Publications by authors named "Helena B Stage"

6 Publications

  • Page 1 of 1

SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return.

R Soc Open Sci 2021 Aug 4;8(8):210310. Epub 2021 Aug 4.

The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.

In this paper, we present work on SARS-CoV-2 transmission in UK higher education settings using multiple approaches to assess the extent of university outbreaks, how much those outbreaks may have led to spillover in the community, and the expected effects of control measures. Firstly, we found that the distribution of outbreaks in universities in late 2020 was consistent with the expected importation of infection from arriving students. Considering outbreaks at one university, larger halls of residence posed higher risks for transmission. The dynamics of transmission from university outbreaks to wider communities is complex, and while sometimes spillover does occur, occasionally even large outbreaks do not give any detectable signal of spillover to the local population. Secondly, we explored proposed control measures for reopening and keeping open universities. We found the proposal of staggering the return of students to university residence is of limited value in terms of reducing transmission. We show that student adherence to testing and self-isolation is likely to be much more important for reducing transmission during term time. Finally, we explored strategies for testing students in the context of a more transmissible variant and found that frequent testing would be necessary to prevent a major outbreak.
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http://dx.doi.org/10.1098/rsos.210310DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8334840PMC
August 2021

SARS-CoV-2 antigen testing: weighing the false positives against the costs of failing to control transmission.

Lancet Respir Med 2021 07 14;9(7):685-687. Epub 2021 Jun 14.

Department of Clinical Sciences, and Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, UK; Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK; WHO Collaborating Centre in Tuberculosis and Social Medicine, Department of Global Public Health, Karolinska Institutet, Solna, Sweden.

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http://dx.doi.org/10.1016/S2213-2600(21)00234-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203180PMC
July 2021

Shut and re-open: the role of schools in the spread of COVID-19 in Europe.

Philos Trans R Soc Lond B Biol Sci 2021 07 31;376(1829):20200277. Epub 2021 May 31.

Emergency Response Department, Public Health England, London, UK.

We investigate the effect of school closure and subsequent reopening on the transmission of COVID-19, by considering Denmark, Norway, Sweden and German states as case studies. By comparing the growth rates in daily hospitalizations or confirmed cases under different interventions, we provide evidence that school closures contribute to a reduction in the growth rate approximately 7 days after implementation. Limited school attendance, such as older students sitting exams or the partial return of younger year groups, does not appear to significantly affect community transmission. In countries where community transmission is generally low, such as Denmark or Norway, a large-scale reopening of schools while controlling or suppressing the epidemic appears feasible. However, school reopening can contribute to statistically significant increases in the growth rate in countries like Germany, where community transmission is relatively high. In all regions, a combination of low classroom occupancy and robust test-and-trace measures were in place. Our findings underscore the need for a cautious evaluation of reopening strategies. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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http://dx.doi.org/10.1098/rstb.2020.0277DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165592PMC
July 2021

Challenges in control of COVID-19: short doubling time and long delay to effect of interventions.

Philos Trans R Soc Lond B Biol Sci 2021 07 31;376(1829):20200264. Epub 2021 May 31.

Department of Mathematics, The University of Manchester, Manchester, UK.

Early assessments of the growth rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but as cases were recorded in multiple countries, more robust inferences could be made. Using multiple countries, data streams and methods, we estimated that, when unconstrained, European COVID-19 confirmed cases doubled on average every 3 days (range 2.2-4.3 days) and Italian hospital and intensive care unit admissions every 2-3 days; values that are significantly lower than the 5-7 days dominating the early published literature. Furthermore, we showed that the impact of physical distancing interventions was typically not seen until at least 9 days after implementation, during which time confirmed cases could grow eightfold. We argue that such temporal patterns are more critical than precise estimates of the time-insensitive basic reproduction number for initiating interventions, and that the combination of fast growth and long detection delays explains the struggle in countries' outbreak response better than large values of alone. One year on from first reporting these results, reproduction numbers continue to dominate the media and public discourse, but robust estimates of unconstrained growth remain essential for planning worst-case scenarios, and detection delays are still key in informing the relaxation and re-implementation of interventions. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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http://dx.doi.org/10.1098/rstb.2020.0264DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165602PMC
July 2021

Superinfection and the evolution of an initial asymptomatic stage.

R Soc Open Sci 2021 Jan 27;8(1):202212. Epub 2021 Jan 27.

Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.

Pathogens have evolved a variety of life-history strategies. An important strategy consists of successful transmission by an infected host before the appearance of symptoms, that is, while the host is still partially or fully asymptomatic. During this initial stage of infection, it is possible for another pathogen to superinfect an already infected host and replace the previously infecting pathogen. Here, we study the effect of superinfection during the first stage of an infection on the evolutionary dynamics of the degree to which the host is asymptomatic (host latency) in that same stage. We find that superinfection can lead to major differences in evolutionary behaviour. Most strikingly, the duration of immunity following infection can significantly influence pathogen evolutionary dynamics, whereas without superinfection the outcomes are independent of host immunity. For example, changes in host immunity can drive evolutionary transitions from a fully symptomatic to a fully asymptomatic first infection stage. Additionally, if superinfection relative to susceptible infection is strong enough, evolution can lead to a unique strategy of latency that corresponds to a local fitness minimum, and is therefore invasible by nearby mutants. Thus, this strategy is a branching point, and can lead to coexistence of pathogens with different latencies. Furthermore, in this new framework with superinfection, we also find that there can exist two interior singular strategies. Overall, new evolutionary outcomes can cascade from superinfection.
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http://dx.doi.org/10.1098/rsos.202212DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890506PMC
January 2021

Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example.

Infect Dis Model 2020 4;5:409-441. Epub 2020 Jul 4.

Department of Mathematics, University of Manchester, UK.

During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.
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http://dx.doi.org/10.1016/j.idm.2020.06.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334973PMC
July 2020
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