A computational tool for trend analysis and forecast of the COVID-19 pandemic
Henrique Mohallem Paiva, Rubens Junqueira Magalhaes Afonso, Fabiana, Mara Scarpelli de Lima Alvarenga Caldeira, Ester de Andrade Velasquez

TL;DR
This paper introduces a computational tool that models and forecasts COVID-19 trends globally, providing an automated, interpretable, and adaptable method to support public health decision-making.
Contribution
It presents a straightforward, automated modeling approach and a novel trend analysis method, along with a freely available tool for real-time COVID-19 data analysis and forecasting.
Findings
Effective trend analysis for multiple regions
Accurate predictions of peak and stabilization dates
Tool supports decision-making for public health authorities
Abstract
Purpose: This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics. Methods: Mathematical functions are employed to describe the number of cases and demises in each region and to predict their final numbers, as well as the dates of maximum daily occurrences and the local stabilization date. The model parameters are calibrated using a computational methodology for numerical optimization. Trend analyses are run, allowing to assess the effects of public policies. Easy to interpret metrics over the quality of the fitted curves are provided. Country-wise data from the European Centre for Disease Prevention and Control (ECDC) concerning the daily number of cases and demises around the world are used, as well as detailed data from Johns Hopkins University and from the Brasil.io…
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