Co-authored by Principal Investigators at PSI, Professor Moritz Kraemer and Professor Oliver Pybus, a new paper in Proceedings of the National Academy of Sciences may help optimise testing strategies for infectious disease surveillance.
Alongside PSI authors, the study included researchers from the University of Oxford’s Biology and Computer Science departments, as well as colleagues from the Oxford Martin Programme on Pandemic Genomics, Imperial College London, Royal Veterinary College and University of California, Los Angeles.
When epidemics and pandemics occur, screening the population for infection is essential to understand how disease is spreading.
Testing resources, however, are always finite, and questions on how to allocate tests to maximise the information gained about disease distributions remain difficult.
The study proposes a novel machine learning strategy (“policy”), Selection by Local-Entropy (LE), to guide the selection of testing sites. When tested in a range of simulated outbreak scenarios, LE mostly outperformed other testing policies considered by the authors.
Professor Kraemer said: ‘Data and robust understanding of the transmission process early in epidemics are essential for effective public health policies. Our study provides a step towards more rational implementation of public health policies.’
The framework created by this study will allow researchers and policymakers to more adaptively design surveillance systems for infection disease.
Read the full paper on the Proceedings of the National Academy of Sciences website: https://doi.org/10.1073/pnas.2412424121