Decoding Data: Dawn of syndromic surveillance for public health security

Written by Renz Miciel M. Trovela
Illustration by Sabrina Laceda
Published 2020 December 18

Disease surveillance is complementary with public health response; through statistics and data analysis of disease dynamics and trends, public health officials are able to use information for detection, planning, response, and prevention.[1] Data from disease surveillance systems become information that help authorities[1] and ordinary citizens to understand the nitty-gritty of certain diseases in the country like dengue, leptospirosis, and typhoid fever.

However, disease surveillance remains lacking in the Philippines.[1] In 2018, the Asia Pacific Observatory on Health Systems and Policies (APO)‎‎ released a review of the Philippine Health Systems that includes an assessment of the country’s epidemic surveillance. According to this, “Coordination with hospitals and LGUs continues to be a challenge to ensure timely detection and management of outbreaks.”[2]

Unfortunately, the coronavirus disease (COVID-19) pandemic hit the country when systems are still undergoing reassessments and improvements. Surveillance delays due to faulty systems badly crippled the country with delays in urgent decisions for safety precautions, delays in immediate isolation of exposed populations, shortage of healthcare facilities, and high burn-out rate of healthcare workers.[3] Truly, the need for revamping the country’s information systems is further emphasized in the middle of the battle against COVID-19.

In line with this, researchers from Ateneo de Manila University (ADMU) continue to contribute at the front line of disease surveillance amidst the pandemic. They developed a tool for surveillance of COVID-19 cases and other diseases across the country. This technology, powered by data science, aims to lead epidemiology and surveillance units (ESUs) and public health agencies to more efficient strategies towards public health security. Today, data science experts work side-by-side to deliver pandemic-related health trends and information “faster” than the spread of the virus.

Meeting FASSSTER

Feasibility Analysis of Syndromic Surveillance using Spatio Temporal Epidemiological Modeler for Early Detection of Diseases (FASSSTER) is a web application for generating disease models and spatio-temporal maps to visualize syndromic surveillance reports.[4] This can be integrated in present surveillance systems to generate public health research during health emergencies.[4] Through the aid of simulated representations, concerned units can devise evidence-informed safety protocols suitable to their respective communities.

FASSSTER is a project spearheaded by Dr. Ma. Regina Justina E. Estuar, a professor in the Department of Information Systems and Computer Science of the ADMU. She shared that the project started with the experience in 2012 for developing e-health platforms wherein there is “difficulty in disease monitoring because of lack in real time health information.”[4] In 2015, they began focusing on surveillance systems, an approach where disease indicators are monitored real-time to detect disease outbreaks.[5] This was approved in 2016 when the team began to study the use of disease models with Spatio Temporal Epidemiological Modeler (STEM), a tool that generates flexible mathematical models of infectious diseases.[6] Development of website and mobile interface, institutionalization, pilot testing, and training happened in the proceeding years.[4]

The platform was initially used to monitor dengue, measles, and typhoid fever in the Philippines.[7] However, when the first case of COVID-19 hit the headlines in January, Estuar proposed to the Department of Science and Technology-Philippine Council for Health Research and Development (DOST-PCHRD) that FASSSTER may be utilized to serve as an indicator of a potential outbreak of a certain disease in an area. Estuar and her team officially wrote a proposal in March and formed a group, named “FASSSTER than COVID-19”, to operate the disease surveillance platform.[4]

Currently, Ateneo Center for Computing Competency and Research (ACCCRe) works in collaboration with the University of the Philippines Manila – National Telehealth Center (UP-NTHC) and the Department of Health-Epidemiology Bureau (DOH-EB) and is funded by the DOST-PCHRD.[8] In order to project visualizations of scenarios, she said that FASSSTER uses data from the COVID system of the DOH-EB, information from TanodCOVID – a free SMS-based, self-screening application where locals report symptoms to their respective LGUs – and other data sources related to security, social, and economic data.[4]

Furthermore, Estuar explained that these data undergo an iterative standard process of data cleaning, feeding into a data warehouse, then storing for publishing in a website, and finally producing key statistics, LGU risk frameworks, epidemiological classifications, and health capacity projections.[4] “This platform is specifically designed for LGUs. So we have a public facing side which is the DOH NCOV Tracker and we have the LGU facing side and the agency facing side which is the FASSSTER platform. […] It’s a fast platform for decision making,” she added.[4]

Through this platform, people may be categorized into six compartments: susceptible, exposed, infectious asymptomatic, infectious symptomatic, confirmed, and recovered population. Estuar also stated that FASSSTER also provides LGU classification risks where it uses case doubling time.[4] Hypothetically, a municipality with cases that double every three days has a higher transmission rate than a municipality with cases that double monthly. It also utilizes critical care utilization rate where the availability of health care facilities in a location are put into consideration. Moreover, she said that FASSSTER can serve as a simulation platform as users can select scenarios based on health indicators such as personal protection, community quarantine, and health interventions to visualize the peak of the curve.[4]

Challenging breakthrough

Reviving a platform to monitor COVID-19 cases in the country, the people behind FASSSTER experienced challenges in bridging data for decision making.[4] One challenge Estuar emphasized is that scientists are still in the process of fully understanding COVID-19 while it has affected millions globally. In the data level, she said that it is a challenge to ensure data integrity, consistency, system streamlining, and model building since the present platform functions as an engine where data should be processed into the system on a daily basis. Additionally, COVID-19 is dynamic, thus more models are significant to view the disease in different aspects.

Another challenge she mentioned is that at the rear of this platform are human beings. There is an apparent challenge in operating the platform but there is also a challenge when they realize how life must continue. “However, there is a need for analytics for decision making that is grounded in science. There’s a need for an engine that cannot be turned off to adjust to everyday feedback, to synchronize with real data, to provide baseline numbers for simulated scenarios, […]” she said.[4]

Nevertheless, FASSSTER continues to lay a reliable foundation for authorities involved in deciding for community safety. The approval of the extension of the enhanced community quarantine earlier April was backed by the modeler, thus providing ample time for different agencies to heighten the health capacity system against the pandemic.[8] On September 4, the platform was also officially turned over to DOH for wider surveillance of COVID-19 in the country.[9]

During a virtual event on November 9 to 11, FASSSTER was among the recognized technologies for the 5th National Research and Development Conference (NRDC).[10] The event, hosted by DOST, puts a spotlight on scientific innovations geared towards the health sector that were supported by DOST-PCHRD. In her presentation, Estuar mentioned that as the adoption of FASSSTER in surveillance units constantly increases towards the end of the year, they enhanced Tanod COVID which is presently connected to the contact tracing application Kontra COVID of DOH, hence LGUs will be able to monitor arising hotspots of the disease and provide prompt healthcare interventions necessary.[7]

In the heat of the battle against COVID-19, authorities have witnessed the importance of taking the next steps to securing public safety with data and information. Hopefully, the support for disease surveillance will not cease once the curve has flattened; instead, authorities should take this crisis as an urgent undertaking to overhaul health systems in the Philippines. This is a wake-up call to allocate more support for research and innovation on disease monitoring platforms: for early detection of outbreaks, for more efficient emergency response, and for the utmost benefit of Filipino families

References

1.Department of Health. Manual of Procedures for the Philippine Integrated Disease Surveillance and Response (PIDSR) 3rd Edition [Internet]. Manila: DOH; 2014 [updated 2017 May 17, cited 2020 Dec 3]. Available from: https://www.doh.gov.ph/node/9985
2. Dayrit MM, Lagrada LP, Picazo OF, Pons MC, Villaverde MC. The Philippines health system review [Internet]. New Delhi: WHO South-East Asia Regional Office; 2018 [cited 2020 Dec 3]. Available from: https://apps.who.int/iris/handle/10665/274579
3. Haw NJ, Uy J, Sy KT, Abrigo MR. Epidemiological profile and transmission dynamics of COVID-19 in the Philippines. Epidemiology and Infection [Internet]. 2020 [cited 2020 Dec 3];148(e204). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506175/
4. Estuar MR. The FASSSTER Platform [web streaming video]. [place unknown]: Archium Ateneo; 2020 [cited 2020 Dec 3]. Available from: https://archium.ateneo.edu/acts-of-magis/1
5. Department of Health & Human Resources. §64-7-12 Syndromic Surveillance [Internet]. West Virginia: DHHR; 2013 [updated 2013 Jul 19; cited 2020 Dec 3]. Available from: https://dhhr.wv.gov/oeps/disease/Surveillance/documents/syndromic-surveillance/ss-reporting.pdf
6. Eclipse Foundation. The Spatiotemporal Epidemiological Modeler (STEM) Project [Internet]. Ontario: Eclipse Foundation; [date unknown] [cited 2020 Dec 3]. Available from: https://www.eclipse.org/stem/
7. Department of Science & Technology. 5th National Research and Development Conference (Day 1) [web streaming video]. [place unknown]: DOST NRDC; 2020 [cited 2020 Dec 3]. Available from: https://fb.watch/27z9Zv1vB0/
8. Itulid AC, Gonzales CJ. FASSSTER than COVID-19: The science used to forecast COVID-19 in PH [Internet]. [place unknown]: DOST PCHRD; 2020 Apr 8 [cited 2020 Dec 3]. Available from: http://www.pchrd.dost.gov.ph/index.php/news/6532-fassster-than-covid-19-the-science-used-to-forecast-covid-19-in-ph
9. Gonzales CJ. DOH adopts DOST, Ateneo FASSSTER disease surveillance tool [Internet]. [place unknown]: DOST PCHRD; 2020 Sep 3 [cited 2020 Dec 3]. Available from: http://pchrd.dost.gov.ph/index.php/news/6596-doh-adopts-dost-ateneo-fassster-disease-surveillance-tool
10. Dimailig, CJ. 5th National R&D Conference to showcase breakthrough technologies in health [Internet]. [place unknown]: DOST PCHRD; 2020 Nov 5 [cited 2020 Dec 3]. Available from: http://pchrd.dost.gov.ph/index.php/news/6607-5th-national-r-d-conference-to-showcase-breakthrough-technologies-in-health

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