Modeling a traffic light warning system for acute respiratory infections

Saul Diaz-Infante, M. Adrian Acuña-Zegarra*, Jorge X. Velasco-Hernández

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The high morbidity of acute respiratory infections constitutes a crucial global health burden. In particular, for SARS-CoV-2, non-pharmaceutical intervention geared to enforce social distancing policies, vaccination, and treatments will remain an essential part of public health policies to mitigate and control disease outbreaks. However, the implementation of mitigation measures directed to increase social distancing when the risk of contagion is a complex enterprise because of the impact of NPI on beliefs, political views, economic issues, and, in general, public perception. The way of implementing these mitigation policies studied in this work is the so-called traffic-light monitoring system that attempts to regulate the application of measures that include restrictions on mobility and the size of meetings, among other non-pharmaceutical strategies. Balanced enforcement and relaxation of measures guided through a traffic-light system that considers public risk perception and economic costs may improve the public health benefit of the policies while reducing their cost. We derive a model for the epidemiological traffic-light policies based on the best response for trigger measures driven by the risk perception of people, instantaneous reproduction number, and the prevalence of a hypothetical acute respiratory infection. With numerical experiments, we evaluate and identify the role of appreciation from a hypothetical controller that could opt for protocols aligned with the cost due to the burden of the underlying disease and the economic cost of implementing measures. As the world faces new acute respiratory outbreaks, our results provide a methodology to evaluate and develop traffic light policies resulting from a delicate balance between health benefits and economic implications.

Original languageEnglish
Pages (from-to)217-230
Number of pages14
JournalApplied Mathematical Modelling
StatePublished - Sep 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc.


  • Best response
  • Control
  • Epidemiological traffic light
  • Mathematical model
  • Risk perception


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