Estimating reproduction numbers for adults and children from case data

Glass, Kathryn; Mercer, G. N.; Nishiura, Hiroshi; McBryde, Emma S.; Becker, Niels G.

doi:10.1098/rsif.2010.0679

Cited by 30 publications

(42 citation statements)

References 30 publications

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“…Note that both matrix forms are typically asymmetric. In prior work, we identified that asymmetric matrices are generally preferable when age groups are of unequal size, 35 which is the situation for most studies considered here.…”

Section: Contact Matricesmentioning

confidence: 95%

“…34 Finally, differences in infectivity or susceptibility between population groups can lead to differences in estimates of the reproduction number if the disease is largely confined to one group. 35 For example, estimates of the reproduction number from early Japanese data (with 84% child cases) 16 were considerably higher than estimates over an extended period of time with 67% child cases, 10 suggesting a higher reproduction number in children.…”

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confidence: 93%

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Pandemic Influenza H1N1

2012

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Section: Contact Matricesmentioning

confidence: 95%

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confidence: 93%

Pandemic Influenza H1N1

2012

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“…This method has been applied to estimate reproduction numbers during epidemics of diseases including Ebola virus disease (Althaus, 2015;Kelly et al, 2018), Middle-East Respiratory Syndrome (MERS) and porcine reproductive and respiratory syndrome (Arruda et al, 2017). It has also been extended to permit inference in different settings including in populations consisting of multiple host types (Glass et al, 2011), as well as to allow estimates to be informed by other types of data (Jombart et al, 2014;Campbell et al, 2019). Because of the importance of tracking temporal changes in epidemiological parameters, software implementing the framework of Wallinga and Teunis (2004) was developed to allow such analyses to be performed (Obadia et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

et al. 2019

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A B S T R A C TAccurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) upto-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

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“…The importance of the dynamics of influenza transmission between age groups is well-appreciated [1][2][3][4][5][6]. Several studies have assessed the non-uniformity of the impact of influenza, particularly pandemic influenza, on different age groups [1][2][3][4][7][8][9]. The overarching interest in these studies is to gather information in order to influence policy to best determine a strategy to impact on the spread of outbreaks.…”

Section: Introductionmentioning

confidence: 99%

Determining the dynamics of influenza transmission by age

White

Archer

Pagano

2014

Emerg Themes Epidemiol

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BackgroundIt is widely accepted that influenza transmission dynamics vary by age; however methods to quantify the reproductive number by age group are limited. We introduce a simple method to estimate the reproductive number by modifying the method originally proposed by Wallinga and Teunis and using existing information on contact patterns between age groups. We additionally perform a sensitivity analysis to determine the potential impact of differential healthcare seeking patterns by age. We illustrate this method using data from the 2009 H1N1 Influenza pandemic in Gauteng Province, South Africa.ResultsOur results are consistent with others in showing decreased transmission with age. We show that results can change markedly when we make the account for differential healthcare seeking behaviors by age.ConclusionsWe show substantial heterogeneity in transmission by age group during the Influenza A H1N1 pandemic in South Africa. This information can greatly assist in targeting interventions and implementing social distancing measures.

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Estimating reproduction numbers for adults and children from case data

Cited by 30 publications

References 30 publications

Pandemic Influenza H1N1

Pandemic Influenza H1N1

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Determining the dynamics of influenza transmission by age

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