Contextual Contact Tracing based Spatio Enhanced Compartment Modelling & Spatial Risk Assessment

Abstract

The current situation of COVID-19 appears as a paradigm shift that seems to have farreaching impacts on the way humans will now continue with their daily routine. The overall scenario highlights the paramount importance of infectious disease surveillance, which necessitates immediate monitoring for effective preparedness and efficient response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. This thesis research investigates both requirements in a hybrid approach by the implementation of disease outbreak modelling and exploring its induced dynamic spatial risk in the form of Risk Assessment, along with its real-time integration back into the disease model. The study implements human mobility based contact tracing in the form of an event-based stochastic SIR model as a baseline and further modifies the existing setup to be inclusive of the spatial risk. This modification of each individual-level contact’s intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results suggest that the Spatio-SIR model tends to perform more meaningful events concerned with the Susceptible population rather than events to the Infected or Quarantined. With an example of a real-world scenario of induced spatial high-risk, it is highlighted that the new Spatio-SIR model can empower the analyst with a capability to explore disease dynamics from an additional perspective. The study concludes that even if this domain is hindered due to lack of data availability, the investigation process related to it should keep on exploring methods to effectively understand the disease dynamics.