Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion

• DOI: 10.1016/j.aml.2020.106617
• Publication Date: 2020-07-15T06:05:18Z
• Authors: Alex Viguerie; Guillermo Lorenzo; Ferdinando Auricchio; Davide Baroli; Thomas J.R. Hughes; Alessia Patton; Alessandro Reali; Thomas E. Yankeelov; Alessandro Veneziani
• Keywords: Physics - Physics and Society; Mathematical epidemiology; 330; Applied Mathematics; G.1.8; Populations and Evolution (q-bio.PE); COVID-19; FOS: Physical sciences; Numerical Analysis (math.NA); Physics and Society (physics.soc-ph); Partial differential equations; Compartmental model; Article; 3. Good health; G.1.10; Mathematical biology; FOS: Biological sciences; G.1.8; G.1.10; FOS: Mathematics; Mathematical modeling; Mathematics - Numerical Analysis; Quantitative Biology - Populations and Evolution

We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitative agreement between the simulated forecast of the spatio-temporal COVID-19 spread in Lombardy and epidemiological data collected at the municipality level. Additional simulations exploring alternative scenarios for the relaxation of lockdown restrictions suggest that reopening strategies should account for local population densities and the specific dynamics of the contagion. Thus, we argue that data-driven simulations of our model could ultimately inform health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources.<br/>7 pages, 3 figures<br/>