Short-term analysis and long-term predictions for the COVID-19 epidemic in a seasonality regime: the Italian case

Abstract

As of July 14th, COVID-19 has caused in Italy 34.984 deaths and 243.344 infection cases. Strict lockdown policies were necessary to contain the first outbreak wave and prevent the Italian healthcare system from being overwhelmed by patients requiring intensive care. After the progressive reopening, predicting how the epidemic situation will evolve is urgent and fundamental to control any future outbreak and prevent a second wave. We defined a time-varying optimization procedure to repeatedly calibrate the SIDARTHE model with data up to June 24th. The computed parameter distributions allow us to robustly analyse how the epidemic situation evolved and outline possible future scenarios. Assuming a seasonal regime for COVID-19, we tested different lockdown policies. Our results suggest that an intermittent lockdown where six "open days" are allowed every other week may prevent a resurgent exponential outbreak and, at the same time, ease the societal burden of an extensive lockdown.

A Retrospective Analysis of the COVID-19 Pandemic Evolution in Italy

“…For our study, we used a refined version of the SIDARTHE model [ 7 , 8 ], whose graphical representation is provided in Figure 1 . The original model divides the entire population into eight mutually exclusive compartments describing different infection stages: each individual can be either susceptible (S), asymptomatic undetected or pauci-symptomatic infected (I), detected asymptomatic infected (D), undetected symptomatic infected (A), detected symptomatic infected (R), detected life-threatened symptomatic infected (T), recovered (H), or dead (E).…”

“…Many researchers have proposed models for COVID-19, building upon the common SIR (Susceptible, Infectious, Recovered)-SEIR (Susceptible, Exposed, Infectious, Recovered) models for human-to-human transmission, to study the key traits of the epidemic [ 3 , 4 , 5 , 6 ]. In this work, we opted for the SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model [ 7 , 8 ], which captures the different epidemiological stages of the infection due to its granularity in stratifying infected subjects for both symptoms and detection. We used a refined version of the model to capture the COVID-19 trajectory fuelled by the original SARS-CoV-2 strain in Italy and to assess the efficacy of adopted countermeasures, and the smooth operation of healthcare and contact tracing systems in terms of model rates.…”