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基于生殖数(R)对卡纳塔克邦 COVID-19 感染的日和累计病例的预测:数据驱动的分析。

Prediction of daily and cumulative cases for COVID-19 infection based on reproductive number (R) in Karnataka: a data-driven analytics.

机构信息

Department of Spatial Epidemiology, ICAR-National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, Karnataka, India.

Department of Virology, ICAR-National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, Karnataka, India.

出版信息

Sci Rep. 2021 May 12;11(1):10075. doi: 10.1038/s41598-021-89573-x.

Abstract

To estimate the reproductive number (R) of the coronavirus in the present scenario and to predict the incidence of daily and probable cumulative cases, by 20 August, 2020 for Karnataka state in India. The model used serial interval with a gamma distribution and applied 'early R' to estimate the R and 'projections' package in R program. This was performed to mimic the probable cumulative epidemic trajectories and predict future daily incidence by fitting the data to existing daily incidence and the estimated R by a model based on the assumption that daily incidence follows Poisson distribution. The maximum-likelihood (ML) value of R was 2.242 for COVID-19 outbreak, as on June 2020. The median with 95% CI of R values was 2.242 (1.50-3.00) estimated by bootstrap resampling method. The expected number of new cases for the next 60 days would progressively increase, and the estimated cumulative cases would reach 27,238 (26,008-28,467) at the end of 60th day in the future. But, if R value was doubled the estimated total number of cumulative cases would increase up to 432,411 (400,929-463,893) and if, R increase by 50%, the cases would increase up to 86,386 (80,910-91,861). The probable outbreak size and future daily cumulative incidence are largely dependent on the change in R values. Hence, it is vital to expedite the hospital provisions, medical facility enhancement work, and number of random tests for COVID-19 at a very rapid pace to prepare the state for exponential growth in next 2 months.

摘要

为了估计当前情况下冠状病毒的繁殖数 (R),并预测印度卡纳塔克邦 2020 年 8 月 20 日之前的每日和可能的累计病例数。该模型使用具有伽马分布的序列间隔,并应用“早期 R”来估计 R 和 R 程序中的“预测”包。这是为了模拟可能的累积疫情轨迹,并通过将数据拟合到现有的每日发病率和基于假设每日发病率遵循泊松分布的模型估计的 R 来预测未来的每日发病率。截至 2020 年 6 月,COVID-19 爆发时 R 的最大似然 (ML) 值为 2.242。通过自举重采样方法估计 R 值的中位数及其 95%置信区间为 2.242 (1.50-3.00)。预计未来 60 天内新病例的数量将逐渐增加,预计到第 60 天结束时,累计病例将达到 27238 例(26008-28467)。但是,如果 R 值增加一倍,估计的累计病例总数将增加到 432411 例(400929-463893),如果 R 值增加 50%,病例将增加到 86386 例(80910-91861)。可能的疫情规模和未来的每日累计发病率在很大程度上取决于 R 值的变化。因此,加快医院供应、医疗设施增强工作以及 COVID-19 的随机检测数量非常重要,以便为未来 2 个月的指数增长做好准备。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b127/8115614/b202e4383474/41598_2021_89573_Fig1_HTML.jpg

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