Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework

Abstract

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.

Keywords: COVID-19; Case fatality rate; Global health; Pandemics; SHAP; Strategy; Vaccination; XGBoost.

Fig. 1

Trends in and distributions of CFR. a Epidemiological curves of COVID-19 CFR by WHO region from 28 January 2020 to 31 January 2022. b Global distribution of CFR in the original, Alpha, Delta, and Omicron periods

Fig. 2

The importance of each factor affecting CFR and its effects in the original, Alpha, Delta, and Omicron periods. a ISs for each feature affecting CFR in each period model, obtained by taking the absolute mean of the SHAP values. The 35 features represent seven distinct dimensions: vaccination coverage, demographic factors, disease burden, behavioural risk factors, environmental risk factors, health services, and trust levels. b SHAP dependence plots for proportion of population aged over 65, booster vaccination rate, CVD, and GDP per capita in the XGBoost models. SHAP values above zero represent an increased risk of higher COVID-19 CFR. Abbreviations: IS, important score; LRI, lower respiratory infections; URI, upper respiratory infections; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular diseases; CKD, chronic kidney disease; HTN, hypertension; MD, mental disorders; NCD, noncommunicable diseases; HIV, HIV infection; TB, tuberculosis

Fig. 3

Country classification according to the most important risk factors and concurrent risks influencing COVID-19 CFR. a Grouping of countries into five classes based on the most important risk factors in the Omicron model. Class 1: low vaccine coverage; Class 2: ageing; Class 3: high disease burden; Class 4: low GDP; Class 5: other. b Percentage of countries with certain concurrent risks in each class of countries

Fig. 4

Overall risk and contributions of main risk factors to the CFR for each country in Classes 1-4. Country abbreviations use the ISO 3166 ALPHA-3 codes [44]

Fig. 5

Distribution of and cross-class differences in the change in CFR after a simulated 5% increase in vaccination. a Global distribution of the predicted change in CFR after a 5% increase in vaccination coverage. b Scatter plot showing the change in CFR following increased vaccination versus current booster vaccination rate for each country. The box plot shows the distribution of change in CFR for each cluster, with boxes indicating the median and 25th and 75th percentiles