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Prediction of Epidemics Trend of COVID-19 in Bangladesh

机译:孟加拉国Covid-19的流行病趋势预测

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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.
机译:背景:在冠状病毒疾病(Covid-19)与极端健康和经济影响相关的危急和紧急情况下,我们使用并呈现了易感传染恢复的数学建模(先生),以有一个可以脱落的数字示范光线决定孟加拉国祸害的命运。为了描述影响爆发数据的因素的想法,我们介绍了Covid-19爆发的当前情况与图形趋势。方法:通过使用预先创建的谷歌调查表格收集并分析了主要数据,并在不同地区的社会疏远地位上设有预先创建的谷歌调查表格。从公共可用来源提取了总计和每日数量的实验室检验,确认阳性病例和死亡病例的二级数据,以便预测。我们估计了基于SIR数学模型的基本再生号(R?),并预测了孟加拉国这个大流行的可能命运。结果:孟加拉国不同地区的检疫情况被评估并呈现。我们还向2020年5月31日之前提供了暂定预测,发现预测曲线遵循实际曲线约。估计r? -Values(6.924)表明感染率大于回收率。此外,通过校准SIR模型的参数来满足报告的数据,我们在2022年12月之前担任孟加拉国大流行的最终结束。结论:我们希望我们分析的结果可能有助于阐明这一点的关键方面爆发并帮助有关的权威决策。

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