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Sparse HP filter: Finding kinks in the COVID-19 contact rate.

Sokbae Lee1,2, Yuan Liao3, Myung Hwan Seo4

  • 1Department of Economics, Columbia University, 420 West 118th Street, New York, NY 10027, USA.

Journal of Econometrics
|October 5, 2020
PubMed
Summary

This study introduces a sparse HP filter to estimate time-varying COVID-19 contact rates using case data. The method effectively tracks outbreak events and proposes using contact growth rates for monitoring.

Keywords:
COVID-19Hodrick–Prescott filterKnotsPiecewise linear fittingTrend filtering

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Time Series Analysis

Background:

  • Accurate estimation of time-varying transmission dynamics is crucial for understanding and controlling infectious disease outbreaks.
  • Existing methods for estimating contact rates in epidemiological models may lack the precision to capture real-world event-driven changes.
  • The Susceptible-Infected-Recovered (SIR) model is a fundamental framework for studying disease spread.

Purpose of the Study:

  • To develop and validate a novel filtering technique for estimating the time-varying contact rate within a SIR model for COVID-19.
  • To compare the performance of the proposed method against existing trend filtering techniques.
  • To establish a method for documenting and monitoring COVID-19 outbreaks using contact growth rates.

Main Methods:

  • Development of the 'sparse HP filter,' a variant of the Hodrick-Prescott filter constrained by the number of potential kinks.
  • Application of the sparse HP filter to daily COVID-19 case data (infected, recovered, deceased) from Canada, China, South Korea, the UK, and the US.
  • Theoretical establishment of risk consistency for both the sparse HP filter and a comparative trend filter.

Main Results:

  • The sparse HP filter successfully identified key events influencing COVID-19 transmission dynamics across five countries.
  • The proposed filter produced fewer kinks than a standard trend filter while maintaining comparable data-fitting performance.
  • Both the sparse HP filter and the comparative trend filter demonstrated theoretical risk consistency.

Conclusions:

  • The sparse HP filter offers a robust method for estimating time-varying COVID-19 contact rates.
  • The alignment of filter-identified kinks with real-world events validates its utility in outbreak analysis.
  • Time-varying contact growth rates derived from this method can serve as a valuable tool for monitoring and documenting infectious disease outbreaks.