Containment effort reduction and regrowth patterns of the Covid-19 spreading

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Political decisions aim for stable Covid-19 (Coronavirus Disease 2019) configurations, leading to reduced containment and social reopening.
• Reopening phases often see renewed infection spread and potential for rapid growth due to viral mutations.
• Quantitative analysis of infection regrowth patterns is crucial for managing the pandemic during societal reopening.

Purpose of the Study:

• To demonstrate a macroscopic approach for predicting Covid-19 diffusion trends after initial containment.
• To provide a proof-of-concept for forecasting infection spread during reopening phases using simple growth models.
• To outline potential future scenarios of Covid-19 diffusion based on early post-lockdown data.

Main Methods:

• Utilizing macroscopic growth models adapted with a time-dependent carrying capacity.
• Analyzing data collected during the initial lockdown period.
• Applying the model to predict diffusion trends in the subsequent reopening phase.
• Illustrating the method with case studies from France, Italy, and the United Kingdom.

Main Results:

• The macroscopic approach, using early data, can outline different scenarios for Covid-19 diffusion over longer time scales.
• The model demonstrates the feasibility of predicting regrowth patterns after containment measures are eased.
• Analysis of France, Italy, and the UK showcases the method's applicability in diverse national contexts.

Conclusions:

• A quantitative analysis of infection regrowth is valuable for navigating post-lockdown phases.
• The proposed macroscopic model with time-dependent carrying capacity offers a framework for forecasting Covid-19 spread.
• This approach aids in understanding and managing the dynamic nature of the pandemic during societal reopening and potential viral evolution.