GENERALIZED GROWTH CURVE MODEL FOR COVID-19 IN BRAZILIAN STATES

Q4 Medicine Revista Brasileira de Biometria Pub Date : 2020-06-29 DOI:10.28951/rbb.v38i2.481

Magali Teresópolis Reis Amaral, K. S. Conceição, M. Andrade, C. Padovani

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Abstract

The present paper consists of using the Chapman-Richard generalized growth model to functionally relate the number of people infected by COVID-19 with the number of days The objective of this work is to estimate the instant that the number of infected people stops growing using the dataset of the accumulated amount of infected For this propose, one conducted a comparative study of the performances of three models of Richard in eight Brazilian States In the methodological context, the Gauss Newton procedure was used to estimate the parameters In addition, selection criteria of the models were used to select the one that best fits the dataset The methodology used allowed consistent estimates of the number of people infected by COVID-19 as a function of time and, consequently, it was possible to conclude that the projections provided by the growth curves point to a scenario of general contamination acceleration Besides, the models predict that the epidemic is close to reaching its peak in Amazonas, Ceara, Maranhao, Pernambuco, and Sao Paulo States © 2020, Universidade Federal de Lavras -Departamento de Estatistica All rights reserved

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巴西各州COVID-19的广义增长曲线模型

本论文使用Chapman-Richard广义增长模型将感染COVID-19的人数与天数在功能上联系起来。本工作的目标是使用累积感染数量的数据集估计感染人数停止增长的时刻。为了提出这一建议，在方法学背景下，对Richard的三种模型在巴西八个州的表现进行了比较研究。此外，使用高斯牛顿程序来估计参数，并使用模型的选择标准来选择最适合数据集的模型。所使用的方法允许将COVID-19感染人数作为时间函数进行一致的估计，因此，可以得出结论，由增长曲线提供的预测指向一般污染加速的情况。模型预测，该流行病在亚马逊州、塞拉亚州、马拉尼昂州、伯南布哥州和圣保罗州接近达到顶峰©2020，拉夫拉斯联邦大学-统计部门版权所有

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来源期刊

Revista Brasileira de Biometria Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)

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审稿时长

53 weeks