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Model Based on COVID-19 Evidence to Predict and Improve Pandemic Control

Rafael I. González, Pablo S. Moya, Eduardo M. Bringa, Gonzalo Bacigalupe, Muriel Ramírez-Santana, Miguel Kiwi.

Abstract

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges.

Introduction:

The COVID-19 pandemic has dramatically affected all of humanity, but it also provided an enormous amount of data which may provide valuable lessons on how to effectively face future events of similar characteristics. Already a wealth of statistical data analysis has been published, and models to predict epidemic evolution have been developed. A few recent examples are the work of Biggerstaff et al.

Methods:

During the first weeks of the epidemic, the velocity data can be quite noisy, so we decided to average the velocity reduction, growth, or variation (as appropriate) during the first three weeks once 100 cases have been reached. During the rest of the days, it is calculated directly on the variations without averaging. We present the averaged speed every seven days in the curves, so that they look smoother.

Discussion:

Several different indicators have to be incorporated in the analysis to enhance the probability of effectively controlling COVID-19, but essentially what is required is to reduce the number of cases as fast as possible. When a new contagion focus is found, TTI or equivalent measures may be applied, but if they do not yield prompt results (i.e., a significant reduction within 1–2 weeks), it means that the strategy will fail to control the contagion. Therefore, it is convenient to analyze the time variation of the variables above, i.e., the time variation of the reduction.

Citation: González RI, Moya PS, Bringa EM, Bacigalupe G, Ramírez-Santana M, Kiwi M (2023) Model based on COVID-19 evidence to predict and improve pandemic control. PLoS ONE 18(6): e0286747. https://doi.org/10.1371/journal.pone.0286747

Editor: Hana Maria Dobrovolny, Texas Christian University, UNITED STATES

Received: August 17, 2022; Accepted: May 22, 2023; Published: June 15, 2023.

Copyright: © 2023 González et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data from the site https://ourworldindata.org/ using https://covid.ourworldindata.org/data/owid-covid-data.csv. Detailed information about the dataset at https://github.com/owid/covid-19-data/tree/master/public/data. Some modified data included within the Supporting information.

Funding: Supported by ANID Chile through Fondecyt grants Nos. 11180557, 1191351, 1211902 and Center for the Development of Nanoscience and Nanotechnology, CEDENNA AFB220001. EMB thanks funding from CONICET.

Competing interests: The authors have declared that no competing interests exist.

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