Forecasting Covid-19 dynamics in Brazil: a data driven approach

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's 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. Finally, curve fitting is carried out on the predictions in order to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated between the 25th of April and the 19th of May 2020. Predicted numbers reach a total of 240 thousand infected Brazilians, distributed among the different states, with Sao Paulo leading with almost 65 thousand estimated, confirmed cases. The estimated end of the pandemics (with 97 % of cases reaching an outcome) starts as of May 28th for some states and rests through August 14th, 2020.

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Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17145115 ◽

2020 ◽

Vol 17 (14) ◽

pp. 5115 ◽

Cited By ~ 2

Author(s):

Igor Gadelha Pereira ◽

Joris Michel Guerin ◽

Andouglas Gonçalves Silva Júnior ◽

Gabriel Santos Garcia ◽

Prisco Piscitelli ◽

...

Keyword(s):

Short Term Memory ◽

Data Driven ◽

Advanced Stage ◽

Short Term ◽

Term Memory ◽

Future Data ◽

Data Driven Approach ◽

Ann Models ◽

Preliminary Study ◽

Long Short Term Memory

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.

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Review of Consumer-to-Consumer E-Commerce Research Collaboration

Journal of Organizational and End User Computing ◽

10.4018/joeuc.20210701.oa8 ◽

2021 ◽

Vol 33 (4) ◽

pp. 167-184

Author(s):

Chih-Hung Yuan ◽

Chia-Huei Wu ◽

Dajiang Wang ◽

Shiyun Yao ◽

Yingying Feng

Keyword(s):

Research Collaboration ◽

The United States ◽

Data Driven ◽

Analysis Method ◽

Theoretical Frameworks ◽

Research Papers ◽

Main Approach ◽

Content Analysis Method ◽

Data Driven Approach ◽

Study Teams

This study uses a content analysis method to systematically review 83 research papers from 2002-2018 to explore consumer-to-consumer (C2C) e-commerce research trends. The findings of this study indicate that (1) C2C e-commerce is discussed and investigated in many disciplines, but mainly published in e-commerce journals; (2) studies on C2C e-commerce increasingly focus on diverse topics, but concentrate on regions such as China and the United States; (3) the focus of academic collaboration has shifted from domestic to international collaboration, and collaboration within the same institution. However, collaboration is scarce across different study teams; (4) the data-driven approach is the main approach used in studies on C2C e-commerce; (5) while the number of recent C2C e-commerce studies adopted theories is increasing, few have developed theoretical frameworks or models. Finally, study implications and future study suggestions are also discussed.

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A Data-Driven Approach for Automatic Classification of Extreme Precipitation Events: Preliminary Results

Communications in Computer and Information Science - Applied Informatics ◽

10.1007/978-3-030-61702-8_14 ◽

2020 ◽

pp. 197-209

Author(s):

J. González-Vergara ◽

D. Escobar-González ◽

D. Chaglla-Aguagallo ◽

D. H. Peluffo-Ordóñez

Keyword(s):

Extreme Precipitation ◽

Automatic Classification ◽

Data Driven ◽

Preliminary Results ◽

Extreme Precipitation Events ◽

Data Driven Approach ◽

Precipitation Events

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An open data-driven approach for travel demand synthesis: an application to São Paulo

Regional Studies Regional Science ◽

10.1080/21681376.2021.1968941 ◽

2021 ◽

Vol 8 (1) ◽

pp. 371-386

Author(s):

Aurore Sallard ◽

Miloš Balać ◽

Sebastian Hörl

Keyword(s):

Travel Demand ◽

Open Data ◽

Sao Paulo ◽

Data Driven ◽

São Paulo ◽

Data Driven Approach

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A data-driven approach for assessing biking safety in cities

EPJ Data Science ◽

10.1140/epjds/s13688-021-00265-y ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Sara Daraei ◽

Konstantinos Pelechrinis ◽

Daniele Quercia

Keyword(s):

Local Governments ◽

Policy Decision ◽

Sustainable Transportation ◽

Data Driven ◽

Critical Factors ◽

The Past ◽

The World ◽

Accident Data ◽

Data Driven Approach ◽

Policy Decision Making

AbstractWith the focus that cities around the world have put on sustainable transportation during the past few years, biking has become one of the foci for local governments globally. Cities all over the world invest in biking infrastructure, including bike lanes, bike parking racks, shared (dockless) bike systems etc. However, one of the critical factors in converting city-dwellers to (regular) bike users/commuters is safety. In this work, we utilize bike accident data from different cities to model the biking safety based on street-level (geographical and infrastructural) features. Our evaluations indicate that our model provides well-calibrated probabilities that accurately capture the risk of a biking accident. We further perform cross-city comparisons in order to explore whether there are universal features that relate to cycling safety. Finally, we discuss and showcase how our model can be utilized to explore “what-if” scenarios and facilitate policy decision making.

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