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This research was supported by Complutense University of Madrid, Spain, research group 910395 METODOS BAYESIANOS.

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Valverde, GabrielAuthor

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October 27, 2024
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Expert System to Model and Forecast Time Series of Epidemiological Counts with Applications to COVID-19

Publicated to:Mathematics. 9 (13): 1485- - 2021-07-01 9(13), DOI: 10.3390/math9131485

Authors: Gonzalez-Perez, Beatriz; Nunez, Concepcion; Sanchez, Jose L; Valverde, Gabriel; Velasco, Jose Manuel

Affiliations

Complutense Univ Madrid UCM, Comp Architecture & Automat Dept, Madrid 28040, Spain - Author
Complutense Univ Madrid UCM, Dept Stat & Operat Res, Madrid 28040, Spain - Author
Complutense Univ Madrid UCM, Interdisciplinary Math Inst IMI, Madrid 28040, Spain - Author
Hosp Clin San Carlos, IdISSC, Lab Res Genet Complex Dis, Madrid 28040, Spain - Author

Abstract

We developed two models for real-time monitoring and forecasting of the evolution of the COVID-19 pandemic: a non-linear regression model and an error correction model. Our strategy allows us to detect pandemic peaks and make short- and long-term forecasts of the number of infected, deaths and people requiring hospitalization and intensive care. The non-linear regression model is implemented in an expert system that automatically allows the user to fit and forecast through a graphical interface. This system is equipped with a control procedure to detect trend changes and define the end of one wave and the beginning of another. Moreover, it depends on only four parameters per series that are easy to interpret and monitor along time for each variable. This feature enables us to study the effect of interventions over time in order to advise how to proceed in future outbreaks. The error correction model developed works with cointegration between series and has a great forecast capacity. Our system is prepared to work in parallel in all the Autonomous Communities of Spain. Moreover, our models are compared with a SIR model extension (SCIR) and several models of artificial intelligence.

Keywords

Artificial intelligenceError correction modelMachine learningNon-linear regressionSiSir

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Mathematics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 21/333, thus managing to position itself as a Q1 (Primer Cuartil), in the category Mathematics. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.87. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-23, the following number of citations:

  • WoS: 7
  • Scopus: 9

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-23:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 17.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 12 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 2.25.
  • The number of mentions on the social network X (formerly Twitter): 7 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.