Covid-like epidemic spreading on networks

Abstract

The COVID-19 pandemic has had significant impacts on health, economies, and societies. Mathematical models in networks play a crucial role in understanding and mitigating the spread of infectious diseases. This study develops a customized SIR model to simulate the spread of COVID-19, incorporating non-pharmaceutical interventions (NPIs) to limit transmission. Self-protection and mobility restrictions are complementary, allowing hygiene measures to serve as an alternative to strict mobility limitations. Gillespie algorithm is used to simulate infection and recovery events. To combat the late response in the first wave of infection in Spain, the study explores different lockdown strategies, including Intense and Multi-phase approaches. Intense lockdowns effectively reduce cases, but may be challenging to sustain due to population fatigue with prolonged restrictions. On the other hand, Multi-phase lockdowns have limited impact on final case numbers but aid in the recovery of the health system between waves, minimizing the impact on the economy, fatalities and people’s mental health.