Rigorous surveillance is necessary for high confidence in end-of-outbreak declarations for Ebola and other infectious diseases

Thompson, Robin N; Morgan, Oliver; Jalava, Katri

doi:10.1098/rstb.2018.0431

Cited by 42 publications

(62 citation statements)

References 42 publications

(56 reference statements)

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“…If information is available to quantify changes in reporting over time, this would permit correction to allow for temporal variation in underreporting, which might otherwise be interpreted as variation in the reproduction number. Underreporting has hindered estimation of disease burden for a number of diseases including dengue (Shepard et al, 2013), yellow fever (Garske et al, 2014) and Ebola (Dalziel et al, 2018;Thompson et al, 2019b). Other additions to our work might involve allowing for reporting delays (Cowling et al, 2010;van de Kassteele et al, 2019).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…The 2001 foot and mouth disease outbreak in the UK and the 2003 SARS pandemic were new epidemics that were driven extinct without vaccination before they became endemic, but both exhibited long tails before eradication was achieved. The 2014-16 Ebola epidemic in West Africa was eliminated (with vaccination at the end of the epidemic [72]), but eradication took some time with flare ups occurring in different countries [73,74].…”

Section: (D) Is Global Eradication Of Sars-cov-2 a Realistic Possibilmentioning

confidence: 99%

Key questions for modelling COVID-19 exit strategies

et al. 2020

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Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute ‘Models for an exit strategy’ workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

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“…The model is directly applicable for situations in which surveillance does not depend on the self-reporting of symptomatic hosts, which makes it appropriate for most animal and plant species. Our model is also appropriate for certain monitoring schemes aimed at pathogens of humans, for example, visitations of rural villages to find Ebola infections where access to healthcare is limited [31,32].…”

Section: Discussionmentioning

confidence: 99%

Translating surveillance data into incidence estimates

Bourhis

Gottwald

Bosch

2019

Phil. Trans. R. Soc. B

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Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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Rigorous surveillance is necessary for high confidence in end-of-outbreak declarations for Ebola and other infectious diseases

Cited by 42 publications

References 42 publications

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

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

Key questions for modelling COVID-19 exit strategies

Translating surveillance data into incidence estimates

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