Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework
文献类型：期刊论文
| 作者 | Zhou, Cui1,6; Wheelock, asa M.2; Zhang, Chutian1,3,6; Ma, Jian1,6; Li, Zhichao4; Liang, Wannian1,6; Gao, Jing1,2,5,7; Xu, Lei1,6 |
| 刊名 | POPULATION HEALTH METRICS
 |
| 出版日期 | 2024-06-03 |
| 卷号 | 22期号:1页码:17 |
| 关键词 | COVID-19 Global health Strategy Vaccination Case fatality rate Pandemics XGBoost SHAP |
| ISSN号 | 1478-7954 |
| DOI | 10.1186/s12963-024-00330-4 |
| 英文摘要 | Background There are significant geographic inequities in COVID-19 case fatality rates (CFRs), and comprehensive understanding its country-level determinants in a global perspective is necessary. This study aims to quantify the country-specific risk of COVID-19 CFR and propose tailored response strategies, including vaccination strategies, in 156 countries. Methods Cross-temporal and cross-country variations in COVID-19 CFR was identified using extreme gradient boosting (XGBoost) including 35 factors from seven dimensions in 156 countries from 28 January, 2020 to 31 January, 2022. SHapley Additive exPlanations (SHAP) was used to further clarify the clustering of countries by the key factors driving CFR and the effect of concurrent risk factors for each country. Increases in vaccination rates was simulated to illustrate the reduction of CFR in different classes of countries. Findings Overall COVID-19 CFRs varied across countries from 28 Jan 2020 to 31 Jan 31 2022, ranging from 68 to 6373 per 100,000 population. During the COVID-19 pandemic, the determinants of CFRs first changed from health conditions to universal health coverage, and then to a multifactorial mixed effect dominated by vaccination. In the Omicron period, countries were divided into five classes according to risk determinants. Low vaccination-driven class (70 countries) mainly distributed in sub-Saharan Africa and Latin America, and include the majority of low-income countries (95.7%) with many concurrent risk factors. Aging-driven class (26 countries) mainly distributed in high-income European countries. High disease burden-driven class (32 countries) mainly distributed in Asia and North America. Low GDP-driven class (14 countries) are scattered across continents. Simulating a 5% increase in vaccination rate resulted in CFR reductions of 31.2% and 15.0% for the low vaccination-driven class and the high disease burden-driven class, respectively, with greater CFR reductions for countries with high overall risk (SHAP value > 0.1), but only 3.1% for the ageing-driven class. Conclusions Evidence from this study suggests that geographic inequities in COVID-19 CFR is jointly determined by key and concurrent risks, and achieving a decreasing COVID-19 CFR requires more than increasing vaccination coverage, but rather targeted intervention strategies based on country-specific risks. |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Public, Environmental & Occupational Health |
| 语种 | 英语 |
| WOS记录号 | WOS:001238230700001 |
| 出版者 | BMC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206794]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Liang, Wannian; Gao, Jing; Xu, Lei |
| 作者单位 | 1.Tsinghua Univ, Vanke Sch Publ Hlth, Beijing, Peoples R China 2.Karolinska Inst, Ctr Mol Med, Dept Med, Resp Med Unit, Stockholm, Sweden 3.Northwest A&F Univ, Coll Nat Resources & Environm, Yangling, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China 5.Univ Helsinki, Dept Resp Med, Helsinki, Finland 6.Tsinghua Univ, Inst Hlth China, Beijing, Peoples R China 7.Lanzhou Univ, Sch Clin Med 1, Lanzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Cui,Wheelock, asa M.,Zhang, Chutian,et al. Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework[J]. POPULATION HEALTH METRICS,2024,22(1):17. |
| APA | Zhou, Cui.,Wheelock, asa M..,Zhang, Chutian.,Ma, Jian.,Li, Zhichao.,...&Xu, Lei.(2024).Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework.POPULATION HEALTH METRICS,22(1),17. |
| MLA | Zhou, Cui,et al."Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework".POPULATION HEALTH METRICS 22.1(2024):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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