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机器学习对尼日利亚非药物干预措施抗击新冠肺炎有效性的见解。

Machine learning insights on the effectiveness of non-pharmaceutical interventions against COVID-19 in Nigeria.

作者信息

Oyebola Kolapo M, Ligali Funmilayo C, Owoloye Afolabi J, Erinwusi Blessing D, Musa Adesola Z, Aina Oluwagbemiga O, Salako Babatunde L

机构信息

Centre for Genomic Research in Biomedicine, Mountain Top University, Ibafo, Nigeria.

Nigerian Institute of Medical Research, Lagos, Nigeria.

出版信息

Int Health. 2025 Sep 3;17(5):809-819. doi: 10.1093/inthealth/ihae065.

Abstract

BACKGROUND

The lack of effective pharmacological measures during the early phase of the COVID-19 pandemic prompted the implementation of non-pharmaceutical interventions (NPIs) as initial mitigation strategies. The impact of these NPIs on COVID-19 in Nigeria is not well-documented. This study sought to assess the effectiveness of NPIs to support future epidemic responses.

METHODS

Daily COVID-19 cases and deaths were analysed using smoothed variables to identify transmission trends. Regression analysis and clustering algorithms were applied to evaluate the impact of each NPI.

RESULTS

Multiple transmission peaks were reported, with the highest smoothed daily new cases (approximately 1790) observed around 29 December 2021 and smoothed daily new deaths (approximately 23) peaking around 8 September 2021. NPIs such as public transport (coefficient value -166.56, p=0.01) and workplace closures (coefficient value -150.06, p=0.01) strongly correlated with decreased case numbers. This finding highlights the importance of mobility control and non-essential workplace management in slowing infection transmission during an outbreak. Public transport restrictions (coefficient value -2.43, p<0.001) also had a direct effect on death reduction.

CONCLUSIONS

Public transport restrictions and workplace closures correlated with reductions in the number of cases and deaths. These findings can guide future pandemic responses to enhance favourable public health outcomes.

摘要

背景

在新冠疫情早期阶段,由于缺乏有效的药物治疗措施,促使实施非药物干预措施(NPIs)作为初始缓解策略。这些非药物干预措施对尼日利亚新冠疫情的影响尚无充分记录。本研究旨在评估非药物干预措施的有效性,以支持未来的疫情应对。

方法

使用平滑变量分析每日新冠病例和死亡数据,以确定传播趋势。应用回归分析和聚类算法评估每项非药物干预措施的影响。

结果

报告了多个传播高峰,2021年12月29日左右观察到最高的平滑每日新增病例数(约1790例),2021年9月8日左右平滑每日新增死亡数(约23例)达到峰值。公共交通(系数值-166.56,p=0.01)和工作场所关闭(系数值-150.06,p=0.01)等非药物干预措施与病例数减少密切相关。这一发现凸显了在疫情爆发期间控制流动性和管理非必要工作场所在减缓感染传播方面的重要性。公共交通限制(系数值-2.43,p<0.001)对死亡人数减少也有直接影响。

结论

公共交通限制和工作场所关闭与病例数和死亡人数的减少相关。这些发现可为未来的疫情应对提供指导,以提高有利的公共卫生结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7aa3/12406770/ef1fbeb23acc/ihae065fig1.jpg

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