Journal article
Reconstructing the first COVID-19 pandemic wave with minimal data in England
- Abstract:
- Accurate measurement of exposure to SARS-CoV-2 in the population is crucial for understanding the dynamics of disease transmission and evaluating the impacts of interventions. However, it was particularly challenging to achieve this in the early phase of a pandemic because of the sparsity of epidemiological data. We previously developed an early pandemic diagnostic tool that linked minimum datasets: seroprevalence, mortality and infection testing data to estimate the true exposure in different regions of England and found levels of SARS-CoV-2 population exposure to be considerably higher than suggested by seroprevalence surveys. Here, we re-examine and evaluate the model in the context of reconstructing the first COVID-19 epidemic wave in England from three perspectives: validation against the Office for National Statistics (ONS) Coronavirus Infection Survey, relationship among model performance and data abundance and time-varying case detection ratios. We find that our model can recover the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 if two or three serological measurements are given as additional model inputs, while the second wave during winter of 2020 is validated by estimates from the ONS Coronavirus Infection Survey. Moreover, the model estimates that by the end of October in 2020 the UK government's official COVID-9 online dashboard reported COVID-19 cases only accounted for 9.1 % of cumulative exposure, dramatically varying across the two epidemic waves in England in 2020, 4.3 % vs 43.7 %.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 4.0MB, Terms of use)
-
- Publisher copy:
- 10.1016/j.epidem.2025.100814
Authors
- Publisher:
- Elsevier
- Journal:
- Epidemics More from this journal
- Volume:
- 50
- Article number:
- 100814
- Place of publication:
- Netherlands
- Publication date:
- 2025-01-14
- Acceptance date:
- 2025-01-10
- DOI:
- EISSN:
-
1878-0067
- ISSN:
-
1755-4365
- Pmid:
-
39827808
- Language:
-
English
- Keywords:
- Pubs id:
-
2079524
- Local pid:
-
pubs:2079524
- Deposit date:
-
2025-01-28
Terms of use
- Copyright holder:
- Chen et al
- Copyright date:
- 2025
- Rights statement:
- © 2025 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
If you are the owner of this record, you can report an update to it here: Report update to this record