COVID-19 and other viruses: Holding back its spreading by massive testing

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health Policy

Background:

• COVID-19 pandemic highlighted the need for effective containment strategies.
• Nations like Singapore and South Korea demonstrated success with widespread testing.
• Existing epidemiological models often lack detailed incorporation of testing and tracing.

Purpose of the Study:

• To develop and validate a modified SEIR model incorporating contact tracing and massive testing.
• To theoretically demonstrate the impact of increased tracing and testing on infection reduction.
• To propose a heuristic approach for optimizing test distribution to minimize disease spread.

Main Methods:

• Modification of the standard SEIR (Susceptible-Exposed-Infectious-Recovered) epidemiological model.
• Inclusion of parameters for contact tracing efficiency and testing capacity.
• Application of the model to the first wave of COVID-19 in Spain for numerical validation.
• Development of a heuristic algorithm for planning regional test distributions.

Main Results:

• Theoretical proof that enhanced tracing and testing lead to a long-term decrease in total infections.
• Numerical results from applying the model to Spain's first COVID-19 wave.
• Quantification of the impact of optimized test distribution in New York counties, showing saved infected individuals.

Conclusions:

• Massive testing and robust contact tracing are crucial for controlling COVID-19 transmission.
• The modified SEIR model provides a valuable tool for understanding and predicting disease dynamics under these interventions.
• Heuristic-based test distribution planning can significantly enhance public health outcomes by minimizing infections.