+ A
A -
tribune news network
doha
STUDENTS at the College of Science and Engineering (CSE), part of Hamad Bin Khalifa University (HBKU), have successfully developed and implemented a statistical inference system that can provide projections of COVID-19 cases in Qatar.
An outcome of the students’ learning process in CSE’s Master of Science in Data Science and Engineering programme, the online platform aims to give users all the necessary information to understand the severity of the pandemic at a given time and help avert activities that may cause additional exposure to the virus. “The main objective of the application is to provide a data-driven dashboard for monitoring the COVID-19 pandemic in Qatar”, said Shehel Yoosuf, a graduate of the Master of Science in Data Science and Engineering and a candidate in HBKU’s PhD programme as of September, 2020. “The model presents daily statistics, provides a predictive number of new cases for the next 10 days and gives an estimate of the current basic reproductive number of these cases. This number tells us how fast the virus is spreading within the population.”
The model’s algorithms are based on the SIR model - a three-tier classification of the population into Susceptible, Infectious and Recovered (SIR), and key data derived from the daily number of positive cases. The system draws daily data from the Ministry of Public Health’s website; estimates variables of the infectious disease SIR model; makes predictions, and plots them based on these variables. It then feeds this information into the students’ online platform. “We were inspired by a global study that showed how government interventions affected the progress of the pandemic using this model for Europe”, said Yoosuf, who worked alongside classmate Ahmed Aziz on the project. “From the study, we realised we could get better predictions if we took into account nation-wide events and preventive measures because they tend to shift the spread rate of the pandemic, which can otherwise be assumed to be constant. We adapted the model to data from Qatar and extended it so that we can also account for the fact that daily positive cases are only a part of the infected compartment. There are many more people who are infected, but not tested.”
The model can be used to infer reasons as to why occasional spikes may be observed, or to measure the effectiveness of government interventions. It can also explore ‘what-if’ scenarios.
“One very effective measure was enforcing masks in public, which also coincided with further restrictions around the Eid period”, explained Yoosuf. “This may have contributed significantly to the downward trend we are seeing today. In contrast, the beginning of the Holy Month of Ramadan may have been a setback due to unhindered gatherings and shopping rushes. Similarly, we are modelling lockdown relaxation measures and closely monitoring changes in the reproductive number. Basic reproductive number estimates show that the virus has been in a decayed state since the end of May, and the first phase of easing the lockdown has been promising.”
According to Yoosuf, a second surge in cases is always possible, but the model can offer insights as to how the lift is faring, especially when compared to the early days of the pandemic. Post-lockdown, such models can provide tracking insights and lessons learned in the unfortunate case of a future pandemic.
“Our web application framework can be generalised for monitoring other infectious diseases or fluctuating waves of the current pandemic”, explained Yoosuf. “What makes it unique is that it incorporates governmental measures and other major events in Qatar, which can have a major impact on how the pandemic progresses. Our work is a data-driven way to monitor the past and present situation during a pandemic and make rough estimations on the spread of the disease in the future, based on the data we have.”
CSE’s Master of Science in Data Science and Engineering programme aims to help students build strong foundational knowledge in big data and data analysis. The college’s PhD programme in Computer Science and Engineering provides students with a broad understanding of the methods, technologies, and tools needed to succeed in the in-demand field of computer engineering.
doha
STUDENTS at the College of Science and Engineering (CSE), part of Hamad Bin Khalifa University (HBKU), have successfully developed and implemented a statistical inference system that can provide projections of COVID-19 cases in Qatar.
An outcome of the students’ learning process in CSE’s Master of Science in Data Science and Engineering programme, the online platform aims to give users all the necessary information to understand the severity of the pandemic at a given time and help avert activities that may cause additional exposure to the virus. “The main objective of the application is to provide a data-driven dashboard for monitoring the COVID-19 pandemic in Qatar”, said Shehel Yoosuf, a graduate of the Master of Science in Data Science and Engineering and a candidate in HBKU’s PhD programme as of September, 2020. “The model presents daily statistics, provides a predictive number of new cases for the next 10 days and gives an estimate of the current basic reproductive number of these cases. This number tells us how fast the virus is spreading within the population.”
The model’s algorithms are based on the SIR model - a three-tier classification of the population into Susceptible, Infectious and Recovered (SIR), and key data derived from the daily number of positive cases. The system draws daily data from the Ministry of Public Health’s website; estimates variables of the infectious disease SIR model; makes predictions, and plots them based on these variables. It then feeds this information into the students’ online platform. “We were inspired by a global study that showed how government interventions affected the progress of the pandemic using this model for Europe”, said Yoosuf, who worked alongside classmate Ahmed Aziz on the project. “From the study, we realised we could get better predictions if we took into account nation-wide events and preventive measures because they tend to shift the spread rate of the pandemic, which can otherwise be assumed to be constant. We adapted the model to data from Qatar and extended it so that we can also account for the fact that daily positive cases are only a part of the infected compartment. There are many more people who are infected, but not tested.”
The model can be used to infer reasons as to why occasional spikes may be observed, or to measure the effectiveness of government interventions. It can also explore ‘what-if’ scenarios.
“One very effective measure was enforcing masks in public, which also coincided with further restrictions around the Eid period”, explained Yoosuf. “This may have contributed significantly to the downward trend we are seeing today. In contrast, the beginning of the Holy Month of Ramadan may have been a setback due to unhindered gatherings and shopping rushes. Similarly, we are modelling lockdown relaxation measures and closely monitoring changes in the reproductive number. Basic reproductive number estimates show that the virus has been in a decayed state since the end of May, and the first phase of easing the lockdown has been promising.”
According to Yoosuf, a second surge in cases is always possible, but the model can offer insights as to how the lift is faring, especially when compared to the early days of the pandemic. Post-lockdown, such models can provide tracking insights and lessons learned in the unfortunate case of a future pandemic.
“Our web application framework can be generalised for monitoring other infectious diseases or fluctuating waves of the current pandemic”, explained Yoosuf. “What makes it unique is that it incorporates governmental measures and other major events in Qatar, which can have a major impact on how the pandemic progresses. Our work is a data-driven way to monitor the past and present situation during a pandemic and make rough estimations on the spread of the disease in the future, based on the data we have.”
CSE’s Master of Science in Data Science and Engineering programme aims to help students build strong foundational knowledge in big data and data analysis. The college’s PhD programme in Computer Science and Engineering provides students with a broad understanding of the methods, technologies, and tools needed to succeed in the in-demand field of computer engineering.
