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Modeling and control of epidemics through testing policies.

Muhammad Umar B Niazi1, Alain Kibangou1, Carlos Canudas-de-Wit1

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This study introduces a control-theoretic epidemic model for COVID-19 testing. Two strategies, BEST and COST, were developed to manage disease spread and minimize peak infections, offering insights for public health policy.

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Area of Science:

  • Epidemiology
  • Control Theory
  • Public Health

Background:

  • Testing is vital for epidemic control, especially before vaccines are available.
  • Existing literature lacks a control-theoretic perspective on epidemic testing strategies.
  • Early epidemic phases require effective methods to detect and isolate infected individuals.

Purpose of the Study:

  • To propose a novel epidemic model incorporating testing rate as a control input.
  • To develop and evaluate two distinct testing policies: BEST and COST.
  • To analyze the impact of these policies on intensive care unit (ICU) cases and mortality.

Main Methods:

  • Developed an epidemic model differentiating detected and undetected infected cases.
  • Incorporated testing rate as a controllable input in the model.
  • Estimated the model using early COVID-19 data from France.
  • Proposed and simulated the BEST (suppression) and COST (mitigation) testing policies.

Main Results:

  • The BEST policy demonstrated a minimum testing rate to halt epidemic growth.
  • The COST policy identified an optimal testing rate to minimize peak infections under test limitations.
  • Both policies were evaluated for their effectiveness in reducing ICU cases and cumulative deaths in France.

Conclusions:

  • A control-theoretic framework can effectively model and optimize epidemic testing strategies.
  • The BEST and COST policies offer valuable approaches for managing infectious disease outbreaks.
  • Testing strategies significantly impact disease transmission, ICU burden, and mortality rates.