Public health factors help explain cross country heterogeneity in excess death during the COVID19 pandemic
Open accessAbstract
The COVID-19 pandemic has taken a devastating toll around the world. Since January 2020, the World Health Organization estimates 14.9 million excess deaths have occurred globally. Despite this grim number quantifying the deadly impact, the underlying factors contributing to COVID-19 deaths at the population level remain unclear. Prior studies indicate that demographic factors like proportion of population older than 65 and population health explain the cross-country difference in COVID-19 deaths. However, there has not been a comprehensive analysis including variables describing government policies and COVID-19 vaccination rate. Furthermore, prior studies focus on COVID-19 death rather than excess death to assess the impact of the pandemic. Through a robust statistical modeling framework, we analyze 80 countries and show that actionable public health efforts beyond just the factors intrinsic to each country are important for explaining the cross-country heterogeneity in excess death.
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Manual segmentation of medical images is labor-intensive, time-consuming, and subject to inter-observer variability, making it impractical for large-scale clinical applications.
“However, manual segmentation of medical images is laborintensive, time-consuming, and subject to inter-observer variability, making it impractical for large-scale clinical applications [1, 2].”
From Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search (2026) · cites this paper as [1]
The Methods passages discuss modeling COVID-19 excess deaths, and the Results passages discuss predictive performance and feature importance; none addresses manual segmentation of medical images. Searches for medical-image segmentation, inter-observer variability, and the claimed workload and clinical-scale limitations found no relevant passage in our copy. (LLM, conf=0.78)
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Cited by
Sorted by citations
Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search
Meng, L., Nie, F., Zhang, Y., & Han, C. (2026) · arXiv · doi:10.48550/arxiv.2602.22361
“However, manual segmentation of medical images is labor-intensive, time-consuming, and subject to inter-observer variability, making it impractical for large-scale clinical applications [1, 2].”
- Authors
- Sun, M. W., Troxell, D., & Tibshirani, R.
- Affiliations
- Stanford University (US)
- Corresponding author
- Sun, Min Woo
- Venue
- Scientific Reports
- Published
- 2023-09-27
- Volume
- 13
- Issue
- 1
- Pages
- 16196-16196
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10533501
- OpenAlex
- W4387105844
- Cited by
- 7
- References
- 1
- Citations by year
- 2026: 2 · 2024: 5
- FWCI
- 1.47
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. National Library of Medicine (2T15LM00703339)
- National Institutes of Health (5R01 EB001988-16)
- National Science Foundation (19 DMS1208164)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Legal, Health, Environmental and COVID-19 Challenges
- Keywords
- COVID-19 pandemic, excess mortality, cross-country heterogeneity, population aging, population health, pandemic policy, COVID-19 vaccination rates, demographic factors
- MeSH terms
- COVID-19, COVID-19 Vaccines, Government, Humans, Public Health, Pandemics
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