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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Uncertainty Reduction in Logistic Growth Regression Using Surrogate Systems Carrying Capacities: a COVID-19 Case Study

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Author(s):
Vieira, Bruno Hebling [1] ; Hiar, Nathalia Hanna [2] ; Cardoso, George C. [1]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Dept Phys, FFCLRP, BR-14040901 Ribeirao Preto, SP - Brazil
[2] Univ Sao Paulo, Dept Biol, FFCLRP, BR-14040901 Ribeirao Preto, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Brazilian Journal of Physics; v. 52, n. 1 FEB 2022.
Web of Science Citations: 0
Abstract

Logistic growth regressions present high uncertainties when data are not past their inflection points. In such conditions, the uncertainty in the estimated carrying capacity K, for example, can be of the order of K. Here, we present a method for uncertainty reduction in logistic growth regression using data from a surrogate logistic growth process. We illustrate the method using Richards' growth function to predict the inflection points of COVID-19 first-wave accumulated causalities in Brazilian cities. First waves of epidemics are known to be reasonably well modeled a posteriori by Richard's growth function. Yet, we make predictions using early data that end before or around the inflection point. For that goal, we estimate K by logistic growth regression using data from surrogate international cities where the epidemics are clearly past their inflection points. The constraint stabilizes the logistic growth regression for the Brazilian cities, reducing the uncertainty in the prediction parameters even when the surrogate K is a rough estimate. The predictions for COVID-19 first-wave peaks in Brazilian cities agree with official data. The method may be used for other logistic models and logistic processes, in areas such as economics and biology, when surrogate populations or systems are identified. (AU)

FAPESP's process: 18/11881-1 - Machine learning prediction of intellectual abilities from magnetic resonance neuroimaging
Grantee:Bruno Hebling Vieira
Support Opportunities: Scholarships in Brazil - Doctorate