ArticlePloS one2021
Understanding an evolving pandemic: An analysis of the clinical time delay distributions of COVID-19 in the United Kingdom.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 48 citations in OpenAlex.
- Review
- Estimating the causal impact of non-pharmaceutical interventions on COVID-19 spread in seven EU countries via machine learning.Scientific reports · 2025Article
- The real-time infection hospitalisation and fatality risk across the COVID-19 pandemic in England.Nature communications · 2024Article
- Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe.Journal of clinical medicine · 2024Article
- Article
- Time warping between main epidemic time series in epidemiological surveillance.PLoS computational biology · 2023Article
- Application of distributed lag models and spatial analysis for comparing the performance of the COVID-19 control decisions in European countries.Scientific reports · 2023Article
- Article
- Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series.Science advances · 2023Article
- Empirical distributions of time intervals between COVID-19 cases and more severe outcomes in Scotland.PloS one · 2023Article
- Estimation and prediction of the multiply exponentially decaying daily case fatality rate of COVID-19.The Journal of supercomputing · 2023Article
- COVID-19 in-hospital mortality during the first two pandemic waves, at Helen Joseph Hospital, South Africa.The Pan African medical journal · 2023Article
- Replacement dynamics and the pathogenesis of the Alpha, Delta and Omicron variants of SARS-CoV-2.Epidemiology and infection · 2022Article
- Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19.Nature communications · 2022Article
- Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022.BMC infectious diseases · 2022Article
- Education and COVID-19 excess mortality.Economics and human biology · 2022Article
- Global Expansion of SARS-CoV-2 Variants of Concern: Dispersal Patterns and Influence of Air Travel.medRxiv : the preprint server for health sciences · 2022Article
- Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.International journal of environmental research and public health · 2022Article
- Evaluating methodological approaches to assess the severity of infection with SARS-CoV-2 variants: scoping review and applications on Belgian COVID-19 data.BMC infectious diseases · 2022Article
- Transmission dynamics of monkeypox in the United Kingdom: contact tracing study.BMJ (Clinical research ed.) · 2022Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding and monitoring the epidemiological time delay dynamics of SARS-CoV-2 infection provides insights that are key to discerning changes in the phenotype of the virus, the demographics impacted, the efficacy of treatment, and the ability of the health service to manage large volumes of patients. This paper analyses how the pandemic has evolved in the United Kingdom through the temporal changes to the epidemiological time delay distributions for clinical outcomes. Using the most complete clinical data presently available, we have analysed, through a doubly interval censored Bayesian modelling approach, the time from infection to a clinical outcome. Across the pandemic, for the periods that were defined as epidemiologically distinct, the modelled mean ranges from 8.0 to 9.7 days for infection to hospitalisation, 10.3 to 15.0 days for hospitalisation to death, and 17.4 to 24.7 days for infection to death. The time delay from infection to hospitalisation has increased since the first wave of the pandemic. A marked decrease was observed in the time from hospitalisation to death and infection to death at times of high incidence when hospitals and ICUs were under the most pressure. There is a clear relationship between age groups that is indicative of the youngest and oldest demographics having the shortest time delay distributions before a clinical outcome. A statistically significant difference was found between genders for the time delay from infection to hospitalisation, which was not found for hospitalisation to death. The results by age group indicate that younger demographics that require clinical intervention for SARS-CoV-2 infection are more likely to require earlier hospitalisation that leads to a shorter time to death, which is suggestive of the largely more vulnerable nature of these individuals that succumb to infection. The distinction found between genders for exposure to hospitalisation is revealing of gender healthcare seeking behaviours.
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