Multi-strain integrated modelling for COVID-19

Abstract

BACKGROUND AND AIM: Quantitative forecasting of the COVID-19 pandemic evolution has become complex, due to the multiple parameters that affect disease dynamics, including (a) non-pharmacological interventions (such as social distancing, extensive (self)-testing), (b) pharmacological interventions (vaccines and antivirals), (c) weather and climate (d) different transmissibility, disease severity and reinfection capacities of the various co-existing strains. METHODS: A multi-modal computational tool (CORE: COVID Risk Evaluation model), able to assess the impact of all the parameters mentioned above, has been developed to evaluate the COVID-19 health risk. The tool has been applied in Greece, Italy and USA. Contact matrices among the various population groups, accounting for their sociodemographic profiles (i.e. age, occupation etc), as well as the impact of targeted social distancing measures have been developed using agent-based modelling. RESULTS: The evolution of the COVID-19 dynamics for Greece since February 2020, and the long-term prediction till the end of 2022, considering risk management has been successfully described. The implementation of the omicron strain dynamics in early December in Greece was critical, because it allowed us to identify at a very early stage the already significant dispersion of this highly contagious strain and warn in a timely manner public authorities, as well as to identify the timing and the height of the pandemics peak. Similarly, an early warning was issued on the advent of omicron 2 in the end of February. CONCLUSIONS: CORE has demonstrated its efficacy in capturing the pandemic dynamics and issuing early warnings in time to take health risk management measures. Currently natural and acquired immunity declines gradually and a high rate of re-infections related to omicron 2 is observed. Thus, a high rate of booster vaccination in adults, rapid completion of schoolchildren vaccination and indoor air disinfection in public spaces are needed to adequately contain the COVID-19 dispersion.