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Estimating effective reproduction number using generation time versus serial interval,with application to COVID-19 in the Greater Toronto Area,Canada

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Infectious Disease Modelling 2020年第1期5卷 889-896页

作者： Jesse Knight sharmistha mishra Institute of Medical Science University of TorontoCanada MAP Centre for Urban Health Solutions Unity Health TorontoCanada Division of Infectious Disease Department of MedicineUniversity of TorontoCanada Institute of Health Policy Management and EvaluationDalla Lana School of Public HealthUniversity of TorontoCanada

*** effective reproduction number Re(t)is a critical measure of epidemic ***(t)can be calculated in near real time using an incidence time series and the generation time distribution:the time between infection events in an infector-infectee *** calculating Re(t),the generation time distribution is often approximated by the serial interval distribution:the time between symptom onset in an infector-infectee ***,while generation time must be positive by definition,serial interval can be negative if transmission can occur before symptoms,such as in COVID-19,rendering such an approximation improper in some *** developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period *** then compared estimates of Re(t)for COVID-19 in the Greater Toronto Area of Canada using:negative-permitting versus non-negative serial interval distributions,versus the inferred generation time *** estimated the generation time of COVID-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 *** to the generation time distribution,non-negative serial interval distribution caused overestimation of Re(t)due to larger mean,while negative-permitting serial interval distribution caused underestimation of Re(t)due to larger *** of the generation time distribution of COVID-19 with non-negative or negative-permitting serial interval distributions when calculating Re(t)may result in over or underestimation of transmission potential,respectively.

关键词： Reproduction number Generation time Serial interval Incubation period COVID-19

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What could re-infection tell us about R0? A modeling casestudy of syphilis transmission

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Infectious Disease Modelling 2019年第1期4卷 257-264页

作者： Joshua Feldman sharmistha mishra Centre for Urban Health Solutions St.Michael's HospitalUniversity of TorontoCanada Department of Medicine Division of Infectious DiseaseUniversity of TorontoCanada Institute of Health Policy Management and EvaluationDalla Lana School of Public HealthUniversity of TorontoCanada Institute of Medical Sciences University of TorontoCanada

Many infectious diseases can lead to *** examined the relationship between the prevalence of repeat infection and the basic reproductive number(R0).First we solved a generic,deterministic compartmental model of re-infection to derive an analytic solution for the *** then numerically solved a disease-specific model of syphilis transmission that explicitly tracked *** derived a generic expression that reflects a non-linear and monotonically increasing relationship between proportion reinfection and R0 and which is attenuated by entry/exit rates and recovery(***).Numerical simulations from the syphilis model aligned with the analytic ***-infection proportions could be used to understand how far regions are from epidemic control,and should be included as a routine indicator in infectious disease surveillance.

关键词： Re-infection Basic reproductive number Compartmental model Syphilis Dynamical system

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Contribution of high risk groups’unmet needs may be underestimated in epidemic models without risk turnover:A mechanistic modelling analysis

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Infectious Disease Modelling 2020年第1期5卷 549-562页

作者： Jesse Knight Stefan D.Baral Sheree Schwartz Linwei Wang Huiting Ma Katherine Young Harry Hausler sharmistha mishra MAP Centre for Urban Health Solutions Unity Health TorontoCanada Deptartment of Epidemiology Johns Hopkins Bloomberg School of Public HealthUSA TB HIV Care South Africa Division of Infectious Disease Department of MedicineUniversity of TorontoCanada Institute of Health Policy Management and EvaluationDalla Lana School of Public HealthUniversity of TorontoCanada Institute of Medical Sciences University of TorontoCanada

Background:Epidemic models of sexually transmitted infections(STIs)are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction(tPAF)of unmet prevention and treatment needs within risk ***,evidence suggests that STI risk is dynamic over an individual’s sexual life course,which manifests as turnover between risk *** sought to examine the mechanisms by which turnover influences modelled projections of the tPAF of high risk ***:We developed a unifying,data-guided framework to simulate risk group turnover in deterministic,compartmental transmission *** applied the framework to an illustrative model of an STI and examined the mechanisms by which risk group turnover influenced equilibrium prevalence across risk *** then fit a model with and without turnover to the same risk-stratified STI prevalence targets and compared the inferred level of risk heterogeneity and tPAF of the highest risk group projected by the two ***:The influence of turnover on group-specific prevalence was mediated by three main phenomena:movement of previously high risk individuals with the infection into lower risk groups;changes to herd effect in the highest risk group;and changes in the number of partnerships where transmission can *** turnover led to a smaller ratio of STI prevalence between the highest and lowest risk *** to the fitted model without turnover,the fitted model with turnover inferred greater risk heterogeneity and consistently projected a larger tPAF of the highest risk group over ***:If turnover is not captured in epidemic models,the projected contribution of high risk groups,and thus,the potential impact of prioritizing interventions to address their needs,could be *** aid the next generation of tPAF models,data collection efforts to parameterize risk group turnover should be prioritized.

关键词： Mathematical modelling Transmission Risk heterogeneity Turnover Sexually transmitted infection Population attributable fraction

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Corrigendum to“Estimating effective reproduction number using generation time versus serial interval,with application to covid-19 in the Greater Toronto Area,Canada”[Infectious Disease Modelling 5(2020)889e896]

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Infectious Disease Modelling 2022年第4期7卷 777-777页

In the originally published version of this work,the parametersθ^(*)=[a,b]were calculated incorrectly because the Kullback-Leibler divergence was defined in the wrong direction in our *** impact on generation time parameters and statistics is as follows(original/fixed):shape(a):1.813/1.633,scale(b):2.199/2.498,mean:3.99/4.08,SD:2.96/*** qualitative interpretation of results is *** sincerely apologize for this *** error correction is shown here:https://***/mishra-lab/covid-r/commit/aaef512 and a corrected draft manuscript is shown here.

关键词： unchanged qualitative correction

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