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Modeling and control of epidemics through testing policies
Muhammad Umar B Niazi1, Alain Kibangou1, Carlos Canudas-de-Wit1
1Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSA-Lab, 38000 Grenoble, France.
Annual Reviews in Control
|October 19, 2021
Summary
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.
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.
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