Métodos de visualização de dados em softwares de gestão de saúde para dados espaço-temporais de doenças infecciosas: uma revisão sistemática
DOI:
https://doi.org/10.21439/conexoes.v19.3728Palavras-chave:
Visualização de dados, Doenças infecciosas, Dados espaço-temporaisResumo
A presente pesquisa tem como objetivo identificar métodos e técnicas de visualização de dados para dados espaço-temporais relacionados a doenças infecciosas, tendo, como pergunta de pesquisa, “Quais são os principais métodos de visualização de dados em softwares de gestão de saúde para dados espaço-temporais de doenças infecciosas?”. Realizou-se uma busca nas bases Pubmed, IEEExplore, Scopus, Web of Science e Science Direct, restringindo-se o período para os últimos 5 anos, o idioma para língua inglesa e temática relacionada a (1) doenças infecciosas, (2) dados espaço-temporais e (3) visualização de dados. As etapas dessa pesquisa foram realizadas com revisão por pares e os estudos foram sintetizados extraindo variáveis de relevância para a pergunta e para o objetivo da pesquisa. Como resultado, observou-se que as principais técnicas de visualização de dados, empregadas neste contexto, são: mapas 4D, mapas organizáveis, dashboards integrados a mapas georreferenciados, e cubos espaço-tempo.
Palavras-chave: Visualização de dados. Doenças infecciosas. Dados espaço-temporais.
Referências
CARUCCIO, L.; DEUFEMIA, V.; POLESE, G. Visualization of (multimedia) dependencies from big data. Multimedia Tools and Applications, v. 78, p. 33151-33167, 2019.
GANESAN, S.; SUBRAMANI, D. Spatio-temporal predictive modeling framework for infectious disease spread. Scientific Reports, v. 11, n. 1, p. 6741, 2021. 2008.
HE, J. et al. Variable-based spatiotemporal trajectory data visualization illustrated. IEEE Access, v. 7, p. 143646-143672, 2019.
KITCHENHAM, B. et al. Systematic literature reviews in software engineering–a systematic literature review. Information and Software Technology, v. 51, n. 1, p. 7-15, 2009.
KOCH, T. Knowing its place: mapping as medical investigation. The Lancet, v. 379, n. 9819, p. 887-888, 2012.
KRONENFELD, B. J.; YOO, K. il. Effectiveness of animated choropleth and proportional symbol cartograms for epidemiological dashboards. Cartography and Geographic Information Science, v. 51, n. 2, p. 330-346, 2024.
LAN, Yu et al. Geovisualization of COVID-19: State of the Art and Opportunities. Cartographica: The International Journal for Geographic Information and Geovisualization, v. 56, n. 1, p. 2-13, 2021.
LIN, Chia-Hsien; WEN, Tzai-Hung. How spatial epidemiology helps understand infectious human disease transmission. Tropical Medicine and Infectious Disease, v. 7, n. 8, p. 164, 2022.
LEDUC, T.; TOURRE, V.; SERVIÈRES, M. The space-time cube as an effective way of representing and analysing the streetscape along a pedestrian route in an urban environment. In: SHS web of conferences. EDP Sciences, 2019. p. 03005.
NARAYAN, K. A.; NAYAK, M. Siva Durga Prasad. Need for interactive data visualization in public health practice: examples from India. International Journal of Preventive Medicine, v. 12, n. 1, p. 16, 2021.
VÖGTLE, F. et al. A Collaborative Platform Supporting Distributed Teams in Visualization and Analysis of Infectious Disease Data. In: 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). IEEE, p. 226-232, 2022.
WU, J. T.; LEUNG, K.; LEUNG, G. M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet, v. 395, n. 10225, p. 689-697, 2020.
Referências dos Estudos Selecionados
AHRENS, K. A. et al. Rural–urban residence and maternal hepatitis C infection, US: 2010–2018. American Journal of Preventive Medicine, v. 60, n. 6, p. 820-830, 2021.
AHRENS, K. A. et al. Maternal hepatitis C prevalence and trends by county, US: 2016–2020. Paediatric and Perinatal Epidemiology, v. 37, n. 2, p. 134-142, 2023.
AMIN, S. et al. Detecting dengue/flu infections based on tweets using LSTM and word embedding. IEEE Access, v. 8, p. 189054-189068, 2020.
ANTWEILER, D. et al. Uncovering chains of infections through spatio-temporal and visual analysis of COVID-19 contact traces. Computers & Graphics, v. 106, p. 1-8, 2022.
BAXTER, L. et al. Development of the United States Environmental Protection Agency’s Facilities Status Dashboard for the COVID-19 Pandemic: Approach and Challenges. International Journal of Public Health, v. 67, p. 1604761, 2022.
BELLO, I. M. et al. Real-time monitoring of a circulating vaccine-derived poliovirus outbreak immunization campaign using digital health technologies in South Sudan. lPan African Medical Journal, v. 40, n. 1, 2021.
BURKOM, H. et al. Electronic surveillance system for the early notification of community-based epidemics (ESSENCE): overview, components, and public health applications. JMIR Public Health and Surveillance, v. 7, n. 6, p. e26303, 2021.
CHAPIN, C.; ROY, S. S. A spatial web application to explore the interactions between human mobility, government policies, and COVID-19 cases. Journal of Geovisualization and Spatial Analysis, v. 5, p. 1-8, 2021.
CHEN, Y.; VOLIĆ, I. Topological data analysis model for the spread of the coronavirus. PLoS ONE, v. 16, n. 8, p. e0255584, 2021.
CHU, A. M. Y. et al. Analyzing cross-country pandemic connectedness during COVID-19 using a spatial-temporal database: Network analysis. JMIR Public Health and Surveillance, v. 7, n. 3, p. e27317, 2021.
COSTA, M. N.; MILEU, N.; ALVES, A. Dashboard comprime_compri_mov: Multiscalar spatio-temporal monitoring of the covid-19 pandemic in Portugal. Future Internet, v. 13, n. 2, p. 45, 2021.
DAO, T. P. et al. A geospatial platform to support visualization, analysis, and prediction of tuberculosis notification in space and time. Frontiers in Public Health, v. 10, p. 973362, 2022.
DUARTE, I. et al. Spatiotemporal evolution of COVID-19 in Portugal’s Mainland with self-organizing maps. International Journal of Health Geographics, v. 22, n. 1, p. 1-18, 2023.
FIELD, E.; DYDA, A.; LAU, C. L. COVID‐19 Real‐time Information System for Preparedness and Epidemic Response (CRISPER). The Medical Journal of Australia, v. 214, n. 8, p. 386-386. e1, 2021.
FRANCESCHI, V. B.i et al. Genomic epidemiology of SARS-CoV-2 in Esteio, Rio Grande do Sul, Brazil. BMC Genomics, v. 22, n. 1, p. 371, 2021.
GÓMEZ-EXPÓSITO, A.; ROSENDO-MACÍAS, J. A.; GONZÁLEZ-CAGIGAL, M. A. Monitoring and tracking the evolution of a viral epidemic through nonlinear kalman filtering: Application to the covid-19 case. IEEE Journal of Biomedical and Health Informatics, v. 26, n. 4, p. 1441-1452, 2021.
GUO, X. et al. Modeling the external, internal, and multi-center transmission of infectious diseases: the COVID-19 case. Journal of Social Computing, v. 3, n. 2, p. 171-181, 2022.
HALL, E. W. et al. County‐Level Variation in Hepatitis C Virus Mortality and Trends in the United States, 2005‐2017. Hepatology, v. 74, n. 2, p. 582-590, 2021.
HE, Y. et al. Geospatial Modeling of Health, Socioeconomic, Demographic, and Environmental Factors with COVID-19 Incidence Rate in Arkansas, US. ISPRS International Journal of Geo-Information, v. 12, n. 2, p. 45, 2023.
JIANG, B. et al. Interactive analysis of epidemic situations based on a spatiotemporal information knowledge graph of COVID-19. IEEE ACCESS, v. 10, p. 46782-46795, 2020.
JIAO, J. T. I. et al. A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network. Biomedical and Environmental Sciences, v. 35, n. 6, p. 494-503, 2022.
KALA, A. K.; ATKINSON, S. F.; TIWARI, C. Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus. PeerJ, v. 8, p. e9577, 2020.
KARABEGOVIC, A.r; PONJAVIC, M.; HUKIC, M. Epidemic Location Intelligence System as response to the COVID-19 Outbreak in Bosnia and Herzegovina. Applied System Innovation, v. 4, n. 4, p. 79, 2021.
KHALIQUE, F.; KHAN, S. A. Multiple disease hotspot mining for public health informatics in resource starved settings: study of communicable diseases in Punjab, Pakistan. IEEE ACCESS, v. 9, p. 89989-89998, 2021.
KUZDEUOV, A. et al. A particle-based covid-19 simulator with contact tracing and testing. IEEE Open Journal of Engineering in Medicine and Biology, v. 2, p. 111-117, 2021.
LIU, R.; YANG, H. Spatial tessellation of infectious disease spread for epidemic decision support. IEEE Robotics and Automation Letters, v. 7, n. 1, p. 626-633, 2021.
LUAN, H.; RANSOME, Y. County-Level Spatiotemporal Patterns of New HIV Diagnoses and Pre-exposure Prophylaxis (PrEP) Use in Mississippi, 2014–2018: A Bayesian Analysis of Publicly Accessible Censored Data. Annals of the American Association of Geographers, v. 113, n. 1, p. 129-148, 2023.
MARCÍLIO-JR, W. E. et al. Visual analytics of COVID-19 dissemination in São Paulo state, Brazil. Journal of Biomedical Informatics, v. 117, p. 103753, 2021.
MEDEIROS, J. A. R. et al. Spatiotemporal dynamics of syphilis in pregnant women and congenital syphilis in the state of São Paulo, Brazil. Scientific Reports, v. 12, n. 1, p. 585, 2022.
MILANO, M.; ZUCCO, C.; CANNATARO, M. COVID-19 Community Temporal Visualizer: A new methodology for the network-based analysis and visualization of COVID-19 data. Network Modeling Analysis in Health Informatics and Bioinformatics, v. 10, p. 1-38, 2021.
MO, Chunbao et al. An analysis of spatiotemporal pattern for COIVD‐19 in China based on space‐time cube. Journal of Medical Virology, v. 92, n. 9, p. 1587-1595, 2020.
MOHAMMADEBRAHIMI, S. et al. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health, v. 21, n. 1, p. 1-18, 2021.
MYER, M. H.; JOHNSTON, J. M. Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors. Science of the Total Environment, v. 650, p. 2818-2829, 2019.
NGAI, S. et al. Built by epidemiologists for epidemiologists: an internal COVID-19 dashboard for real-time situational awareness in New York City. JAMIA Open, v. 5, n. 2, p. ooac029, 2022.
NIU, R. et al. Modeling the COVID-19 pandemic using an SEIHR model with human migration. Ieee Access, v. 8, p. 195503-195514, 2020.
NORTON, A. et al. Are at-risk sociodemographic attributes stable across COVID-19 transmission waves?. Spatial and Spatio-temporal Epidemiology, v. 45, p. 100586, 2023.
ONDRIKOVA, N. et al. Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study. Journal of Medical Internet Research, v. 25, p. e37540, 2023.
PANG, Ming-Fan et al. Spatiotemporal visualization for the global COVID-19 surveillance by balloon chart. Infectious Diseases of Poverty, v. 10, p. 1-8, 2021.
PARPIA, A. S. et al. Spatio-temporal dynamics of measles outbreaks in Cameroon. Annals of Epidemiology, v. 42, p. 64-72. e3, 2020.
RAMÍREZ, I. J.; LEE, J. COVID-19 emergence and social and health determinants in Colorado: a rapid spatial analysis. International Journal of Environmental Research And Public Health, v. 17, n. 11, p. 3856, 2020.
RUSSELL, A. et al. Spatiotemporal analyses of 2 co-circulating SARS-CoV-2 variants, New York state, USA. Emerging Infectious Diseases, v. 28, n. 3, p. 650, 2022.
SHAN, B. et al. Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread. IEEE Journal of Biomedical and Health Informatics, v. 27, n. 6, p. 2693 - 2704, 2023.
SHI, Q. et al. COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study. JMIR Formative Research, v. 6, n. 6, p. e37858, 2022.
SULLIVAN, P. S. et al. A data visualization and dissemination resource to support HIV prevention and care at the local level: analysis and uses of the AIDSVu public data resource. Journal of Medical Internet Research, v. 22, n. 10, p. e23173, 2020.
SUPRIATNA, F. Z. et al. Communicating the High Susceptible Zone of COVID-19 and its Exposure to Population Number through a Web-GIS Dashboard for Indonesia Cases. International Journal of Technology, v. 13, n. 4, p. 706-716, 2022.
TEMEREV, A. et al. Geospatial model of COVID-19 spreading and vaccination with event Gillespie algorithm. Nonlinear Dynamics, v. 109, n. 1, p. 239-248, 2022.
WU, C. et al. Analyzing COVID‐19 using multisource data: An integrated approach of visualization, spatial regression, and machine learning. GeoHealth, v. 5, n. 8, p. e2021GH000439, 2021.
ZAÇAJ, O. et al. Comparative Approach of Tracking COVID-19 in Balkan Countries Using Interactive Web-Based Dashboard. Interdisciplinary Perspectives on Infectious Diseases, v. 2022, n. 6, 2022.
ZHAN, C. et al. Comparative study of COVID-19 pandemic progressions in 175 regions in Australia, Canada, Italy, Japan, Spain, UK and USA using a novel model that considers testing capacity and deficiency in confirming infected cases. IEEE Journal of Biomedical and Health Informatics, v. 25, n. 8, p. 2836-2847, 2021.
ZHOU, C. et al. COVID-19: challenges to GIS with big data. Geography and Sustainability, v. 1, n. 1, p. 77-87, 2020.
WANG, Jin-Long et al. Epidemiological characteristics of imported respiratory infectious diseases in China, 2014-2018. Infectious Diseases of Poverty, v. 11, n. 02, p. 63-71, 2022.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2025 Gabriel Moraes de Oliveira, Elisangela Gisele do Carmo, Isabel Cristina Siqueira da Silva

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Os autores que publicam na Revista Conexões: Ciência e Tecnologia concordam com os seguintes termos: Autores mantêm os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho licenciado sob uma licença Creative Commons Atribuição-NãoComercial-CompartilhaIgual 4.0 Internacional (CC BY -NC-SA 4.0) . Nossos artigos estão disponíveis gratuitamente e gratuitamente, com privilégios para atividades educacionais, pesqueiras e não comerciais.