LEADER 04389nam 22006615 450 001 9910736985003321 005 20230807065000.0 010 $a981-9931-53-3 024 7 $a10.1007/978-981-99-3153-8 035 $a(MiAaPQ)EBC30679944 035 $a(Au-PeEL)EBL30679944 035 $a(OCoLC)1393306579 035 $a(DE-He213)978-981-99-3153-8 035 $a(PPN)272263982 035 $a(CKB)27942475000041 035 $a(EXLCZ)9927942475000041 100 $a20230807d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCOVID-19 Experience in the Philippines $eResponse, Surveillance and Monitoring Using the FASSSTER Platform /$fedited by Maria Regina Justina Estuar, Elvira De Lara-Tuprio 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (169 pages) 225 1 $aDisaster Risk Reduction, Methods, Approaches and Practices,$x2196-4114 311 08$aPrint version: Estuar, Maria Regina Justina COVID-19 Experience in the Philippines Singapore : Springer,c2023 9789819931521 320 $aIncludes bibliographical references. 327 $aChapter 1. Origins of FASSSTER -- Chapter 2. Management of COVID-19 Data for the FASSSTER Platform -- Chapter 3. FASSSTER Data Pipeline and DevOps -- Chapter 4. Disease Surveillance Metrics and Statistics -- Chapter 5. Effective Reproduction Number Rt -- Chapter 6. The FASSSTER SEIR Model -- Chapter 7. Geospatial and Spatio-Temporal Models. . 330 $aThis book provides an overview of the extensive work that has been done on the design and implementation of the COVID-19 Philippines Local Government Unit Monitoring Platform, more commonly known as Feasibility Analysis of Syndromic Surveillance Using Spatio-Temporal Epidemiological Modeler for Early Detection of Diseases (FASSSTER). The project began in 2016 as a pilot study in developing a multidimensional approach in disease modeling requiring the development of an interoperable platform to accommodate input of data from various sources including electronic medical records, various disease surveillance systems, social media, online news, and weather data. In 2020, the FASSSTER platform was reconfigured for use in the COVID-19 pandemic. Using lessons learned from the previous design and implementation of the platform toward its full adoption by the Department of Health of the Philippines, this book narrates the story of FASSSTER in two main parts. Part I provides a historical perspective of the FASSSTER platform as a modeling and disease surveillance system for dengue, measles and typhoid, followed by the origins of the FASSSTER framework and how it was reconfigured for the management of COVID-19 information for the Philippines. Part I also explains the different technologies and system components of FASSSTER that paved the way to the operationalization of the FASSSTER model and allowed for seamless rendering of projections and analytics. Part II describes the FASSSTER analytics and models including the Susceptible-Exposed-Infected-Recovered (SEIR) model, the model for time-varying reproduction number, spatiotemporal models and contact tracing models, which became the basis for the imposition of restrictions in mobility translated into localized lockdowns. 410 0$aDisaster Risk Reduction, Methods, Approaches and Practices,$x2196-4114 606 $aNatural disasters 606 $aPublic health 606 $aElectronic data processing?Management 606 $aDiseases?Animal models 606 $aNatural Hazards 606 $aPublic Health 606 $aIT Operations 606 $aDisease Models 615 0$aNatural disasters. 615 0$aPublic health. 615 0$aElectronic data processing?Management. 615 0$aDiseases?Animal models. 615 14$aNatural Hazards. 615 24$aPublic Health. 615 24$aIT Operations. 615 24$aDisease Models. 676 $a362.1962414009599 702 $aEstuar$b Maria Regina Justina 702 $aDe Lara-Tuprio$b Elvira 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910736985003321 996 $aCOVID-19 Experience in the Philippines$93424590 997 $aUNINA