangladesh were evaluated and presented. We also provided tentative forecasts until 31 May 2020 and found that the predicted curve followed the actual curve approximately. Estimated R◦-values (6.924) indicated that infection rate would be greater than the recovery rate. Furthermore, by calibrating the parameters of the SIR model to fit the reported data, we assume the ultimate ending of the pandemic in Bangladesh by December 2022.Conclusion: We hope that the results of our analysis could contribute to the elucidation of critical aspects of this outbreak and help the concerned authority toward decision making.

Abstract licence: CC BY

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.