MalariaScan: aplicativo web para detecção e visualização de aglomerados espaço-temporais de casos de malária na Fronteira Franco-Brasileira
Resumo
Este trabalho apresenta o MalariaScan, aplicativo web desenvolvido em R/Shiny e Docker que integra o SaTScan™ para detectar aglomerados espaço-temporais de malária na fronteira Brasil–Guiana Francesa, apoiando a visualização dinâmica de dados e a vigilância epidemiológica.Referências
Aboushady, A. T., et al. (2025). “The use of SatScan software to map spatiotemporal trends and detect disease clusters: a systematic review”. Communications Medicine, 5(1), 82.
Da Cruz Franco, V., et al. (2019). “Complex malaria epidemiology in an international border area between Brazil and French Guiana: challenges for elimination”. Tropical medicine and health, 47(1), 24.
Kulldorff, M., et al. (2005). “A space–time permutation scan statistic for disease outbreak detection”. PLoS medicine, 2(3), e59.
Kulldorff, Martin. (2022). “SaTScanTM user guide”.
Levin-Rector, A., et al. (2024). “Prospective spatiotemporal cluster detection using SaTScan: tutorial for designing and fine-tuning a system to detect reportable communicable disease outbreaks”. JMIR public health and surveillance, 10(1), e50653.
Peng, R. D. (2011). “Reproducible research in computational science”. Science, 334(6060), 1226-1227.
Potdar, A. M., et al. (2020). “Performance evaluation of docker container and virtual machine”. Procedia Computer Science, 171, 1419-1428.
Saldanha, R., et al. (2020). Contributing to elimination of cross-border malaria through a standardized solution for case surveillance, data sharing, and data interpretation: development of a cross-border monitoring system. JMIR public health and surveillance, 6(3), e15409.
Shneiderman, B. (2003). “The eyes have it: A task by data type taxonomy for information visualizations”. In The craft of information visualization (pp. 364-371). Morgan Kaufmann.
Wasserstein, R. L., et al. (2019). Moving to a world beyond “p<0.05”. The American Statistician, 73(sup1), 1-19.
Yeng, P. K., et al. (2020). Cluster detection mechanisms for syndromic surveillance systems: systematic review and framework development. JMIR public health and surveillance, 6(2), e11512.
Da Cruz Franco, V., et al. (2019). “Complex malaria epidemiology in an international border area between Brazil and French Guiana: challenges for elimination”. Tropical medicine and health, 47(1), 24.
Kulldorff, M., et al. (2005). “A space–time permutation scan statistic for disease outbreak detection”. PLoS medicine, 2(3), e59.
Kulldorff, Martin. (2022). “SaTScanTM user guide”.
Levin-Rector, A., et al. (2024). “Prospective spatiotemporal cluster detection using SaTScan: tutorial for designing and fine-tuning a system to detect reportable communicable disease outbreaks”. JMIR public health and surveillance, 10(1), e50653.
Peng, R. D. (2011). “Reproducible research in computational science”. Science, 334(6060), 1226-1227.
Potdar, A. M., et al. (2020). “Performance evaluation of docker container and virtual machine”. Procedia Computer Science, 171, 1419-1428.
Saldanha, R., et al. (2020). Contributing to elimination of cross-border malaria through a standardized solution for case surveillance, data sharing, and data interpretation: development of a cross-border monitoring system. JMIR public health and surveillance, 6(3), e15409.
Shneiderman, B. (2003). “The eyes have it: A task by data type taxonomy for information visualizations”. In The craft of information visualization (pp. 364-371). Morgan Kaufmann.
Wasserstein, R. L., et al. (2019). Moving to a world beyond “p<0.05”. The American Statistician, 73(sup1), 1-19.
Yeng, P. K., et al. (2020). Cluster detection mechanisms for syndromic surveillance systems: systematic review and framework development. JMIR public health and surveillance, 6(2), e11512.
Publicado
01/06/2026
Como Citar
CASTILLO, Maira Alejandra Moreno; BARCELLOS, Christovam.
MalariaScan: aplicativo web para detecção e visualização de aglomerados espaço-temporais de casos de malária na Fronteira Franco-Brasileira. In: WORKSHOP DE COMPUTAÇÃO APLICADA ÀS DOENÇAS TROPICAIS NEGLIGENCIADAS - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 298-303.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2026.25332.
