FASSSTER: The science that forecasts COVID-19 in PH
Published Apr 8, 2020 12:00 am
By Dhel Nazario
Filipino researchers have devised a web-based disease surveillance platform that enables evidence-based forecasts of possible cases and scenarios on the spread of COVID-19 in the country called the Feasibility Analysis of Syndromic Surveillance using Spatio-Temporal Epidemiological Modeler or FASSSTER.
(DOST-PCHRD / MANILA BULLETIN)
This same technology was utilized by the Inter-agency Task Force (IATF) in their recommendations and the government’s decision to extend the enhanced community quarantine (ECQ) until April 30. It made use of data generated from FASSSTER, alongside health capacity and economic models developed by volunteers from Asian Institute of Management (AIM) and the Philippine Institute of Development Studies (PIDS).
FASSSTER was developed by Ateneo Center for Computing Competency and Research (ACCCRe) of Ateneo de Manila University in collaboration with the University of the Philippines Manila - National Telehealth Center (UP-NTHC) and the Department of Health-Epidemiology Bureau. It was funded by the Department of Science and Technology-Philippine Council for Health Research and Development (DOST-PCHRD).
According to DOST-PCHRD, this technology allows policymakers to understand the pandemic at the national, regional, and local levels; assess the effects of the preventive measures in place; and use best practices in specific communities.
When the novel coronavirus or COVID-19 ravaged the country, FASSSTER determined appropriate model parameters that can be used to forecast future numbers of infections, confirmed positive cases, deaths, and recoveries.
FASSSTER dashboard generates disease models that enable users to project the effect of interventions such as community quarantine, social distancing, and optimal testing on the total number of confirmed cases, deaths, and recoveries over time.
Another feature of FASSSTER to address COVID-19 is the TanodCOVID, a self-reporting application which enables constituents to report COVID-19 related symptoms to their local health authorities. The data will then feed into the FASSSTER LGU dashboard which serves as a tracker for confirmed cases upon validation of Provincial/City/Municipality Epidemiology Surveillance Units. This will be useful in contact tracing and monitoring identified contacts to ensure that they are appropriately quarantined and managed.
Projected scenarios
FASSSTER has the capability to estimate the number of confirmed positive cases in the National Capital Region (NCR), give the initial interventions and precautionary measures implemented against COVID-19.
According to DOST-PCHRD, data gathered enabled the FASSSTER team to generate models to predict the peak number of cases and peak dates in four scenarios. The tool showed that the peak will reach around two million confirmed cases on May 30 if no ECQ was implemented. If, however, ECQ is lifted on April 14, with health capacity at 25 percent, cases will be slightly reduced to 1.5 million on July 31.
If the health capacity is increased to 50 percent, at the same lift date, a huge decline in confirmed cases is predicted, which is now projected to reach 15,000 on May 4. The last scenario showed that cases will drop to 6,800 on May 21 if the ECQ is lifted on April 30, provided that the health system capacity reaches 75 percent.
The health system capacity can be further explained by two factors. First is the testing capacity, or the availability of test kits, testing centers with quantitative polymerase chain reaction (QPCR) machines, trained health workers to perform testing, and turnaround time of test results. Next is the health management capacity, or the availability of health facilities, medical devices, and health workers to manage the confirmed positive cases so that they are isolated and given proper medical care.
Peak date is the day with the highest number of cases. The projected maximum number of active confirmed positive cases for the duration of the outbreak is referred to as the “Peak of active confirmed cases.”
Peak date is an important factor for epidemiologists in assessing the spread of the virus, an epidemic starts to decline after the peak date. The goal of the government is to “flatten the curve” or to take the necessary preventive measures before hospitals and other healthcare facilities are overburdened with positive cases.
Aside from these data, FASSSTER is also able to project the number of people who will be exposed, symptomatic and asymptomatic infectious, confirmed positive cases, recovered, deaths, and projected mild, severe, and critical cases.
With these projections, the extension of the enhanced community quarantine was approved by the President to provide ample time for the different agencies to strengthen the capacity of the health system against the pandemic.
The initial forecast shows the level of the country's health systems capacity in containing and managing the infection. It also highlighted the need to increase the number of quarantine facilities, implement stronger healthcare programs and regular assessment of health capacities.
Disease modeling is an iterative process. As data comes in, the model will be updated to reflect the latest projections, where performance can be measured and decisions on planned interventions is driven by science.
(DOST-PCHRD / MANILA BULLETIN)
This same technology was utilized by the Inter-agency Task Force (IATF) in their recommendations and the government’s decision to extend the enhanced community quarantine (ECQ) until April 30. It made use of data generated from FASSSTER, alongside health capacity and economic models developed by volunteers from Asian Institute of Management (AIM) and the Philippine Institute of Development Studies (PIDS).
FASSSTER was developed by Ateneo Center for Computing Competency and Research (ACCCRe) of Ateneo de Manila University in collaboration with the University of the Philippines Manila - National Telehealth Center (UP-NTHC) and the Department of Health-Epidemiology Bureau. It was funded by the Department of Science and Technology-Philippine Council for Health Research and Development (DOST-PCHRD).
According to DOST-PCHRD, this technology allows policymakers to understand the pandemic at the national, regional, and local levels; assess the effects of the preventive measures in place; and use best practices in specific communities.
When the novel coronavirus or COVID-19 ravaged the country, FASSSTER determined appropriate model parameters that can be used to forecast future numbers of infections, confirmed positive cases, deaths, and recoveries.
FASSSTER dashboard generates disease models that enable users to project the effect of interventions such as community quarantine, social distancing, and optimal testing on the total number of confirmed cases, deaths, and recoveries over time.
Another feature of FASSSTER to address COVID-19 is the TanodCOVID, a self-reporting application which enables constituents to report COVID-19 related symptoms to their local health authorities. The data will then feed into the FASSSTER LGU dashboard which serves as a tracker for confirmed cases upon validation of Provincial/City/Municipality Epidemiology Surveillance Units. This will be useful in contact tracing and monitoring identified contacts to ensure that they are appropriately quarantined and managed.
Projected scenarios
FASSSTER has the capability to estimate the number of confirmed positive cases in the National Capital Region (NCR), give the initial interventions and precautionary measures implemented against COVID-19.
According to DOST-PCHRD, data gathered enabled the FASSSTER team to generate models to predict the peak number of cases and peak dates in four scenarios. The tool showed that the peak will reach around two million confirmed cases on May 30 if no ECQ was implemented. If, however, ECQ is lifted on April 14, with health capacity at 25 percent, cases will be slightly reduced to 1.5 million on July 31.
If the health capacity is increased to 50 percent, at the same lift date, a huge decline in confirmed cases is predicted, which is now projected to reach 15,000 on May 4. The last scenario showed that cases will drop to 6,800 on May 21 if the ECQ is lifted on April 30, provided that the health system capacity reaches 75 percent.
The health system capacity can be further explained by two factors. First is the testing capacity, or the availability of test kits, testing centers with quantitative polymerase chain reaction (QPCR) machines, trained health workers to perform testing, and turnaround time of test results. Next is the health management capacity, or the availability of health facilities, medical devices, and health workers to manage the confirmed positive cases so that they are isolated and given proper medical care.
Peak date is the day with the highest number of cases. The projected maximum number of active confirmed positive cases for the duration of the outbreak is referred to as the “Peak of active confirmed cases.”
Peak date is an important factor for epidemiologists in assessing the spread of the virus, an epidemic starts to decline after the peak date. The goal of the government is to “flatten the curve” or to take the necessary preventive measures before hospitals and other healthcare facilities are overburdened with positive cases.
Aside from these data, FASSSTER is also able to project the number of people who will be exposed, symptomatic and asymptomatic infectious, confirmed positive cases, recovered, deaths, and projected mild, severe, and critical cases.
With these projections, the extension of the enhanced community quarantine was approved by the President to provide ample time for the different agencies to strengthen the capacity of the health system against the pandemic.
The initial forecast shows the level of the country's health systems capacity in containing and managing the infection. It also highlighted the need to increase the number of quarantine facilities, implement stronger healthcare programs and regular assessment of health capacities.
Disease modeling is an iterative process. As data comes in, the model will be updated to reflect the latest projections, where performance can be measured and decisions on planned interventions is driven by science.