Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data

  1. Cosimo Distante
  2. Igor Gadelha Pereira
  3. Luiz M. Garcia Gonçalves
  4. Prisco Piscitelli
  5. Alessandro Miani

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Abstract

Background

Epidemiological figures of Covid-19 epidemic in Italy are worse than those observed in China.

Methods

We modeled the Covid-19 outbreak in Italian Regions vs. Lombardy to assess the epidemics progression and predict peaks of new daily infections and total cases by learning from the entire Chinese epidemiological dynamics. We trained an artificial neural network model, a modified auto-encoder with Covid-19 Chinese data, to forecast epidemic curve of the different Italian regions, and use the susceptible–exposed–infected–removed (SEIR) compartment model to predict the spreading and peaks. We have estimated the basic reproduction number (R 0 ) - which represents the average number of people that can be infected by a person who has already acquired the infection - both by fitting the exponential growth rate of the infection across a 1-month period, and also by using a day by day assessment, based on single observations.

Results

The expected peak of SEIR model for new daily cases was at the end of March at national level. The peak of overall positive cases is expected by April 11 th in Southern Italian Regions, a couple of days after that of Lombardy and Northern regions. According to our model, total confirmed cases in all Italy regions could reach 160,000 cases by April 30 th and stabilize at a plateau.

Conclusions

Training neural networks on Chinese data and use the knowledge to forecast Italian spreading of Covid-19 has resulted in a good fit, measured with the mean average precision between official Italian data and the forecast.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.09.20059055: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.04.09.20059055v1 on medRxiv