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Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors.

Abhishek M DedheAakash A ChowkaseNiramay V GogateManas M KshirsagarRohan NaphadeAtharv NaphadePranav KulkarniMrunmayi NaikAarya DharmSoham RasteShravan PatankarChinmay M JogdeoAalok SatheSoham KulkarniVibha BapatRohinee JoshiKshitij DeshmukhSubhash LeleKody J Manke-MillerJessica F CantlonPranav S Pandit
Published in: Scientific reports (2024)
Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the "wisdom of crowds" public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.
Keyphrases
  • coronavirus disease
  • sars cov
  • healthcare
  • cardiovascular events
  • big data
  • mental health
  • risk factors
  • emergency department
  • machine learning
  • coronary artery disease
  • respiratory syndrome coronavirus