Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment

Abstract

The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective 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. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include 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 highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation.

Modeling infectious disease dynamics: Integrating contact tracing-based stochastic compartment and spatio-temporal risk models

“…These authors work with an event-based stochastic approach (Gillespie’s Method) ( Gillespie, 1977 ) for the SIR model, originally presented by Kermack and McKendrick (1927) , in which they also incorporate the idea of contact tracing. An exhaustive description of this approach is presented by Mahmood et al (2021) .…”

“…Within this framework, and as described by Mahmood et al (2021) , the average degree , computed for some time period , is given by where represents the count of contacts of a person with other individuals in . However, if we want to have a measure of only infectious contacts, we can define the infectious contacts for some time-window as such that is an indicator function for the event in which the individual can infect others.…”

“…Such a situation requires extraordinary attention of stakeholders, especially with the surface and aerosol stability of infectious diseases ( Van Doremalen et al, 2020 , Simmerman et al, 2010 ). Preparing for an extreme scenario like this requires proactive measures in terms of having a readily available solution (e.g., accounting for spatial information) that can be used as required ( Mahmood et al, 2021 ). At the same time, when the entire humanity is at stake, the requirement of saving lives becomes more critical than ensuring privacy, and the use of these continuous recordings of individual-level mobility is acceptable ( Benreguia et al, 2020 ).…”

“…One such novel tool for Contextual Contact Tracing (CCT) was developed by Mahmood et al (2021) . CCT focuses on the consideration of spatial risk of a contact’s geographical location to influence the overall outcome of a contact; the idea being that some areas are more disease inducing than others.…”

“…CCT focuses on the consideration of spatial risk of a contact’s geographical location to influence the overall outcome of a contact; the idea being that some areas are more disease inducing than others. Mahmood et al (2021) implemented a SIR-based compartment model ( Kermack and McKendrick, 1927 ) with two additional compartments related to quarantine ( Hernández-Orallo et al, 2020 ). The work was concerned with the tracking of infectious trajectories (pedestrian mobility) and the investigation of the stakeholders’ requirements of identification of (i) high-risk areas, and (ii) individuals to be quarantined.…”