The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried o...The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried out on secondary case data,the expected distribution of which is known as the offspring distribution.However,this data is seldom available.Here we introduce a multi-model framework fit to incidence time-series,data that is much more readily available.The framework consists of five discrete-time,stochastic,branching-process models of epidemics spread through a susceptible population.The framework includes a baseline model of homogeneous transmission,a unimodal and a bimodal model for super-spreading events,as well as a unimodal and a bimodal model for super-spreading individuals.Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo methods.Model comparison is conducted by computing Bayes factors,with importance sampling used to estimate the marginal likelihood of each model.This estimator is selected for its consistency and lower variance compared to alternatives.Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters,such as the basic reproduction number.We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic caused by SARS-CoV-2.Model selection consistently identifies the same model and mechanism for a given disease,even when using different time series.Our estimates are consistent with previous studies based on secondary case data.Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control.Our modelling framework is disease-agnostic and implemented as an R package,with potential to be a valuable tool for public health.展开更多
SARS-CoV-2 has recently been found to have spread from humans to minks and then to have transmitted back to humans.However,it is unknown to what extent the human-to-human transmission caused by the variant has reached...SARS-CoV-2 has recently been found to have spread from humans to minks and then to have transmitted back to humans.However,it is unknown to what extent the human-to-human transmission caused by the variant has reached.Here,we used publicly available SARS-CoV-2 genomic sequences from both humans and minks collected in Denmark and the Netherlands,and combined phylogenetic analysis with Bayesian inference under an epidemiological model,to trace the possibility of person-to-person transmission.The results showed that at least 12.5%of all people being infected with dominated minkderived SARS-CoV-2 variants in Denmark and the Netherlands were caused by human-to-human transmission,indicating that this“backto-human”SARS-CoV-2 variant has already caused human-to-human transmission.Our study also indicated the need for monitoring this mink-derived and other animal source“back-to-human”SARS-CoV-2 in future and that prevention and control measures should be tailored to avoid large-scale community transmission caused by the virus jumping between animals and humans.展开更多
基金funding from the Engineering and Physical Sciences Research Council(EPSRC grant number EP/W006790/1)the Medical Research Council(MRC grant number MR/N010760/1)the National Institute for Health Research(NIHR grant number NIHR200892).
文摘The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried out on secondary case data,the expected distribution of which is known as the offspring distribution.However,this data is seldom available.Here we introduce a multi-model framework fit to incidence time-series,data that is much more readily available.The framework consists of five discrete-time,stochastic,branching-process models of epidemics spread through a susceptible population.The framework includes a baseline model of homogeneous transmission,a unimodal and a bimodal model for super-spreading events,as well as a unimodal and a bimodal model for super-spreading individuals.Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo methods.Model comparison is conducted by computing Bayes factors,with importance sampling used to estimate the marginal likelihood of each model.This estimator is selected for its consistency and lower variance compared to alternatives.Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters,such as the basic reproduction number.We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic caused by SARS-CoV-2.Model selection consistently identifies the same model and mechanism for a given disease,even when using different time series.Our estimates are consistent with previous studies based on secondary case data.Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control.Our modelling framework is disease-agnostic and implemented as an R package,with potential to be a valuable tool for public health.
基金This work was supported by the intramural special grant for SARSCoV-2 research from the Chinese Academy of Sciences,the Strategic Priority Research Programme of the Chinese Academy of Sciences(grant number XDB29010102)the National Natural Science Foundation of China(NSFC)(grant numbers 32041010 and 31900155)+1 种基金Y.B.is supported by the NSFC Outstanding Young Scholars(grant number 31822055)the Youth Innovation Promotion Association of CAS(grant number 2017122).
文摘SARS-CoV-2 has recently been found to have spread from humans to minks and then to have transmitted back to humans.However,it is unknown to what extent the human-to-human transmission caused by the variant has reached.Here,we used publicly available SARS-CoV-2 genomic sequences from both humans and minks collected in Denmark and the Netherlands,and combined phylogenetic analysis with Bayesian inference under an epidemiological model,to trace the possibility of person-to-person transmission.The results showed that at least 12.5%of all people being infected with dominated minkderived SARS-CoV-2 variants in Denmark and the Netherlands were caused by human-to-human transmission,indicating that this“backto-human”SARS-CoV-2 variant has already caused human-to-human transmission.Our study also indicated the need for monitoring this mink-derived and other animal source“back-to-human”SARS-CoV-2 in future and that prevention and control measures should be tailored to avoid large-scale community transmission caused by the virus jumping between animals and humans.
