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Dr James Watson

Dr James Watson

Contact information


james.watson@ndm.ox.ac.uk

Podcast interview

Data driven definitions of severe malaria


James studies severe malaria in African children, focusing on improving diagnostic accuracy. By analysing clinical data, he aims to distinguish malaria-related severe illness from other infections and estimate true mortality more reliably. His work supports faster diagnosis and treatment, ultimately reducing preventable child deaths in low-resource settings.

James Watson

IDDO Associate Director

Clinical Therapeutics

I am a statistician interested in improving the treatment of infectious diseases of global health importance. My main work focuses on severe malaria, notably improving the definition of severe malaria in areas of high malaria transmission. We have estimated that a third of children diagnosed with severe malaria in high transmission settings in fact have another cause of severe illness.

I also work on other infectious diseases: I am an investigator and study statistician for PLATCOV, a platform trial characterising in vivo antiviral effects of repurposed or novel drugs for COVID-19; I am an investigator on the CHARM project which aims to develop a new methodology for the assessment of new antiparasitic drugs for the treatment of Chagas disease.

Probabilistic classification of recurrent illness in vivax

A chart showing a fully Bayesian framework that classifies recurrent vivax infection as a recrudescence, relapse or reinfection using time-to-event and genetic data. This analysis showed that high-dose primaquine is very effective (3% failure).
A fully Bayesian framework that classifies recurrent vivax infection as a recrudescence, relapse or reinfection using time-to-event and genetic data. This analysis showed that high-dose primaquine is very effective (3% failure).

Causal pathways in severe malaria

A directed acyclic graph that tries to dissect causal mechanisms leading to death in severe falciparum malaria. The most important part of the causal diagram is in specifying selection biases in severe malaria: "why are certain patients in the dataset and not others?"
A directed acyclic graph that tries to dissect causal mechanisms leading to death in severe falciparum malaria. The most important part of the causal diagram is in specifying selection biases in severe malaria: "why are certain patients in the dataset and not others?"

Recent publications