COVID-19 outbreak model

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COVID-19 outbreak model

The computational models and computer simulations are one of the available research tools that epidemiologists can exploit to better understand the spreading characteristics of COVID-19 and to decide  the social measures to counter, mitigate or simply delay the spread of the infectious diseases.

Our study presents  an extended version of Susceptible-Exposed-Infected-Removed (SEIR) model in which the population age is taken into account and  the infectious population is divided into three sub-classes: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction actions and the related population response are explicitly represented in the model.

The proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont by  varying the force of population response and the proportion between detected and undetected infected individuals. Our  results show that the implemented control measures have proven effective in containing the epidemic, neutralizing, or at least limiting, the potential dangerous impact of a large proportion of undetected cases.

Our model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken.

The age-dependent SEIR model.
The age-dependent SEIR model.
The total infected cases distributed in the counties of the Piedmont region.
The total infected cases distributed in the counties of the Piedmont region.
The top plot reports the 1000 simulated traces describing the cumulative infected cases obtained by the stochastic simulation of our model until May, 1st.
The top plot reports the 1000 simulated traces describing the cumulative infected cases obtained by the stochastic simulation of our model until May, 1st. The green line corresponds to the median trend. It is possible appreciate that the trend of the infected cases obtained from the surveillance data (red line)  is inside the interquartile range derived by the simulation traces.
The bottom plot shown the daily evolution of infected individuals computed by the stochastic simulation until May, 1st.
The bottom plot shown the daily evolution of infected individuals computed by the stochastic simulation until May, 1st. The stack bars reported the undetected infected individuals (orange), the quarantine infected individuals(light blue), and hospitalized infected (blue). The undetected cases are in a proportion of one-to-one with the detected ones, on average. The surveillance data are reported as red line.

Our preliminary results are discussed in the following pre-print:
Pernice, S., Castagno, P., Marcotulli, L., Maule, M., Richiardi, L., Moirano, G., Sereno M., Cordero, F., Beccuti, M. Undetected Cases of Covid-19 and Effects of Social Distancing Strategies: a Modeling Study in Piedmont Region.
E&P Code: repo.epiprev.it/929
This interdisciplinary work is a collaboration of  three research groups of the University of Turin: the group of Quantitative Biology (Dr Marco Beccuti and Dr Francesca Cordero), the group of Quantitative Modelling and Systems Performance Evaluation (Prof. Matteo Sereno), and the Epidemiology group (Prof. Lorenzo Richiardi).