The CP‐ABM approach for modelling COVID‐19 infection dynamics and quantifying the effects of non‐pharmaceutical interventions. (October 2022) - Novakovic, Aleksandar; Marshall, Adele H.
Highlights: Novel CP-ABM method fusing change point detection & agent based modelling approaches. Method accurately models COVID-19 infection dynamics covering two infection waves. CP-ABM identifies key non pharmaceutical interventions & captures their effects. Proposed infection centric modelling approach has significant computation efficiency. Its effectiveness is fully demonstrated on the entire Northern Ireland population. Abstract: The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates C hange P oint detection into an A gent B ased M odel taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population.