A robust nonlinear mixed-effects model for COVID-19 deaths data
Fernanda L. Schumacher, Clecio S. Ferreira, Marcos O. Prates, Alberto, Lachos, Victor H. Lachos

TL;DR
This paper introduces a robust nonlinear mixed-effects model using skew-normal distributions to analyze COVID-19 death data, effectively handling skewness, outliers, and clustered longitudinal measurements across multiple countries.
Contribution
It presents a novel modeling approach combining robustness and flexibility for complex COVID-19 death data, with an efficient estimation algorithm and bootstrap-based confidence intervals.
Findings
Model captures diverse decay patterns in COVID-19 deaths.
Method provides reliable estimates despite skewness and outliers.
Application to Latin American, European, and North American data demonstrates effectiveness.
Abstract
The analysis of complex longitudinal data such as COVID-19 deaths is challenging due to several inherent features: (i) Similarly-shaped profiles with different decay patterns; (ii) Unexplained variation among repeated measurements within each country, these repeated measurements may be viewed as clustered data since they are taken on the same country at roughly the same time; (iii) Skewness, outliers or skew-heavy-tailed noises are possibly embodied within response variables. This article formulates a robust nonlinear mixed-effects model based in the class of scale mixtures of skew-normal distributions for modeling COVID-19 deaths, which allows the analysts to model such data in the presence of the above described features simultaneously. An efficient EM-type algorithm is proposed to carry out maximum likelihood estimation of model parameters. The bootstrap method is used to determine…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Statistical Methods and Bayesian Inference · Bayesian Methods and Mixture Models
