van der Schaar Lab

New tool tackles the “What if?” questions of COVID-19

The van der Schaar Lab has developed Policy Impact Predictor (PIP), an advanced machine learning tool that can answer the “What if?” questions surrounding COVID-19 policy-making.

As world leaders consider when and how to lift the lockdown measures in their respective countries, they have been presented with a number of policy levers. They will need to repeatedly decide how many of these levers to pull, when to pull them, and how far. These choices are exceptionally challenging in the absence of informed guidance regarding likely outcomes of any specific policy combination.

PIP offers a unique solution to this problem: it allows users to generate custom projections for policy combinations of their choosing. These projections are based on learnings from policy decisions made around the world, which are then applied on a local basis while factoring in the characteristics of each individual country. For example, PIP can estimate what would have happened if Italy’s government had waited a week before imposing lockdown measures, or predict what would happen if India were to loosen all existing spread prevention policies.

PIP’s greatest strength is that it can issue predictions based on counterfactual scenario analysis. This enables us to understand not only what the future could look like given a certain set of policies, but also what the present could have looked like if things had been done differently.

– Prof. Mihaela van der Schaar

PIP will soon be made available as an interactive tool, but in the meantime several initial predictions for the United Kingdom have been made available at this stage. These predictions show outcomes for three potential policy scenarios by the end of August, resulting in daily COVID-19 deaths remaining stable at 200, drifting upward to roughly 400, or rising markedly to 750.

To find out more about the van der Schaar Lab’s work related to the COVID-19 pandemic, visit our dedicated page here.

Ahmed Alaa

Ahmed Alaa

Ahmed M. Alaa is a Postdoctoral Scholar at the ECE Department, University of California, Los Angeles (UCLA), and an affiliated Postdoctoral Researcher at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

His primary research focus has been on causal inference, automated machine learning, uncertainty quantification and time-series analysis.

He has published papers in several leading machine learning conferences including NeurIPS, ICML, ICLR and AISTATS.

Zhaozhi Qian

Zhaozhi Qian

After obtaining a MSc in Machine Learning at UCL, Zhaozhi Qian started his career as a data scientist in the largest mobile gaming company in Europe. Three years later, he found it might be more fulfilling to apply AI to cure cancer than to make the gamers hit the purchase button 1% more often.

He thus joined the group in 2019 as a PhD student focusing on robust and interpretable learning for longitudinal data. So far, his work has included inferring latent disease interaction networks from Electronic Health Records, uncovering the causal structure between events that unfold over time, and calibrating the predictive uncertainty under domain shift.

Zhaozhi also worked as a contractor in the NHS during the COVID-19 pandemic contributing his analytical skills to the national response to the pandemic.

Mihaela van der Schaar

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

Nick Maxfield

Nick Maxfield

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.