A perspective on early detection systems models for COVID-19 spreading.

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• The COVID-19 pandemic underscores the need for advanced tools to predict infectious disease outbreaks.
• Early detection and tracking of contagion dynamics are vital for managing emerging infectious diseases (EID).

Purpose of the Study:

• To present a model-free framework using Early Warning Detection Systems (EWDS) for early detection of infection spread.
• To adapt and apply two distinct EWDS approaches (Hub-Jones and Strozzi-Zaldivar) to SARS-CoV-2 outbreak data.

Main Methods:

• Utilized publicly available SARS-CoV-2 spread data from the Italian Civil Protection Department.
• Applied and evaluated the Hub-Jones (H&J) and Strozzi-Zaldivar (S&Z) EWDS techniques.
• Theoretically validated the S&Z criterion using the epidemiological SIR model.

Main Results:

• EWDS promptly generated warning signals, detecting the epidemic onset at early surveillance stages.
• The methods effectively analyzed the self-accelerating SARS-CoV-2 spread, identifying epidemic onset parameters.
• Early clustering detection was facilitated, supporting EID control strategies.

Conclusions:

• EWDS provide an effective, scientifically grounded tool to complement medical interventions against infectious diseases like COVID-19.
• The framework enables precocious identification of epidemic spread and supports public health responses.