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COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

De Lima, CL; Da Silva, CC; Da Silva, ACG; Silva, EL; Marques, GS; De Araújo, LJB; Albuquerque Júnior, LA; ... Da Silva Filho, AG; + view all (2020) COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19. Frontiers in Public Health , 8 , Article 580815. 10.3389/fpubh.2020.580815. Green open access

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Abstract

Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. / Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. / Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. / Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America.

Type: Article
Title: COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fpubh.2020.580815
Publisher version: https://doi.org/10.3389/fpubh.2020.580815
Language: English
Additional information: Copyright © 2020 de Lima, da Silva, da Silva, Luiz Silva, Marques, de Araújo, Albuquerque Júnior, de Souza, de Santana, Gomes, de Freitas Barbosa, Musah, Kostkova, dos Santos and da Silva Filho. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: SARS-CoV-2 spread forecast, intelligent forecasting systems, infectious diseases, dynamic forecasting systems, Covid-19 forecasting
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography
URI: https://discovery.ucl.ac.uk/id/eprint/10115413
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