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

IF 2.3 4区医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

International Health Pub Date : 2025-01-09 DOI:10.1093/inthealth/ihae065

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

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引用次数: 0

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.

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机器学习对尼日利亚COVID-19非药物干预措施有效性的见解。

背景：在COVID-19大流行的早期阶段，缺乏有效的药物措施，促使实施非药物干预措施（npi）作为最初的缓解策略。在尼日利亚，这些国家行动方案对COVID-19的影响没有得到充分记录。这项研究旨在评估国家行动计划在支持未来流行病应对方面的有效性。方法：使用平滑变量分析每日COVID-19病例和死亡人数，以确定传播趋势。采用回归分析和聚类算法评估各NPI的影响。结果：报告了多个传播高峰，在2021年12月29日左右观察到最高的每日平滑新病例（约1790例），在2021年9月8日左右观察到最高的每日平滑新死亡（约23例）。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npi与病例数减少密切相关。这一发现强调了在疫情期间控制流动和非必要的工作场所管理在减缓感染传播方面的重要性。公共交通限制（系数值-2.43）结论：公共交通限制和工作场所关闭与病例和死亡人数的减少相关。这些发现可以指导未来的大流行应对工作，以加强有利的公共卫生成果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

4.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： International Health is an official journal of the Royal Society of Tropical Medicine and Hygiene. It publishes original, peer-reviewed articles and reviews on all aspects of global health including the social and economic aspects of communicable and non-communicable diseases, health systems research, policy and implementation, and the evaluation of disease control programmes and healthcare delivery solutions. It aims to stimulate scientific and policy debate and provide a forum for analysis and opinion sharing for individuals and organisations engaged in all areas of global health.