Periodic Reporting for period 1 - CAOS (Containment, Avalanches and Optimisation in Spreading-processes)
Reporting period: 2019-08-01 to 2021-07-31
The project “Containment, Avalanches and Optimisation in Spreading-processes” (CAOS) was originally aimed at studying the nature of spreading-processes and possible industrial applications. Starting from early 2020, the emerging COVID-19 pandemic due to the rapid spread of SARS-COV-2 virus has created serious challenges on public health and the global economy, as well as obstacles in the implementation of this action. Following the suggestion of the European Commission, we reoriented the main objectives of the project to address research problems related to COVID-19. Elucidating the characteristics of the spread of the SARS-COV-2 virus and resolving practical issues in the global effort to combat the COVID-19 pandemic are clearly highly important for society. Appropriate and timely measures can directly contribute to saving lives, while deeper and more thorough understanding of spreading processes can help with refining the mitigation strategies and providing evidence and support for fighting future epidemic spreads.
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 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.
We conducted work on this project by carrying out all five work packages (WPs). In WP1, we developed a stochastic model to study the epidemic spreading problem, addressing the impact of pre-symptomatic transmission on heterogeneous contact network structures which are relevant in COVID-19. We applied the advanced dynamic message-passing method for solving this model, which provides accurate and efficient algorithms. We also derived analytical expressions for epidemic thresholds and discussed their implications for different containment strategies, e.g. isolating individuals and reducing transmission through social distancing and face masks. In WP2, we proposed and validated group testing strategies to boost the testing capacity through collaboration with researchers in the SARS-COV-2 PCR testing labs at the University of Birmingham and colleagues at King’s College London, and provided a method to reduce the false negative rate of two-stage group testing. In WP3, we studied the mathematical model of failure spreading of support service networks due to epidemic spreading such as COVID-19. We developed algorithms to solve this model using dynamic message-passing and devised mitigation strategies using the optimal control framework. In WP4, we investigated decision-making strategies under uncertainty that is relevant at the early stage of a pandemic such as COVID-19. In WP5, we applied the developed mathematical techniques in neighboring fields in complex systems such as routing and machine learning. For training and knowledge transfer, the Fellow attended five training workshops organized by the Research and Knowledge Exchange Team of the host university. He actively engaged in various teaching and supervision activities, including delivering tutorials, marking assignments and supervising MSc students on research problems. The Fellow participated in grant management, proposal preparations and risk management through the guidance of the host supervisors. The Fellow provided referee services for scientific journals, including Journal of Statistical Mechanics: Theory and Experiment, Scientific Reports and IEEE Access.
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.
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.
The work in WP1 and WP3 provides state-of-the-art analytical methods to tackle epidemic spreading problems with pre-symptomatic transmission, characteristic of the COVID-19 pandemic, and epidemic-induced cascading failure in contact networks. Our theoretical methods yield much better accuracy compared to the usual mean field approach while they are much more efficient than numerical simulations. The analytical expression of epidemic thresholds provides insight into various containment and mitigation strategies, complementing the indicator of the commonly used basic reproduction number. We also derived an intricate optimization algorithm for mitigating epidemic-induced cascading failures. These findings enrich the knowledge and toolbox available to the scientific community for understanding and combating epidemic spreading, which could have a far-reaching benefit to society, considering that we have been suffering from a pandemic for over 21 months, leading to severe public-health crises worldwide and a significant socio-economic loss.
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.
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.