Proximity-Sensitive Awareness in Epidemic Modelling

In global epidemics, such as the SARS-CoV-2 epidemic, people often receive information about proximity of the new infectious cases either through government reporting or through word of mouth. However, the impact of this proximity information on the dynamics of the preventive behaviour (Behavioural Impact, shortly, BI), and consequently on the epidemic propagation (Epidemiological Impact, shortly, EI) is not understood. Previous research aims to understand the EI by proposing simple mathematical equations for the BI (we call these awareness models), and most of these works assume that individuals only have access to global infection counts, and no proximity information. The ongoing SARS-CoV-2 epidemic acts as a natural laboratory, enabling us observe the BI and the EI of proximity-sensitive awareness (PSA) in real datasets, and motivating the mathematical analysis of the EI of new awareness models. Objectives and methods: I propose to carry out a complete methodological cycle from innovative data collection methods to rigorous mathematical proofs and the evaluation of practical implications for improving current epidemic containment methods:1. Observe the BI and the EI of PSA in psychological and epidemiological datasets.2. Implement PSA into simple network-based (SIR and SIS) epidemic models based on the results of Objective 1, and observe new phenomena about the EI of PSAs from a statistical physics perspective by computer simulations. Special focus will be given to understanding the early growth of the number of infections and the density the infection in the endemic state, which are crucial properties of real-world epidemics.3. Rigorously prove the most important discoveries made in Objective 2 by extending the related mathematical work on branching processes.4. Explore the practical implications to improve current epidemic containment methods based on the discoveries of Objectives 1-3.Outcome: A successful completion of these objectives will advance the theory of awareness modelling in a multidisciplinary way.