Navegando por Autor "Alves, Marcos Antonio"

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COVID-ABS : an agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions.
(2020) Silva, Petrônio Cândido de Lima e; Batista, Paulo Vitor do Carmo; Lima, Hélder Seixas; Alves, Marcos Antonio; Guimarães, Frederico Gadelha; Silva, Rodrigo César Pedrosa
The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non- pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs.
(2021) Alves, Marcos Antonio; Castro, Giulia Zanon de; Oliveira, Bruno Alberto Soares; Ferreira, Leonardo Augusto; Ramírez, Jaime Arturo; Silva, Rodrigo César Pedrosa; Guimarães, Frederico Gadelha
The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1–score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by- case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system.
Forecasting in non-stationary environments with fuzzy time series.
(2020) Silva, Petrônio Cândido de Lima e; Severiano Junior, Carlos Alberto; Alves, Marcos Antonio; Silva, Rodrigo; Cohen, Miri Weiss; Guimarães, Frederico Gadelha
Time series arise in many fields of science such as engineering, economy and agriculture to cite a few. In the early 1990’s the so called Fuzzy Time Series were proposed to handle vague and imprecise knowledge in time series data and have since become competitive forecasting models. A common limitation of recent fuzzy time series models is their inability to handle non-stationary data. Thus, in this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS). In the proposed method, we employ Non-Stationary Fuzzy Sets, in which perturbation functions are used to adapt the membership function parameters in the knowledge base in response to statistical changes in the time series. The flexibility of the method by means of computational experiments was tested with eight synthetic non-stationary time series data with several kinds of concept drifts, four real market indices (Dow Jones, NASDAQ, SP500 and TAIEX), three real FOREX pairs (EUR-USD, EUR-GBP, GBP-USD), and two real cryptocoins exchange rates (Bitcoin-USD and Ethereum-USD). As competitor models the Time Variant fuzzy time series and the Incremental Ensemble were used, these are two of the major approaches for handling non-stationary data sets. The proposed method shows resilience to concept drift, by adapting parameters of the model, while preserving the symbolic structure of the knowledge base.