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QBRI’s webinar series discusses coronavirus predictive models, career paths for scientists

QBRI’s webinar series discusses coronavirus 
predictive models, career paths for scientists

Tribune News Network
Doha
Efforts to help policy makers accurately predict and respond to the outbreaks of COVID-19, as well as to support the next generation in their careers in biotechnology, provided the context for two webinars recently organised by the Qatar Biomedical Research Institute (QBRI) at Hamad Bin Khalifa University (HBKU).
QBRI held a webinar titled Pursuing Careers in the Biotech Industry recently as part of its QBRI Talks Webinar series ‘Guide for Early Career STEM Researchers Career Development.’ During the webinar, Dr Lawrence Stanton, Director of the Neurological Disorders Research Center at QBRI, shared his perspective on the careers of the biotechnology industry in particular the transition from academia to industry and the key differences between these two research environments.
Dr. Stanton also discussed the skills needed to pursue a career in the biotech industry, which has played a major role globally in developing diagnoses, therapies, and coronavirus vaccines. The session was moderated by Dr. Adviti Naik, Postdoctoral Researcher at QBRI.
Through the QBRI Talks webinar series, the Institute features inspiring talks from pioneers who discuss their personal career paths in science, with the aim of encouraging young minds to actively pursue their personal and professional development.
Simultaneously with the career development series, QBRI is also hosting another webinar series dedicated to highlighting various topics of current global relevance. COVID-19 Outbreak Prediction and Assessment of Prevention Policies: A Data Science Approach was held on 9 November and two COVID-19 prediction models were presented to the public. The first module used a deep learning approach to predict the cumulative number of cases based on data from countries with similar demographic, socio-economic and health indicators. The model also adopted lockdown measures as input.
The second approach on the other hand, used a deep learning model to evaluate and predict the impact of various lock-down policies on daily COVID-19 cases. This was achieved by grouping countries with similar lock-down policies, then by training a predictive model based on daily cases in each cluster along with data describing their lock-down policies. Once the model has been trained, it is used to evaluate scenarios related to lock-down policies and to investigate their impact on the predicted COVID-19 cases.
Prediction models have been demonstrated by Dr. Abdelkarim Erradi, Associate Professor at the Department of Computer Science and Engineering at Qatar University, whose research activities and interests focus on service-oriented computing, cloud services composition and mobile crowd sensing. He was joined by Dr. Ahmed Ben Said, Data Scientist at Qatar University, who has an interest in machine learning, computer vision, urban computing and mobile health systems.
During the compelling webinar, Qatar was used as a case study to highlight the potential consequences of lifting restrictions without taking full account of what the data reveals about the current status of COVID-19.
Dr. Erradi stressed that using the power of data science and recent advances in machine learning can help us to gain a better understanding of the outbreak of COVID-19. The trained prediction model allows the evaluation of various scenarios related to lock-down policies, such as easing travel restrictions, and quantifying their impact on the predicted COVID-19 cases. It also recognised funding support from the Qatar National Research Fund (QNRF) as part of the Rapid Response Call (RPC) to address COVID-19.
Dr. Julie Decock, a scientist at QBRI and moderator of the webinar, said: “While policy-making responses to COVID-19 vary from country to country, they all share the need for accurate forecasting of coronavirus spread. This webinar has shown that data science approaches have an important role to play in predicting and tracking the spread of COVID-19. As always, we enjoyed hosting our colleagues from the Qatar University.”
For more information on QBRI’s state-of-the-art research facilities and centers for research on diabetes, cancer and neurological disorders, please visit qbri.hbku.edu.qa.

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