COVID-19-ICU

This tool implements a SIRD model applied to COVID-19 spreading.

WE ARE CURRENTLY IMPROVING THE API; THE SOURCE CODE WILL BE PUBLISHED SOON.

The model

The model simulates the variation over time of the following variables: the number of susceptible individuals (S); the number of infected individuals (I); the number of individuals who recovered from the disease (R); the number of deceased patients (D). The model consists in the following system of coupled ordinary differential equations:

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• 

The user must provide time-series of the number of infected, recovered and deceased patients. Then, four parameters are automatically calibrated against observed time-series:

• S(0), i.e., the number of susceptible individuals in the population at t=0;
• the transmission rate ;
• the recovery rate ;
• the fatality rate .

The rest of the initial state of the system (i.e., I(0), R(0), and D(0)) are automatically determined by the input time-series.

Simulating lockdown events

Our tool allows the simulation of lockdown events. An event is modeled as a pair (days, ) where:

• days is the number of days after the beginning of the time-series, in which the lockdown is applied;
• is a scaling factor for the parameter, simulating a slower transmission rate due to limited social interaction.

The user can provide a list of events, in order to simulate the effect of multiple or pulsed lockdowns.

Technical details

• scipy's LSODA is used for ODE integration;
• FST-PSO is the Computational Intelligence algorithm used for parameters calibration;
• the objective function used for the calibration is the cumulative RMSE error of the model with respect to the target time-series.

Further information

For information about the modeling approach, please contact Prof. Daniela Besozzi (daniela.besozzi@unimib.it).

For details about the python implementation, please contact Dr. Marco S. Nobile (m.s.nobile@tue.nl)