Abstract
One of the main functions of the FASSSTER platform is to provide Spatio-Temporal approach in understanding disease spread. In fact, the name of the platform when spelled out explicitly indicates Spatio-Temporal analysis. Specifically, the original version of FASSSTER made use of Spatio-Temporal Epidemiological Modeler (STEM) to visualize the spread of dengue, measles, and typhoid that is derived from the SEIR projections. Given the limitations in using STEM for spatial and spatio-temporal analysis for COVID-19 cases in the Philippines, more appropriate models were developed to accommodate the need for better visualization as well as a better prediction on the spread of the disease.
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References
Anselin L (1995) Local indicators of spatial association-LISA. Geogr Anal 27(2):93–115
Blangiardo M, Cameletti M (2015) Spatial and spatio-temporal Bayesian models with R-INLA. John Wiley & Sons
Blangiardo M, Cameletti M, Baio G, Rue H (2013) Spatial and spatio-temporal models with R-INLA. Spat Spat-Temporal Epidemiol 7:39–55. https://pubmed.ncbi.nlm.nih.gov/24377114/
Brauer F (2008) Compartmental models in epidemiology. In: Mathematical epidemiology. Springer, pp 19–79
Dicker RC, Coronado F, Koo D, Parrish RG (2006) Principles of epidemiology in public health practice; an introduction to applied epidemiology and biostatistics
Elliott P, Wartenberg D (2004) Spatial epidemiology: current approaches and future challenges. Environ Health Perspect 112(9):998–1006
Grimm V, Mengel F, Schmidt M (2021) Extensions of the SEIR model for the analysis of tailored social distancing and tracing approaches to cope with covid-19. Sci Rep 11(1):1–16
Holtz D, Zhao M, Benzell SG, Cao CY, Rahimian MA, Yang J, Allen J, Collis A, Moehring A, Sowrirajan T et al (2020) Interdependence and the cost of uncoordinated responses to covid-19. Proc Natl Acad Sci 117(33):19837–19843
Jaya IGNM, Folmer H (2021) Bayesian spatiotemporal forecasting and mapping of covid-19 risk with application to west java province, indonesia. J Reg Sci
Lawson AB (2018) Bayesian disease mapping: hierarchical modeling in spatial epidemiology. CRC Press
Lin Q, Zhao S, Gao D, Lou Y, Yang S, Musa SS, Wang MH, Cai Y, Wang W, Yang L et al (2020) A conceptual model for the coronavirus disease 2019 (covid-19) outbreak in Wuhan, China with individual reaction and governmental action. Int J Infect Dis 93:211–216
Morgara P (2019) Geospatial health data: modeling and visualization with R-INLA and shiny. Chapman & Hall/CRC Biostatistics Series. https://www.paulamoraga.com/book-geospatial/sec-arealdataexamplespatial.html
Poletto C, Scarpino SV, Volz EM (2020) Applications of predictive modelling early in the covid-19 epidemic. Lancet Digit Health 2(10):e498–e499
RamÃrez-Aldana R, Gomez-Verjan JC, Bello-Chavolla OY (2020) Spatial analysis of covid-19 spread in Iran: insights into geographical and structural transmission determinants at a province level. PLoS Negl Trop Dis 14(11):e0008875
Salva Villarama EP, Lopez EB, Sayo AR, Seposo X, Ariyoshi K, Smith C (2021) Covid-19 is moving to high-density, poor residential areas in metropolitan manila, philippines. West Pac Surveill Response J 11(5)
Sturniolo S, Waites W, Colbourn T, Manheim D, Panovska-Griffiths J (2021) Testing, tracing and isolation in compartmental models. PLoS Comput Biol 17(3):e1008633
Tang L, Zhou Y, Wang L, Purkayastha S, Zhang L, He J, Wang F, Song PXK (2020) A review of multi-compartment infectious disease models. Int Stat Rev 88(2):462–513
Tolles J, Luong T (2020) Modeling epidemics with compartmental models. Jama 323(24):2515–2516
Uyheng J, Pulmano CE, Estuar MRJ (2020) Deploying system dynamics models for disease surveillance in the Philippines. International conference on social computing. Springer, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp 35–44
Vallejo BM Jr, Ong RAC (2020) Policy responses and government science advice for the covid 19 pandemic in the Philippines: January to April 2020. Prog Disaster Sci 7:100115
Wang L, Xu C, Wang J, Qiao J, Yan M, Zhu Q (2021) Spatiotemporal heterogeneity and its determinants of covid-19 transmission in typical labor export provinces of china. BMC Infect Dis 21(1):1–12
Zhang X, Rao H, Wu Y, Huang Y, Dai H (2020) Comparison of spatiotemporal characteristics of the covid-19 and SARS outbreaks in mainland China. BMC Infect Dis 20(1):1–7
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Benito, D.J., Yao, L.F., Uyheng, J., de Lara-Tuprio, E., Pulmano, C., Estuar, M.R. (2023). Geospatial and Spatio-Temporal Models. In: Estuar, M.R.J., De Lara-Tuprio, E. (eds) COVID-19 Experience in the Philippines. Disaster Risk Reduction. Springer, Singapore. https://doi.org/10.1007/978-981-99-3153-8_7
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