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Introduction: Coronavirus disease 2019 (COVID-19) has had a profound impact globally, causing the death of millions of people and deeply affecting socio-psychological, human health, and economic systems, with some nations bearing a disproportionate burden. Despite obesity having been established as one of the major risk factors of COVID-19 severity and other degenerative diseases, the effects that dietary pattern intake plays in COVID-19 outcomes remain poorly understood. The goal of this study is to look into the connection between eating habits, the number of non-obese and obese people, and COVID-19 outcomes in countries with populations exhibiting normal Body Mass Index (BMI), which is an indicator of obesity. Methods: The analysis includes data from 170 countries. From the 170 countries, we focused on 53 nations where the average, BMI falls within the normal range (18.5 to 24.9). A subset of 20 nations was selected for a more detailed examination, comprising 10 nations with the lowest BMI values within the normal range (18.5-19.8) and 10 nations with the highest BMI values within the normal range (23.5-24.9). We used Artificial Intelligence (AI) and Machine Learning (ML) applications to evaluate key metrics, including dietary patterns (sugar and vegetable intake), obesity prevalence, incidence rate, mortality rate, and Case Fatality Rate (CFR). Results: The results demonstrate a significant correlation between higher obesity prevalence and increased COVID-19 severity, evidenced by elevated incidence, mortality, and CFRs in countries like North Macedonia and Italy. In contrast, nations such as Iceland and New Zealand with well-established healthcare systems revealed low mortality rate and case fatality rate despite variations in dietary habits. The study also revealed that vegetable consumption appears to provide a slight to significant protective effects, suggesting that dietary patters alone do not consistently predict COVID-19 Outcomes. Conclusion: Data generated from this study showed the crucial role of healthcare infrastructure along with the testing capacity and data reporting in influencing the success of pandemic responses. It also highlights the need of integrating public health strategies, which focus on obesity management and improvement of healthcare preparedness. In addition, AI-driven predictive modeling offers valuable insights that may guide pandemic response efforts in the future, thereby enhancing global health crisis management and mitigating the impact of future health emergencies. Keywords: COVID-19; Dietary patterns; Obesity; Artificial intelligence; Machine learning; Public health; Health care systems
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This content will become publicly available on October 1, 2026
Estimating the infection fatality rate of emerging diseases using a regression approach applied to global COVID-19 cases
Background: Estimating the infection fatality rate (IFR) for emerging diseases is elusive due to the presence of asymptomatic or mildly symptomatic infections and variable testing capacity. IFR estimates are also affected by region-specific differences in sampling regimes, demographics, and healthcare resources. Methods: Here we present a novel regression approach using population testing and readily available case fatality rates (CFR) to estimate the IFR during an outbreak. The approach is based on few assumptions and can be used for a wide range of emerging diseases. We validate the use of the method using commonly reported COVID-19 testing data. Results: Our new statistical approach reveals a conservative global IFR of 0.90 % (CI: 0.70 %, 1.16 %) for COVID-19 across the 139 countries affected before May 2020. Deviation of countries’ reported CFR from the estimator did not correlate with demography, per capita GDP, or healthcare access and quality, suggesting variation is due to differing testing regimes or reporting guidelines by country. Conclusions: This method can be used retrospectively or for future disease outbreaks when other data are limited.
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- Award ID(s):
- 2011179
- PAR ID:
- 10626607
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Journal of Infection and Public Health
- Volume:
- 18
- Issue:
- 10
- ISSN:
- 1876-0341
- Page Range / eLocation ID:
- 102856
- Subject(s) / Keyword(s):
- Asymptomatic Linear regression Pandemic SARS-COV-2 Seroprevalence Testing capacity
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Introduction: Coronavirus disease 2019 (COVID-19) has had a profound impact globally, causing the death of millions of people and deeply affecting socio-psychological, human health, and economic systems, with some nations bearing a disproportionate burden. Despite obesity having been established as one of the major risk factors of COVID-19 severity and other degenerative diseases, the effects that dietary pattern plays in COVID-19 outcomes remain poorly understood. The goal of this study is to look into the connection between eating habits, the number of non-obese and obese people, and COVID-19 outcomes in countries with populations exhibiting normal Body Mass Index (BMI), which is an indicator of obesity. Methods: The analysis includes data from 170 countries. From the 170 countries, we focused on 53 nations where the average, BMI falls within the normal range (18.5 to 24.9). A subset of 20 nations was selected for a more detailed examination, comprising 10 nations with the lowest BMI values within the normal range (18.5-19.8) and 10 nations with the highest BMI values within the normal range (23.5-24.9). We used Artificial Intelligence (AI) and Machine Learning (ML) applications to evaluate key metrics, including dietary patterns (sugar and vegetable intake), obesity prevalence, incidence rate, mortality rate, and Case Fatality Rate (CFR). Results: The results demonstrate a significant correlation between higher obesity prevalence and increased COVID-19 severity, evidenced by elevated incidence, mortality, and CFRs in countries like North Macedonia and Italy. In contrast, nations such as Iceland and New Zealand with well-established healthcare systems revealed low mortality rates and case fatality rates despite variations in dietary habits. The study also revealed that vegetable consumption appears to provide a slight to significant protective effect, suggesting that dietary patterns alone do not consistently predict COVID-19 Outcomes. Conclusion: Data generated from this study showed the crucial role of healthcare infrastructure along with the testing capacity and data reporting in influencing the success of pandemic responses. It also highlights the need to integrate public health strategies, which focus on obesity management and improvement of healthcare preparedness. In addition, AI-driven predictive modeling offers valuable insights that may guide pandemic response efforts in the future, thereby enhancing global health crisis management and mitigating the impact of future health emergencies.more » « less
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