Lockdown, one, two, none, or smart. Modeling containing covid-19 infection. A conceptual model

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• COVID-19 outbreaks necessitate understanding disease transmission dynamics.
• Mathematical models are crucial for simulating infectious disease spread and evaluating control measures.
• Previous models often lack detailed representations of public health interventions like lockdowns.

Purpose of the Study:

• To develop a mathematical model simulating COVID-19 spread.
• To assess the impact of different lockdown scenarios on infection rates.
• To evaluate the effectiveness of quarantine strategies in mitigating outbreaks.

Main Methods:

• Utilized Systems Dynamics methodology for model creation.
• Extended a basic SIR (Susceptible-Infected-Recovered) model with variables for hospital capacity, contacts, and deaths.
• Incorporated piecewise functions to represent lockdowns and contact reduction effectiveness.

Main Results:

• Simulations demonstrated a decrease in infected individuals due to implemented quarantines.
• Model projected infection trends across three distinct lockdown scenarios: one extended, two medium with a smart lockdown interval, and an initial lockdown followed by a smart lockdown.
• All simulated lockdowns commenced 25 days post-first reported infection.

Conclusions:

• The developed model effectively captures COVID-19 outbreak dynamics.
• Lockdowns demonstrably reduce infection rates, aiding in outbreak control.
• The model provides valuable insights into optimizing quarantine strategies for public health.