Modeling the heterogeneity in COVID-19's reproductive number and its impact on predictive scenarios

Abstract

The correct evaluation of the reproductive number R for COVID-19 is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R is modeled as a constant -effectively averaging out the inherent variability of the transmission process due to varying individual contact rates, population densities, or temporal factors amongst many. Yet, due to the exponential nature of epidemic growth, the error due to this simplification can be rapidly amplified, and its extent remains unknown. How can this intrinsic variability be percolated into epidemic models, and its impact, better quantified? We study this question here through a Bayesian perspective that captures at scale the heterogeneity of a population and environmental conditions, creating a bridge between the traditional agent-based and compartmental approaches. We use our model to simulate the spread as well as the impact of different social distancing strategies on real COVID-19 data, and highlight the significant impact of the heterogeneity. We emphasize that the contribution of this paper focuses on discussing the importance of the impact of R's heterogeneity on uncertainty quantification from a statistical viewpoint, rather than developing new predictive models.

Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach

“…In order to model the uncertainty associated with the aerosol transmission model, we added a sampling step at the end of the Jimenez and Peng pipeline. This allowed us to account for individual variations in infectious participants’ ability to spread the disease and to remain consistent with the extensive literature on the heavy-tailed Pareto nature of COVID-19 transmission and superspreading [ 24 - 27 ]. For each infected participant, we sampled the number of quanta that they exhale using a Pareto distribution with shape θ = 1.16 and rate η = θ/(θ – 1) q exhalation .…”

“…This is because a singular focus on the expected outcome precludes consideration of the distribution of all possible outcomes, including worst-case scenarios. In the context of COVID-19, where the majority of new cases has been shown to be caused by a minority of index cases [ 24 - 26 ], the modelling of tail events and potential super-spreader phenomena takes on significant importance for risk assessment [ 26 , 27 ].…”

Scenarios for the Development of Polish Passenger Transport Services in Pandemic Conditions

“…Many tools and methods were used by researchers in these scenarios, including predictions based on the Bayesian model [52,53], SIR models [54,55], an agent-based model and a deterministic compartmental model [56], fractional models [57], and a modified SEIR compartmental model [58].…”

A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling

“…In fact, the constraint R 0 ≤ 1 is known to be the condition for the almost sure extinction of the epidemic. Note however that R 0 is observed to be very variable in practice, especially for COVID-19 (see the discussions of Elliott and Gouriéroux 2020;Donnat and Holmes 2021).…”