Abstract
The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world’s regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country’s response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with São Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.
Highlights
The world population is being rapidly infected by the SARS-COV-2 virus pandemic, known asCovid-19 [1]
We present the results obtained with the Modified Auto-Encoder model to forecast the epidemic curves of Covid-19 in Brazil, as well as the fitted distribution curves confirming the peaks obtained for all Brazilian states
The numbers obtained for Brazil, which are of direct interest to the population, are presented in for some states, and complete results can be found at www.natalnet.br/covid
Summary
The world population is being rapidly infected by the SARS-COV-2 virus pandemic, known asCovid-19 [1]. The flattening of the virus spreading curve, which can be modeled using different approaches [3–7], is the main goal that is strictly related to the rules mentioned. If this does not happen, the number of deaths would skyrocket, as it was recently experimented in countries, such as the USA, Brazil, Italy, Spain, France, and the UK. Several warnings about this have been spread in the literature, for example, in the beginning of March 2020, Fanelli [3] explained that: “In Italy and in other countries that will be facing the epidemic surge soon, this is quite possibly only achievable through a cooperative and disciplined effort of the population as a whole”. Successful example of curve flattening have already been seen in Portugal, Germany and South Korea, among others
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