A two-phase dynamic contagion model for COVID-19.

Area of Science:

• Epidemiology
• Mathematical Biology
• Stochastic Processes

Background:

• Standard epidemic models often assume constant infectivity, which may not accurately reflect real-world disease dynamics.
• COVID-19 (coronavirus disease 2019) presents complex transmission patterns necessitating advanced modeling approaches.

Purpose of the Study:

• To introduce a continuous-time stochastic intensity model, the two-phase dynamic contagion process (2P-DCP), for COVID-19.
• To investigate the impact of interventions like lockdowns using a dynamic contagion framework.
• To estimate key epidemiological quantities and intervention time lags.

Main Methods:

• Developed a continuous-time stochastic intensity model (2P-DCP).
• Incorporated randomness into individual infectivity, moving beyond constant reproduction numbers.
• Derived and estimated epidemiological metrics like final epidemic size and duration using real-world data.

Main Results:

• The 2P-DCP model successfully estimates epidemic size, duration, and intervention time lags.
• Estimated time lags align with known COVID-19 incubation periods.
• The model demonstrates flexibility across different regions and countries.

Conclusions:

• The 2P-DCP is a valuable tool for modeling COVID-19 dynamics.
• This stochastic contagion model can effectively describe regional epidemics and global pandemics.
• The model's simple structure and adaptability make it suitable for diverse epidemiological scenarios.