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Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data.

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  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

Journal of the American Statistical Association
|May 19, 2023
PubMed
Summary

We developed a new model, MERMAID, to estimate COVID-19 prevalence and ascertainment rates, addressing challenges like under-reporting and data lags. This model provides crucial insights into infectious disease dynamics.

Keywords:
COVID-19 transmissionEM algorithmEffective reproductive numberEpidemic modelMissing dataPrevalenceSerological studiesUnder-ascertainment

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Mathematical Modeling

Background:

  • Accurate estimation of infectious disease parameters like incidence, prevalence, and the effective reproductive number (R-number) is vital, especially during pandemics.
  • Challenges in data collection, including under-ascertainment, reporting inconsistencies, and time lags, hinder precise estimations.

Purpose of the Study:

  • To introduce the Multilevel Epidemic Regression Model to Account for Incomplete Data (MERMAID) for jointly estimating key epidemiological parameters.
  • To enable flexible modeling of the R-number with geographic and time-varying covariates.
  • To improve estimations by accounting for under-ascertainment and data delays.

Main Methods:

  • MERMAID jointly models confirmed infections and serological survey data, incorporating ascertainment probability as a function of testing metrics.
  • Stochastic lag times between infection, onset, and reporting are modeled as missing data.
  • An Expectation-Maximization (EM) algorithm is employed for parameter estimation.

Main Results:

  • MERMAID demonstrated robust performance in simulation studies and sensitivity analyses.
  • Application to US COVID-19 data (March-December 2020) yielded an estimated overall prevalence of 12.5% and an ascertainment rate of 45.5%.
  • Significant regional variations in prevalence and ascertainment rates were observed across the United States.

Conclusions:

  • MERMAID offers a powerful framework for estimating infectious disease dynamics, effectively handling incomplete and delayed data.
  • The model provides valuable insights into the true extent of infections and testing effectiveness.
  • Findings highlight the importance of accounting for ascertainment bias and reporting delays in epidemiological modeling.