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Advancing infection profiling under data uncertainty through contagion potential.

Satyaki Roy1, Preetom Biswas2, Preetam Ghosh3

  • 1Department of Mathematical Sciences, The University of Alabama in Huntsville, Huntsville, Alabama, United States of America.

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Estimating contagion potential (CP) from asymptomatic COVID-19 cases is challenging due to data limitations. This study introduces statistical methods to reliably estimate CP, improving epidemiological statistics for public health policy.

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Area of Science:

  • Epidemiology
  • Statistical Modeling
  • Public Health

Background:

  • Asymptomatic COVID-19 cases complicate accurate epidemiological statistics.
  • Contagion potential (CP) is a metric to quantify infection risk from asymptomatic individuals.
  • Estimating CP is difficult due to incomplete and biased incidence data.

Purpose of the Study:

  • To develop robust statistical methods for estimating contagion potential (CP) from incomplete and biased epidemiological data.
  • To address challenges in CP estimation caused by underreporting, testing constraints, and spatial sampling biases.
  • To provide reliable CP estimates for informing public health and outbreak mitigation strategies.

Main Methods:

  • Employed a hypothesis-testing approach to infer CP from sampled data.
  • Introduced an adjustment factor to calibrate sample CP estimates to population CP.
  • Utilized inverse probability weighting to correct for biases in epidemiological and mobility data.
  • Applied a spatial model for infection spread and an SIRS epidemic model optimization framework.
  • Analyzed real infection datasets from Italy, Germany, and Austria.

Main Results:

  • Demonstrated high-confidence CP estimates using statistical methods.
  • Showcased the ability to account for variations in sample size, confidence level, mobility models, and viral strains.
  • Identified and proposed statistical corrections for biases, social mixing, and sampling frequency effects on CP prediction accuracy.

Conclusions:

  • Reliable contagion potential (CP) estimation is achievable even with incomplete and biased epidemiological data.
  • Statistical corrections enhance CP prediction accuracy, crucial for effective outbreak mitigation.
  • Accurate CP estimates support informed public health policymaking during pandemics.