COVID-19 Experience in the Philippines

This 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.

Provides a framework for designing and developing an operational disease surveillance dashboard in a health crisis Serves as a mini-handbook or toolkit on disease modeling and surveillance Includes source codes available for future disease surveillance needs

Autorentext

Regina Estuar is a full professor in the Department of Information Systems and Computer Science, Loyola Schools, Ateneo de Manila University, Philippines. She holds a senior fellow post at the Philippine Public Safety College where she serves as an adviser to information and communication technology (ICT) tools for disaster resilience. In 2019, she received The Outstanding Women in the Nation's Service (TOWNS) award for her contribution in science and technology, specifically in the design of ICT-based platforms for public health, disease surveillance and disaster response. In 2012, she founded the Ateneo Social Computing Laboratory to establish a firm foundation in the development of social computing platforms through the lens of social psychology. Since 2007, she has managed the Ateneo Center for Computing Competency and Research (formerly the Ateneo Java Wireless Competency Center), a research incubator laboratory that has been producing technologies for social good and social change. In 2020, she led a team of mathematical modelers, data scientists, software engineers and epidemiologists, in designing, developing, deploying and maintaining a scenario-based analytics and disease modeling platform to aid in the management of the COVID-19 pandemic. Elvira P. de Lara-Tuprio is a professor and former chair of the Department of Mathematics, School of Science and Engineering at the Ateneo de Manila University. She has been teaching mathematics since 1990 and financial mathematics, including financial derivatives and risk management, since 2009. She was awarded a FINEX-Citibank Outstanding Finance Educator Award for the National Capital Region in 2011. Outside academe, she is a finance practitioner, having served as a consultant and reviewer of risk management models for a number of local banks since 2010. Aside from finance, her research includes epidemiological modeling and flood hazard modeling. She is also actively involved in mathematics education, through teacher training and reviewing of learning materials and teachers' guides for public schools. She is an author of mathematics textbooks designed for high school and college students. Dr. Elvie heads the mathematical modeling team for FASSSTER.

Inhalt

Chapter 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.