A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study

Hart, William S; Maini, Philip K.; Yates, Christian A.; Thompson, Robin N

doi:10.1098/rsif.2020.0230

Cited by 29 publications

(35 citation statements)

References 70 publications

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“…Conversely, viral load data suggest a longer duration of viral shedding due to infection with B1.1.7 compared to the original variant of SARS-CoV-2 [45]. If higher viral loads lead to increased infectiousness [46][47][48][49][50], this may suggest a longer-tailed generation time distribution for the B1.1.7 variant.…”

Section: Discussionmentioning

confidence: 99%

Inference of SARS-CoV-2 generation times using UK household data

Hart

Abbott

Endo

et al. 2021

Preprint

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The distribution of the generation time (the interval between individuals becoming infected and passing on the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to SARS-CoV-2 data from January-March 2020 compared to existing models. However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March-November 2020 in the UK, we provide updated estimates of the generation time. We consider both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% CrI 3.3-5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0-8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (6.0 days, 5.2-7.0 days) and a similar standard deviation (4.9 days, 4.0-6.3 days). Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, continued data collection and analysis is necessary to inform future public health policy decisions.

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Section: Discussionmentioning

confidence: 99%

Inference of SARS-CoV-2 generation times using UK household data

Hart

Abbott

Endo

et al. 2021

Preprint

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“…Here, we develop a mechanistic approach for inferring key epidemiological time periods using data from infector–infectee pairs ( Figure 1B , right). This approach was motivated by compartmental epidemic models with Gamma distributed stage durations ( Lloyd, 2009 ; Wearing et al, 2005 ) and changes in infectiousness during infection ( Hethcote et al, 1991 ; Christofferson et al, 2014 ; Hart et al, 2019 ; Hart et al, 2020 ; Gatto et al, 2020 ; Aleta et al, 2020 ). Our method provides an improved fit to data from SARS-CoV-2 transmission pairs compared to previous approaches, namely, (1) a model assuming that transmission and symptoms are independent ( Ferretti et al, 2020a ; Deng et al, 2020 ; Ganyani et al, 2020 ; Knight and Mishra, 2020 ) and (2) a previous statistical method in which this assumption is relaxed ( Ferretti et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing

Hart

Maini

Thompson

2021

eLife

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Background:Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing.Methods:Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector–infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches.Results:The mechanistic method provides an improved fit to data from SARS-CoV-2 infector–infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms.Conclusions:High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced.Funding:Engineering and Physical Sciences Research Council (EPSRC).

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“…Although our approach could be extended for different types of models (such as network models), compartmental models (such as the SIS and SIR models) are commonly used for assessing outbreak risks. Accurate outbreak forecasting using a compartmental model requires the model to be carefully matched to the epidemiology of the host–pathogen system, potentially including within-host dynamics [ 97 , 98 ], asymptomatic transmission [ 9 , 99 , 100 ] or spread between spatially distinct regions [ 29 , 101 ]. For certain definitions of a severe epidemic, it may be necessary to include bed-ridden or convalescent hosts in the model explicitly.…”

Section: Discussionmentioning

confidence: 99%

Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic

Thompson

Gilligan

Cunniffe

2020

J. R. Soc. Interface.

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Forecasting whether or not initial reports of disease will be followed by a severe epidemic is an important component of disease management. Standard epidemic risk estimates involve assuming that infections occur according to a branching process and correspond to the probability that the outbreak persists beyond the initial stochastic phase. However, an alternative assessment is to predict whether or not initial cases will lead to a severe epidemic in which available control resources are exceeded. We show how this risk can be estimated by considering three practically relevant potential definitions of a severe epidemic; namely, an outbreak in which: (i) a large number of hosts are infected simultaneously; (ii) a large total number of infections occur; and (iii) the pathogen remains in the population for a long period. We show that the probability of a severe epidemic under these definitions often coincides with the standard branching process estimate for the major epidemic probability. However, these practically relevant risk assessments can also be different from the major epidemic probability, as well as from each other. This holds in different epidemiological systems, highlighting that careful consideration of how to classify a severe epidemic is vital for accurate epidemic risk quantification.

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A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study

Cited by 29 publications

References 70 publications

Inference of SARS-CoV-2 generation times using UK household data

Inference of SARS-CoV-2 generation times using UK household data

High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing

Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic

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