Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Abstract

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data

“…The conditions (i) and (ii) above imply an increase of the constraints of the PCA optimization problem defined in (1). To obtain the disjoint components, the heuristic procedure presented in [15,16] can be used.…”

“…The COVID-19 pandemic has deteriorated the usual dynamics that rule the world [1]. Its impact on health and the worldwide economy is evident [2,3].…”

Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies

“…185–203; Meehan et al., 2020 ; Chen & Toxvaerd, 2014 ; González & Villena, 2020 ; Jia et al., 2020 ; May & Anderson, 1984 ). Finally, alternative approaches, as agent-based, statistical models and a combination of these techniques with compartmental models (specially to gather data), are also commonly employed ( Amaral, Casaca, Oishi, & Cuminato, 2021 ; Bendtsen Cano et al., 2020 ; Calvetti, Hoover, Rose, & Somersalo, 2020 ; Donnat & Holmes, 2004 ; Ferguson, Laydon, & Nedjati-Gilani, 2020 ; Roda, Varughese, Han, & Li, 2020 ).…”

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

“…Ref. [ 20 ] integrated the classical SIRD model with artificial neural network modeling, but again using large-area data and not considering the spatial factors influencing the spread of the virus.…”