Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

Schneckenreither, Günter; Herrmann, Lukas; Reisenhofer, Rafael; Popper, Niki

doi:10.1101/2022.02.21.22271241

Cited by 4 publications

(8 citation statements)

References 47 publications

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“…Another common method of allowing for transmission heterogeneity is an instant-level heterogeneity model [ 22 , 25 ]. This model extended the standard model ( 1 ) by replacing the instantaneous reproduction number R t with an instant-related random variable η t for all the infected cases, that is, where Γ(⋅, ⋅) stands for Gamma distribution in the shape-rate parameterizations.…”

Section: Methodsmentioning

confidence: 99%

“…Several tools for the estimating of real-time reproduction number based on incidence data had been developed with successful applications [19][20][21], but the study on realtime transmission heterogeneity is so far rather limited. In some recent studies, researchers suggested the relationship between the transmission heterogeneity and the incidence over an epidemic [22][23][24][25], but none have attempted to accurately delineate the heterogeneity with incidence data and to compare with those records in literatures. In this study, we attempted to develop a simple method to estimate the transmission heterogeneity on the basis of incidence data.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring real-time transmission heterogeneity from incidence data

2022

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring real-time transmission heterogeneity from incidence data

2022

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“…Another common method of allowing for transmission heterogeneity is an instant-level heterogeneity model [22, 25]. This model extended the standard model (1) by replacing the instantaneous reproduction number R t with an instant-related random variable for all the infected cases, that is, where Γ(·, ·) stands for Gamma distribution in the shap e-rate parameterizations.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the composite rate under this model is . And the incidence I t is Negative Binomial distribution as (NegB indicating Negative Binomial distribution ): This model accounted for the variation in infectiousness at different times, which could be useful in epidemic forecasting in the long term [22, 25]. But this model overlooked the variation in infectiousness of different infectious individuals, and hence failed to identify the exact degree of heterogeneity from incidence data (showed in Results).…”

Section: Methodsmentioning

confidence: 99%

“…Several tools for the estimating of real-time reproduction number based on incidence data had been developed with successful applications [19][20][21], but the study on real-time transmission heterogeneity is so far rather limited. In some recent studies, researchers suggested the relationship between the transmission heterogeneity and the incidence over an epidemic [22][23][24][25], but none have attempted to accurately delineate the heterogeneity with incidence data and to compare with those records in literatures. In this study, we attempted to develop a simple method to estimate the transmission heterogeneity on the basis of incidence data.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring real-time transmission heterogeneity from Incidence data

Zhang

Britton

Zhou

2022

Preprint

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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 is sophisticated and 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 characterized 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 both simulated data and five historic epidemics. Our estimates of the overall and real-time heterogeneities of the five 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 the recent data from South Africa, we found evidences that the Omicron might be of more significant transmission heterogeneity than the Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity.

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Characterizing spatiotemporal variation in transmission heterogeneity during the 2022 mpox outbreak in the USA

Love,

LaPrete,

Sheets

et al. 2023

Preprint

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Transmission heterogeneity plays a critical role in the dynamics of an epidemic. During an outbreak of an emerging infectious disease, efforts to characterize transmission heterogeneity are generally limited to quantifications during a small outbreak or a limited number of generations of a larger outbreak. Understanding how transmission heterogeneity itself varies over the course of a large enduring outbreak not only improves understanding of observed disease dynamics but also informs public health strategy and response. In this study, we employ a method, adaptable to other emerging infectious disease outbreaks, to quantify spatiotemporal variation in transmission heterogeneity for the 2022 mpox outbreak in the United States. Based on past research on mpox and following reports of potential superspreading events early in this outbreak, we expected to find high transmission heterogeneity as quantified by the dispersion parameter of the offspring distribution,k. Our methods use maximum likelihood estimation to fit a negative binomial distribution to transmission chain offspring distributions informed by a large mpox contact tracing dataset. We find that, while estimates of transmission heterogeneity varied across the outbreak with spatiotemporal pockets of higher heterogeneity, overall transmission heterogeneity was low. When testing our methods on simulated data from an outbreak with high transmission heterogeneity,kestimate accuracy depended on the contact tracing data completeness. Because the actual contact tracing data had high incompleteness, our values ofkestimated from the empirical data may be artificially high. However, it is also possible that our estimates accurately reflect low transmission heterogeneity for the United States mpox outbreak.

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Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

Cited by 4 publications

References 47 publications

Monitoring real-time transmission heterogeneity from incidence data

Monitoring real-time transmission heterogeneity from incidence data

Monitoring real-time transmission heterogeneity from Incidence data

Characterizing spatiotemporal variation in transmission heterogeneity during the 2022 mpox outbreak in the USA

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