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Mathematical modeling of SARS-CoV-2 spread in Chile reveals three contagion waves. Lower public risk perception and reduced personal protective equipment (PPE) use correlate with higher infection rates and mortality.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • The SARS-CoV-2 pandemic necessitated widespread safety measures, with varying public adherence to protocols like personal protective equipment (PPE) use.
  • Understanding the interplay between biological and behavioral factors is crucial for managing viral outbreaks.

Purpose of the Study:

  • To develop a mathematical model simulating SARS-CoV-2 contagion dynamics in Chile.
  • To assess the interaction between biological factors (e.g., vaccination) and behavioral factors (e.g., risk perception) on virus spread.

Main Methods:

  • Development of a mathematical contagion model.
  • Utilized Chilean epidemiological data for model calibration and analysis.
  • Simulated various scenarios by altering risk perception and PPE usage variables.

Main Results:

  • The model consistently predicted three distinct waves of SARS-CoV-2 contagion, with the second wave being the most significant.
  • The timing and intensity of these waves were largely independent of variable alterations, which primarily influenced total infection numbers.
  • Reduced population risk perception and lower willingness to use PPE were directly associated with increased contagion waves and mortality.

Conclusions:

  • Behavioral factors, specifically risk perception and PPE adoption, significantly impact SARS-CoV-2 transmission dynamics and outcomes.
  • Mathematical modeling provides valuable insights into managing infectious disease outbreaks by integrating biological and behavioral data.
  • Public health strategies should consider behavioral interventions to mitigate the impact of viral contagions.