Literature DB >> 33402022

On the relationship between serial interval, infectiousness profile and generation time.

Sonja Lehtinen1, Peter Ashcroft1, Sebastian Bonhoeffer1.   

Abstract

The timing of transmission plays a key role in the dynamics and controllability of an epidemic. However, observing generation times-the time interval between the infection of an infector and an infectee in a transmission pair-requires data on infection times, which are generally unknown. The timing of symptom onset is more easily observed; generation times are therefore often estimated based on serial intervals-the time interval between symptom onset of an infector and an infectee. This estimation follows one of two approaches: (i) approximating the generation time distribution by the serial interval distribution or (ii) deriving the generation time distribution from the serial interval and incubation period-the time interval between infection and symptom onset in a single individual-distributions. These two approaches make different-and not always explicitly stated-assumptions about the relationship between infectiousness and symptoms, resulting in different generation time distributions with the same mean but unequal variances. Here, we clarify the assumptions that each approach makes and show that neither set of assumptions is plausible for most pathogens. However, the variances of the generation time distribution derived under each assumption can reasonably be considered as upper (approximation with serial interval) and lower (derivation from serial interval) bounds. Thus, we suggest a pragmatic solution is to use both approaches and treat these as edge cases in downstream analysis. We discuss the impact of the variance of the generation time distribution on the controllability of an epidemic through strategies based on contact tracing, and we show that underestimating this variance is likely to overestimate controllability.

Entities:  

Keywords:  SARS-CoV-2; contact tracing; epidemiology; generation time; infectiousness; modelling

Mesh:

Year:  2021        PMID: 33402022     DOI: 10.1098/rsif.2020.0756

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  14 in total

1.  Assessing the impact of lateral flow testing strategies on within-school SARS-CoV-2 transmission and absences: A modelling study.

Authors:  Trystan Leng; Edward M Hill; Robin N Thompson; Michael J Tildesley; Matt J Keeling; Louise Dyson
Journal:  PLoS Comput Biol       Date:  2022-05-27       Impact factor: 4.779

2.  Inference of the SARS-CoV-2 generation time using UK household data.

Authors:  Sebastian Funk; Robin N Thompson; William S Hart; Sam Abbott; Akira Endo; Joel Hellewell; Elizabeth Miller; Nick Andrews; Philip K Maini
Journal:  Elife       Date:  2022-02-09       Impact factor: 8.713

3.  Caveats on COVID-19 herd immunity threshold: the Spain case.

Authors:  David García-García; Enrique Morales; Eva S Fonfría; Isabel Vigo; Cesar Bordehore
Journal:  Sci Rep       Date:  2022-01-12       Impact factor: 4.379

4.  On the use of aggregated human mobility data to estimate the reproduction number.

Authors:  Fabio Vanni; David Lambert; Luigi Palatella; Paolo Grigolini
Journal:  Sci Rep       Date:  2021-12-02       Impact factor: 4.379

5.  Characterizing the effective reproduction number during the COVID-19 pandemic: Insights from Qatar's experience.

Authors:  Raghid Bsat; Hiam Chemaitelly; Peter Coyle; Patrick Tang; Mohammad R Hasan; Zaina Al Kanaani; Einas Al Kuwari; Adeel A Butt; Andrew Jeremijenko; Anvar Hassan Kaleeckal; Ali Nizar Latif; Riyazuddin Mohammad Shaik; Gheyath K Nasrallah; Fatiha M Benslimane; Hebah A Al Khatib; Hadi M Yassine; Mohamed G Al Kuwari; Hamad Eid Al Romaihi; Mohamed H Al-Thani; Abdullatif Al Khal; Roberto Bertollini; Laith J Abu-Raddad; Houssein H Ayoub
Journal:  J Glob Health       Date:  2022-02-05       Impact factor: 4.413

6.  Test-trace-isolate-quarantine (TTIQ) intervention strategies after symptomatic COVID-19 case identification.

Authors:  Peter Ashcroft; Sonja Lehtinen; Sebastian Bonhoeffer
Journal:  PLoS One       Date:  2022-02-11       Impact factor: 3.240

7.  Inferring the reproduction number using the renewal equation in heterogeneous epidemics.

Authors:  William Green; Neil Ferguson; Anne Cori
Journal:  J R Soc Interface       Date:  2022-03-30       Impact factor: 4.118

8.  Estimating the generation interval from the incidence rate, the optimal quarantine duration and the efficiency of fast switching periodic protocols for COVID-19.

Authors:  Giuseppe Petrillo; Lucilla de Arcangelis; Eugenio Lippiello
Journal:  Sci Rep       Date:  2022-03-17       Impact factor: 4.379

9.  Generation time of the alpha and delta SARS-CoV-2 variants: an epidemiological analysis.

Authors:  William S Hart; Elizabeth Miller; Nick J Andrews; Pauline Waight; Philip K Maini; Sebastian Funk; Robin N Thompson
Journal:  Lancet Infect Dis       Date:  2022-02-14       Impact factor: 71.421

10.  A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic.

Authors:  Bernard Cazelles; Clara Champagne; Benjamin Nguyen-Van-Yen; Catherine Comiskey; Elisabeta Vergu; Benjamin Roche
Journal:  PLoS Comput Biol       Date:  2021-07-26       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.