Forecasting Covid-19 dynamics in Brazil: a data driven approach
Igor G. Pereira, Joris M. Guerin, Andouglas G. Silva Junior, Cosimo, Distante, Gabriel S. Garcia, Luiz M. G. Gon\c{c}alves

TL;DR
This paper presents a data-driven approach using clustering and modified auto-encoders to forecast Covid-19 dynamics in Brazil, providing estimates of infection peaks and pandemic duration for different states.
Contribution
It introduces a novel clustering-based transfer learning method with modified auto-encoders for pandemic prediction in Brazilian states, improving forecast accuracy.
Findings
Most Brazilian states' infection peaks are estimated between April 25 and May 19, 2020.
Predicted total infections in Brazil reach approximately 240,000 cases.
The pandemic is expected to end between late May and mid-August 2020 in most states.
Abstract
This paper has a twofold contribution. The first is a data driven approach for predicting the Covid 19 pandemic dynamics, based on data from more advanced countries. The second is to report and discuss the results obtained with this approach for Brazilian states, as of May 4th, 2020. We start by presenting preliminary results obtained by training an LSTM SAE network, which are somewhat disappointing. Then, our main approach consists in an initial clustering of the world regions for which data is available and where the pandemic is at an advanced stage, based on a set of manually engineered features representing a country response to the early spread of the pandemic. A Modified Auto-Encoder network is then trained from these clusters and learns to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks.…
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