Intrinsic Heterogeneities & the Effect of Control on Pathogen Superspreading Dynamics

Abstract

Background & aims of study: Superspreading is a normal feature of many infectious diseases where a small number of primary infectors contribute to most secondary infections. For increasingly heterogenous outbreaks, targeted control measures are more effective than population-wide measures, and vice versa. Accurately characterising overall transmission heterogeneity is crucial to predict the frequency of future superspreading events and for effective outbreak control. However, the intrinsic mechanisms of superspreading and interactions with real-world control are still poorly understood. This study aims to improve the quantitative measurement of intrinsic pathogen heterogeneity by furthering epidemic theory using both empirical and simulated datasets. 

Methods & results: We first replicated synthetic epidemic data assuming either homogeneous or heterogeneous transmission using a negative binomial branching process model and varying overdispersion (k) incrementally. Control resulted in greater estimates of heterogeneity than expected, especially for intrinsically homogenous outbreaks. This shift appeared primarily from the accumulation of new chain-terminating cases rather than the presence of uncontrolled cases with greater-than-expected individual variance who continued to infect others as previously thought. Recovery of the expected k from synthetic data was improved by using a zero-inflated negative binomial model (ZINB) additionally parameterised to account for the presence of structural zeros (ϕ) resulting from control. Reanalysis of empirical contact tracing data from historic coronavirus outbreaks in  Hong Kong using the ZINB distribution reduced estimates of overdispersion from k = 0.30 to 1.00 for COVID-19, and k = 0.06 to 0.13 for SARS.

Implications: Outbreak control effects, such as non-pharmaceutical interventions, individual self-isolation, or sterilising immunity, can increase apparent measures of overall heterogeneity by shifting the realised offspring distribution towards zero, even when the unconstrained pattern of spread is intrinsically homogeneous, leading to an overestimation of superspreading potential. Improved accuracy of estimation can be achieved by using zero-inflated variations of the commonly used negative binomial model. Intrinsic heterogeneity for both COVID-19 and SARS may be less than previously estimated but both are still relatively overdispersed. Our results have implications for designing ongoing intervention strategies that correctly balance individual-specific and population-wide measures of both homogeneous and heterogenous epidemics.

Note: This conference abstract was presented at the 9th International Conference on Infectious Disease Dynamics organized by the journal Epidemics. This abstract has not been screened by SSRN for potential for public harm and should not be used to inform any clinical decision making. No competing interests or funding statements have been declared.