Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Abstract

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus.Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics.Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%.Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

“…2021 Bangladesh Worldometer website Daily confirmed cases 10 April to 30 June 2020 LSTM 6.55 - - 4.51 [ 37 ] da Silva, C. C., et al. 2021 Brazil Brasil.io portal Daily confirmed cases till 6 June 2020 Linear Regression 11.42% - - - [ 38 ] Pernambuco (A state in brazil) 1.92% de Souza, D. G. S. et al., 2020 Amapa (A state in Brazil) Health surveillance secretary of Amapa Cumulative confirmed cases from 20 March to 31 August, 2020 Holt-Winters 162 - 0.98 0.34 [ 39 ] Dharani, N. P., et al. 2021 India Kaggle website Daily confirmed cases 30 January to 21 May 2020 Linear Regression 223.89 157.78 1.0 - [ 40 ] Doe, S. W., et al., 2020 USA Johns Hopkins University confirmed cases data for US counties Daily confirmed cases from 22 January to 31 May, 2020 and latitude, and longitude of each county CLEIR-Net 264.33 - - - [ 41 ] Fokas, A.…”

Use of Artificial Intelligence for Predicting COVID-19 Outcomes: A Scoping Review

“…The purpose of using the artificial intelligence method was to predict cases, death, mortality, and severity. Since one paper could predict more than one variable, among the 17 papers, seven papers forecasted mortality [1,7,11,[13][14][15]19], five papers forecasted daily report COVID-19 test-positive cases and death cases [4][5][6]12,16], four papers forecasted severity [3,9,17,18], and one paper predicted all of them [20]. Tree-based model was the most common choice, which was used seven times [9,11,12,13,15,16,19].…”

“…The neural network method was a new trend, and five papers applied this method [3,4,5,7,20]. Three papers used the regression method [1,6,17]. In addition, two papers used the deep learning method [14,18].…”

Ethical Issues in AI-enabled Disease Surveillance: Perspectives from Global Health

“…Johns Hopkins University's COVID-19 global map dashboard. Pilot initiatives for COVID-19 forecasting, modelling and machine learning rose exponentially in 2020, demonstrating the capability of AI for disease surveillance applications [55]. The Global Partnership on Artificial Intelligence (GPAI) identified 84 initiatives supporting some form of AI tool, application and/or platform, from across the global north and south, related to the pandemic [56].…”