Advanced Multi-Mutation With Intervention Policies Pandemic Model

Abstract

A pandemic is a threat to humanity with potentially millions of deaths worldwide. Epidemiological models can be used to better understand pandemic dynamics and assist policymakers in optimizing their Intervention Policies (IPs). Most existing epidemiological models assume, sometimes incorrectly, that a pandemic is caused by a single pathogen, ignoring pathogen mutations over time that result in different pathogen strains with different characteristics. In addition, the existing models do not incorporate the effect of IPs like vaccinations and lockdowns during the fitting phase. In this work, we introduce a new model called Suspected-Infected-Vaccinated-Recovered-reInfected (SIVRI). This model extends the SIRS model with adaptation to incorporate available knowledge related to the different pathogen mutations together with multiple IPs. In order to find the model parameters we propose a new fitting procedure that supports the complex social, epidemiological, and clinical dynamics that occur during a pandemic. We examine the suggested SIVRI model in comparison to the SIRS and XGboost models on the COVID-19 pandemic in Israel that includes four COVID-19 mutations, and the vaccination and lockdown IPs. We show that the proposed model can fit accurately to the historical data and outperform the existing models in predictions of basic reproduction number, mortality rate, and severely infected individuals rate. MSC: 34-04 | 65-05 | 68U20