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Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics.运用逻辑模型和机器学习技术预测新冠疫情趋势。
Chaos Solitons Fractals. 2020 Oct;139:110058. doi: 10.1016/j.chaos.2020.110058. Epub 2020 Jul 1.
2
Long-term and herd immunity against SARS-CoV-2: implications from current and past knowledge.长期和群体免疫对 SARS-CoV-2 的影响:基于当前和既往知识的推断。
Pathog Dis. 2020 Apr 1;78(3). doi: 10.1093/femspd/ftaa025.
3
ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA.利用有限数据对尼日利亚新冠肺炎病例进行在线预测
Data Brief. 2020 May 8;30:105683. doi: 10.1016/j.dib.2020.105683. eCollection 2020 Jun.

塞浦路斯应对 COVID-19:政策变化与疫情趋势。

Response to COVID-19 in Cyprus: Policy changes and epidemic trends.

机构信息

Department of Respiratory Medicine, University of Patras General Hospital, Patras, Greece.

Department of Health Sciences, University of Ulster, Belfast, UK.

出版信息

Int J Clin Pract. 2021 Apr;75(4):e13944. doi: 10.1111/ijcp.13944. Epub 2021 Jan 3.

DOI:10.1111/ijcp.13944
PMID:33338320
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7883066/
Abstract

OBJECTIVES

In late July, Cyprus experienced the second epidemic wave of COVID-19. We present the steps taken by the government and evaluate their effect on epidemic trends.

MATERIALS

Cyprus Press and Information Office data were analysed. Using an R-based forecasting program, two models were created to predict cases up to 01/09/2020: Model 1, which utilised data up to 09/06/2020, when airports reopened to foreign travelers with COVID-19 screening; and Model 2, which utilised data until 24/06/2020, when screening for passengers from low-transmission countries was discontinued.

RESULTS

PIO data revealed no significant policy changes between 24/06/2020 and 31/07/2020. Prediction models were robust and accurate (Model 1, R  = 0.999, P < .001; Model 2, R  = 0.998, P < .001). By August 30th, recorded cases exceeded those predicted by Model 1 by 24.47% and by Model 2 by 20.95%, with P values <.001 for both cases.

CONCLUSIONS

The significant difference between recorded cases and those projected by Models 1 and 2 suggests that changes in epidemic trends may have been associated with policy changes after their respective dates. Discontinuation of major restrictions such as airport reopening, can destabilise the control of the epidemic, and may concomitantly necessitate a reevaluation of the current epidemic status. In the face of an evolving situation such as the COVID-19 pandemic, states are forced to balance the imposing of restrictions against their impact on the economy.

摘要

目的

7 月下旬,塞浦路斯经历了第二波 COVID-19 疫情。我们介绍了政府采取的措施,并评估了这些措施对疫情趋势的影响。

材料

分析了塞浦路斯新闻和信息办公室的数据。使用基于 R 的预测程序,创建了两个模型来预测截至 2020 年 9 月 1 日的病例数:模型 1 利用截至 2020 年 9 月 6 日的数据,当时机场重新向有 COVID-19 筛查的外国旅行者开放;模型 2 利用截至 2020 年 6 月 24 日的数据,当时停止对来自低传播国家的乘客进行筛查。

结果

PIO 数据显示,2020 年 6 月 24 日至 7 月 31 日期间没有重大政策变化。预测模型稳健且准确(模型 1,R 为 0.999,P <.001;模型 2,R 为 0.998,P <.001)。到 8 月 30 日,记录的病例数比模型 1 预测的病例数多 24.47%,比模型 2 预测的病例数多 20.95%,两个模型的 P 值均<.001。

结论

记录的病例数与模型 1 和 2 预测的病例数之间的显著差异表明,疫情趋势的变化可能与各自日期之后的政策变化有关。取消机场重新开放等主要限制可能会破坏疫情的控制,并可能需要重新评估当前的疫情状况。在 COVID-19 大流行等不断变化的情况下,各国被迫在实施限制措施及其对经济的影响之间取得平衡。