Periodic Reporting for period 1 - CAOS (Containment, Avalanches and Optimisation in Spreading-processes)
Reporting period: 2019-08-01 to 2021-07-31
The overall objectives of this project are to develop mathematical models and algorithms to gain theoretical insights into epidemic spreading related issues, which include (i) developing principled probabilistic analysis of epidemic spreading to better understand the disease spread of COVID-19; (ii) designing group testing strategy for boosting test capacity for COVID-19; (iii) investigating the spread of failure in support service networks due to epidemic spreading; (iv) investigating decision-making strategies under uncertainty; (v) applying the developed mathematical techniques to neighboring fields in complex systems. A simultaneous objective of this project is to provide various skills training to the Fellow and foster his career development. The project has achieved most of its objectives for the period.
The academic results in this project were mainly disseminated through publications in scientific journals, which include (1) one published paper about epidemic-spreading with pre-symptomatic transmission; (2) one forthcoming paper about failure spreading of support service networks due to epidemic spreading; (3) one forthcoming paper about decision-making strategies under uncertainty; (4) three published papers and one forthcoming paper in other complex systems. Before the COVID-19 pandemic, we were invited to three conferences to present our work. After the onset of the pandemic, we attended and presented in online conferences and used other online platforms (such as researchgate) to communicate our work to other researchers. We also tried to lobby the government of the United Kingdom to adopt group testing strategy to boost test capacity during the first wave of the COVID-19 pandemic.
In WP2, through collaborations with researchers in the University of Birmingham testing lab and King’s College London, we proposed group testing methods and validated their efficacy in testing for COVID-19. These methods can save a significant amount of testing resources. We further contacted the Diagnostics Innovation Team for the COVID-19 Testing Programme in the UK and suggested using group testing methods to increase significantly the testing capacity. Unfortunately, such methods were not adopted eventually for widely use (in the UK), due to policy and regulatory decisions. Nevertheless, our work has raised the government’s awareness of novel testing methods, which could be useful in the future or in the next public health crisis. Similar methods have been suggested in parallel by other groups and have been put into use (e.g. in Israel).
In WP4 and WP5, we advanced the frontiers of sequential decision-making strategies relevant to COVID-19 and other complex systems (such as traffic routing and machine learning) through theoretical studies of the corresponding mathematical models, as well as deriving optimization algorithms for solving related hard computational problems. These studies cultivate knowledge transfer across disciplines and facilitate the application of methods from one discipline to another. They can potentially benefit society when the developed algorithms will be adopted to solve practical problems such as traffic congestion mitigation.