Monitoring real-time transmission heterogeneity from incidence data

Abstract

The transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duration. Methods have been proposed to identify significant transmission heterogeneity in historic epidemics based on several data sources, such as contact history, viral genomes and spatial information, which may not be available, and more importantly ignore the temporal trend of transmission heterogeneity. Here we attempted to establish a convenient method to estimate real-time heterogeneity over an epidemic. Within the branching process framework, we introduced an instant-individualheterogenous infectiousness model to jointly characterize the variation in infectiousness both between individuals and among different times. With this model, we could simultaneously estimate the transmission heterogeneity and the reproduction number from incidence time series. We validated the model with data of both simulated and real outbreaks. Our estimates of the overall and real-time heterogeneities of the six epidemics were consistent with those presented in the literature. Additionally, our model is robust to the ubiquitous bias of under-reporting and misspecification of serial interval. By analyzing recent data from South Africa, we found evidence that the Omicron might be of more significant transmission heterogeneity than Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity.

Supercritical and homogenous transmission of monkeypox in the capital of China

“…We used the individual‐instant heterogeneity model 11 to analyze the incidence data and estimated R e and k simultaneously. Denoted the daily incidence data at time t as I t , and the epidemic curve till time t as $${\mathop{I}\limits^{-}}_{1}^{t}=\{{I}_{1},{I}_{2},{\rm{\cdots }},{I}_{t}\}$$.…”

“…We used the individual-instant heterogeneity model 11 to analyze the incidence data and estimated R e and k simultaneously. Denoted the daily incidence data at time t as I t , and the epidemic curve till time t as…”

“…As in Zhang et al, 11 we used a Bayesian framework with non‐informative priors (i.e., uniform prior distributions over [10 −6 −100] and [0.1−10], respectively) for $${R}_{e}$$ and k and estimated their posterior distributions given the daily Mpox incidence data from May 31 to June 21, 2023 in Beijing. For each posterior estimate, both the maximum a posteriori estimate and the 95% highest posterior density (HPD) interval were generated.…”

“…The current study was to analyze the transmission characteristics based on an epidemiological report of confirmed daily Mpox cases from May 31 to June 21, 2023 in Beijing 4 . Specifically, we employed the instant‐individual heterogeneity transmission model 11 to analyze the daily incidence to reveal R e and k of Beijing Mpox epidemic in the first 3 weeks. As the daily incidence thereafter was not available from July to November, we simulated the subsequent infection size in Beijing to project subsequent transmission dynamics.…”

“…However, R e is an average estimate of per-capita transmissibility, which may mask variation in individual transmissibility (or transmission heterogeneity, denoted as k). A high level of heterogeneity (i.e., k < 1) may not only bias our estimate/interpretation of R e , 10,11 but also reduce the efficiency of control measures by invoking a superspreading event (SSE), that is, one individual infects a larger number of secondary cases than the average. 12 This was reported in the 1980−84 Mpox epidemic in West Africa.…”