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Predicting the Evolution of COVID-19 Cases and Deaths Through a Correlations-Based Temporal Network

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Intelligent Systems (BRACIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12320))

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Abstract

Given the most recent events involving the fast spreading of COVID-19, policy makers around the world have been challenged with the difficult task of developing efficient strategies to contain the dissemination of the disease among the population, sometimes by taking severe measures to restrict local activities, both socially and economically. Within this context, models which can help on predicting the spread evolution of COVID-19 in a specific region would surely help the authorities on their planning. In this paper, we introduce a semi-supervised regression model which makes use of a correlations-based temporal network, by considering the evolution of COVID-19 in different world regions, in order to predict the evolution of new confirmed cases and deaths in 27 federal units of Brazil. In this approach, each node in the network represents the COVID-19 time series in a specific region, and the edges are created according to the variations similarity between each pair of nodes, at each new time step. The results obtained, by predicting the weekly new confirmed cases and deaths in each region, are promising, with a median and mean absolute percentage error of 21% and 24%, respectively, when predicting new cases, and a median and mean absolute percentage error of 16% and 23%, respectively, when predicting new deaths, for the considered period.

This work is supported in part by the São Paulo State Research Foundation (FAPESP) under grant numbers 2015/50122-0 and 2013/07375-0, the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and the Brazilian National Council for Scientific and Technological Development (CNPq) under grant number 303199/2019-9.

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Authors and Affiliations

  1. Institute of Mathematics and Computer Science, University of Sao Paulo, São Paulo, Brazil

    Tiago Colliri & Alexandre C. B. Delbem

  2. Faculty of Philosophy, Science, and Letters, University of Sao Paulo, São Paulo, Brazil

    Liang Zhao

Authors
  1. Tiago Colliri
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  2. Alexandre C. B. Delbem
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  3. Liang Zhao
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Correspondence to Tiago Colliri .

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Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Ricardo Cerri

  2. Federal University of ABC, Santo Andre, Brazil

    Ronaldo C. Prati

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Colliri, T., Delbem, A.C.B., Zhao, L. (2020). Predicting the Evolution of COVID-19 Cases and Deaths Through a Correlations-Based Temporal Network. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12320. Springer, Cham. https://doi.org/10.1007/978-3-030-61380-8_27

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  • DOI: https://doi.org/10.1007/978-3-030-61380-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61379-2

  • Online ISBN: 978-3-030-61380-8

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