On the Impact of Quarantine Policies and Recurrence Rate in Epidemic Spreading Using a Spatial Agent-Based Model

Abstract

Pandemic outbreaks often determine swift global reaction, proven by for example the more recent COVID-19, H1N1, Ebola, or SARS outbreaks. Therefore, policy makers now rely more than ever on computational tools to establish various protection policies, including contact tracing, quarantine, regional or national lockdowns, and vaccination strategies. In support of this, we introduce a novel agent-based simulation framework based on: (i) unique mobility patterns for agents between their home location and a point of interest, and (ii) the augmented SICARQD epidemic model. Our numerical simulation results provide a qualitative assessment of how quarantine policies and the patient recurrence rate impact the society in terms of the infected population ratio. We investigate three possible quarantine policies (proactive, reactive, and no quarantine), a variable quarantine restrictiveness (0–100%), respectively, and three recurrence scenarios (short, long, and no recurrence). Overall, our results show that the proactive quarantine in correlation to a higher quarantine ratio (i.e., stricter quarantine policy) triggers a phase transition reducing the total infected population by over 90% compared to the reactive quarantine. The timing of imposing quarantine is also paramount, as a proactive quarantine policy can reduce the peak infected ratio by over ×2 times compared to a reactive quarantine, and by over ×3 times compared to no quarantine. Our framework can also reproduce the impactful subsequent epidemic waves, as observed during the COVID-19 pandemic, according to the adopted recurrence scenario. The suggested solution against residual infection hotspots is mobility reduction and proactive quarantine policies. In the end, we propose several nonpharmaceutical guidelines with direct applicability by global policy makers.