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How optimal allocation of limited testing capacity changes epidemic dynamics.

Justin M Calabrese1, Jeffery Demers2

  • 1Center for Advanced Systems Understanding (CASUS), Goerlitz, Germany; Dept. of Biology, University of Maryland, College Park, MD, USA.

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Summary

Optimizing COVID-19 testing is crucial. A modified SEIR model shows that while clinical testing is best for low capacity, a mix of clinical and non-clinical testing is optimal as capacity increases.

Keywords:
COVID-19EpidemiologyOptimal controlSARS-CoV-2SEIR model

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Area of Science:

  • Epidemiology
  • Public Health Policy
  • Mathematical Modeling

Background:

  • Limited testing capacity presents a significant challenge in managing the COVID-19 pandemic.
  • Effective pandemic response planning necessitates optimizing the allocation of scarce testing resources.

Purpose of the Study:

  • To optimize COVID-19 testing strategies using a modified SEIR model under limited testing capacity.
  • To determine the optimal balance between clinical and non-clinical testing based on capacity and focus.

Main Methods:

  • Utilized a modified SEIR (Susceptible-Exposed-Infectious-Recovered) model incorporating pre-symptomatic, asymptomatic, and symptomatic infected classes.
  • Modeled two testing strategies: clinical (symptomatic only) and non-clinical (pre- and asymptomatic).
  • Analyzed the impact of a concentration parameter for non-clinical testing on high-risk individuals.

Main Results:

  • Purely clinical testing is optimal only at very low testing capacities.
  • A combination of clinical and non-clinical testing becomes optimal as testing capacity increases.
  • Even unfocused non-clinical testing combined with clinical testing is optimal at high, empirically observed capacities.

Conclusions:

  • The optimal COVID-19 testing strategy shifts from purely clinical to a mixed approach as testing capacity grows.
  • Early implementation of testing programs and integration with non-pharmaceutical interventions (e.g., lockdowns, masking) are advantageous.
  • Resource allocation for testing should adapt to available capacity and population risk factors.