van der Schaar Lab

Paper on COVID-19 risk factors in Brazil published in Nature Scientific Reports

A first-of-its-kind COVID-19 analysis authored jointly by the van der Schaar Lab, the University of Cambridge’s Department of Medicine, and a group of Brazilian researchers has been published in Nature Scientific Reports (editor-in-chief: Richard White).

The goal of this research was to provide the scientific community and, in particular, the Brazilian authorities with a ranking of the most important social, health, and economic risk factors related to COVID-19.

Building on previous statistical research (published in The Lancet Global Health) to identify the importance of ethnicity and socioeconomic status in determining outcome, this new study applies machine learning techniques to the Brazilian SIVEP-Gripe respiratory infection surveillance dataset to study demographic, patient, socioeconomic and organizational structure influences on COVID-19 outcome.

The authors concluded that socioeconomic and structural factors are as important as biological factors in determining COVID-19 outcomes in Brazil. Particularly important factors were: the state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity was also confirmed to be more important than comorbidities but less than the aforementioned factors.

This is the first known study of its kind to have been conducted in Brazil. Further details can be found below.

Comparing COVID-19 risk factors in Brazil using machine learning:
the importance of socioeconomic, demographic and structural factors

Pedro Baqui, Valerio Marra, Ahmed M. Alaa, Ioana Bica, Ari Ercole, Mihaela van der Schaar

Nature Scientific Reports


For a full list of the van der Schaar Lab’s publications, click here.

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

Ioana Bica

Ioana Bica is a second year PhD student at the University of Oxford and at the Alan Turing Institute. She has previously completed a BA and MPhil in Computer Science at the University of Cambridge where she has specialised in machine learning and its applications to biomedicine.

Ioana’s PhD research focuses on building machine learning methods for causal inference and individualised treatment effect estimation from observational data. In particular, she has developed methods capable of estimating the heterogeneous effects of time-dependent treatments, thus enabling us to determine when to give treatments to patients and how to select among multiple treatments over time.

Recently, Ioana has started working on methods for understanding and modelling clinical decision making through causality, inverse reinforcement learning and imitation learning.

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.

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 and a Fellow at The Alan Turing Institute in London.

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

From 2020 to 2022, Nick oversaw the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.