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Chapman LAC, Aubry M, Maset N, Russell TW, Knock ES, Lees JA, Mallet HP, Cao-Lormeau VM, Kucharski AJ. Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia. Nat Commun 2023; 14:7330. [PMID: 37957160 PMCID: PMC10643399 DOI: 10.1038/s41467-023-43002-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Estimating the impact of vaccination and non-pharmaceutical interventions on COVID-19 incidence is complicated by several factors, including successive emergence of SARS-CoV-2 variants of concern and changing population immunity from vaccination and infection. We develop an age-structured multi-strain COVID-19 transmission model and inference framework to estimate vaccination and non-pharmaceutical intervention impact accounting for these factors. We apply this framework to COVID-19 waves in French Polynesia and estimate that the vaccination programme averted 34.8% (95% credible interval: 34.5-35.2%) of 223,000 symptomatic cases, 49.6% (48.7-50.5%) of 5830 hospitalisations and 64.2% (63.1-65.3%) of 1540 hospital deaths that would have occurred in a scenario without vaccination up to May 2022. We estimate the booster campaign contributed 4.5%, 1.9%, and 0.4% to overall reductions in cases, hospitalisations, and deaths. Our results suggest that removing lockdowns during the first two waves would have had non-linear effects on incidence by altering accumulation of population immunity. Our estimates of vaccination and booster impact differ from those for other countries due to differences in age structure, previous exposure levels and timing of variant introduction relative to vaccination, emphasising the importance of detailed analysis that accounts for these factors.
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Affiliation(s)
- Lloyd A C Chapman
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
| | - Maite Aubry
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
| | - Noémie Maset
- Cellule Epi-surveillance Plateforme COVID-19, Tahiti, French Polynesia
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Edward S Knock
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Cambridgeshire, UK
| | | | - Van-Mai Cao-Lormeau
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
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Gaythorpe KAM, Fitzjohn RG, Hinsley W, Imai N, Knock ES, Perez Guzman PN, Djaafara B, Fraser K, Baguelin M, Ferguson NM. Data pipelines in a public health emergency: The human in the machine. Epidemics 2023; 43:100676. [PMID: 36913804 DOI: 10.1016/j.epidem.2023.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 01/31/2023] [Accepted: 03/06/2023] [Indexed: 03/10/2023] Open
Abstract
In an emergency epidemic response, data providers supply data on a best-faith effort to modellers and analysts who are typically the end user of data collected for other primary purposes such as to inform patient care. Thus, modellers who analyse secondary data have limited ability to influence what is captured. During an emergency response, models themselves are often under constant development and require both stability in their data inputs and flexibility to incorporate new inputs as novel data sources become available. This dynamic landscape is challenging to work with. Here we outline a data pipeline used in the ongoing COVID-19 response in the UK that aims to address these issues. A data pipeline is a sequence of steps to carry the raw data through to a processed and useable model input, along with the appropriate metadata and context. In ours, each data type had an individual processing report, designed to produce outputs that could be easily combined and used downstream. Automated checks were in-built and added as new pathologies emerged. These cleaned outputs were collated at different geographic levels to provide standardised datasets. Finally, a human validation step was an essential component of the analysis pathway and permitted more nuanced issues to be captured. This framework allowed the pipeline to grow in complexity and volume and facilitated the diverse range of modelling approaches employed by researchers. Additionally, every report or modelling output could be traced back to the specific data version that informed it ensuring reproducibility of results. Our approach has been used to facilitate fast-paced analysis and has evolved over time. Our framework and its aspirations are applicable to many settings beyond COVID-19 data, for example for other outbreaks such as Ebola, or where routine and regular analyses are required.
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Affiliation(s)
- Katy A M Gaythorpe
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom.
| | - Rich G Fitzjohn
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Wes Hinsley
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Natsuko Imai
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Edward S Knock
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Pablo N Perez Guzman
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Bimandra Djaafara
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Keith Fraser
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Marc Baguelin
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Neil M Ferguson
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
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Imai N, Rawson T, Knock ES, Sonabend R, Elmaci Y, Perez-Guzman PN, Whittles LK, Kanapram DT, Gaythorpe KAM, Hinsley W, Djaafara BA, Wang H, Fraser K, FitzJohn RG, Hogan AB, Doohan P, Ghani AC, Ferguson NM, Baguelin M, Cori A. Quantifying the effect of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study. Lancet Public Health 2023; 8:e174-e183. [PMID: 36774945 PMCID: PMC9910835 DOI: 10.1016/s2468-2667(22)00337-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 02/11/2023]
Abstract
BACKGROUND The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3 weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the SARS-CoV-2 alpha variant prompted the UK to extend the interval between doses to 12 weeks. In this study, we aimed to quantify the effect of delaying the second vaccine dose in England. METHODS We used a previously described model of SARS-CoV-2 transmission, calibrated to COVID-19 surveillance data from England, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data, using a Bayesian evidence-synthesis framework. We modelled and compared the epidemic trajectory in the counterfactual scenario in which vaccine doses were administered 3 weeks apart against the real reported vaccine roll-out schedule of 12 weeks. We estimated and compared the resulting numbers of daily infections, hospital admissions, and deaths. In sensitivity analyses, we investigated scenarios spanning a range of vaccine effectiveness and waning assumptions. FINDINGS In the period from Dec 8, 2020, to Sept 13, 2021, the number of individuals who received a first vaccine dose was higher under the 12-week strategy than the 3-week strategy. For this period, we estimated that delaying the interval between the first and second COVID-19 vaccine doses from 3 to 12 weeks averted a median (calculated as the median of the posterior sample) of 58 000 COVID-19 hospital admissions (291 000 cumulative hospitalisations [95% credible interval 275 000-319 000] under the 3-week strategy vs 233 000 [229 000-238 000] under the 12-week strategy) and 10 100 deaths (64 800 deaths [60 200-68 900] vs 54 700 [52 800-55 600]). Similarly, we estimated that the 3-week strategy would have resulted in more infections compared with the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. In results by age group, the 12-week strategy led to more hospitalisations and deaths in older people in spring 2021, but fewer following the emergence of the delta variant during summer 2021. INTERPRETATION England's delayed-second-dose vaccination strategy was informed by early real-world data on vaccine effectiveness in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial (single-dose) vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths overall. FUNDING UK National Institute for Health Research; UK Medical Research Council; Community Jameel; Wellcome Trust; UK Foreign, Commonwealth and Development Office; Australian National Health and Medical Research Council; and EU.
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Affiliation(s)
- Natsuko Imai
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Thomas Rawson
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Edward S Knock
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, UK Health Security Agency, London School of Hygiene & Tropical Medicine, London, UK
| | - Raphael Sonabend
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; Department of Computer Science, Technische Universität Kaiserslautern, Kaiserslautern, Germany; Engineering Department, University of Cambridge, Cambridge, UK
| | - Yasin Elmaci
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Pablo N Perez-Guzman
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Lilith K Whittles
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Divya Thekke Kanapram
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Wes Hinsley
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Bimandra A Djaafara
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Haowei Wang
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Keith Fraser
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Richard G FitzJohn
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Alexandra B Hogan
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; School of Population Health, University of New South Wales, Sydney, NSW, Australia
| | - Patrick Doohan
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Azra C Ghani
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Neil M Ferguson
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, UK Health Security Agency, London School of Hygiene & Tropical Medicine, London, UK
| | - Marc Baguelin
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, UK Health Security Agency, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Anne Cori
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, UK Health Security Agency, London School of Hygiene & Tropical Medicine, London, UK.
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FitzJohn RG, Knock ES, Whittles LK, Perez-Guzman PN, Bhatia S, Guntoro F, Watson OJ, Whittaker C, Ferguson NM, Cori A, Baguelin M, Lees JA. Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate. Wellcome Open Res 2021; 5:288. [PMID: 34761122 PMCID: PMC8552050 DOI: 10.12688/wellcomeopenres.16466.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/07/2021] [Indexed: 11/20/2022] Open
Abstract
State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user’s needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.
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Affiliation(s)
- Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Edward S Knock
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK.,Modelling and Economics Unit, National Infection Service, Public Health England, London, UK
| | - Pablo N Perez-Guzman
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Fernando Guntoro
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK.,Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 8HT, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
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Sonabend R, Whittles LK, Imai N, Perez-Guzman PN, Knock ES, Rawson T, Gaythorpe KAM, Djaafara BA, Hinsley W, FitzJohn RG, Lees JA, Kanapram DT, Volz EM, Ghani AC, Ferguson NM, Baguelin M, Cori A. Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study. Lancet 2021; 398:1825-1835. [PMID: 34717829 PMCID: PMC8550916 DOI: 10.1016/s0140-6736(21)02276-5] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/28/2021] [Accepted: 10/07/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND England's COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories. METHODS This mathematical modelling study was done to assess the UK Government's four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions. FINDINGS The roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69-83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500-5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700-1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness. INTERPRETATION Our findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures. FUNDING National Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office.
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Affiliation(s)
- Raphael Sonabend
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, Public Health England, London School of Hygiene & Tropical Medicine, London, UK; Modelling and Economics Unit, National Infection Service, Public Health England, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Pablo N Perez-Guzman
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Edward S Knock
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, Public Health England, London School of Hygiene & Tropical Medicine, London, UK
| | - Thomas Rawson
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Bimandra A Djaafara
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Divya Thekke Kanapram
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Erik M Volz
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, Public Health England, London School of Hygiene & Tropical Medicine, London, UK.
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, Public Health England, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, Public Health England, London School of Hygiene & Tropical Medicine, London, UK.
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Imai N, Hogan AB, Williams L, Cori A, Mangal TD, Winskill P, Whittles LK, Watson OJ, Knock ES, Baguelin M, Perez-Guzman PN, Gaythorpe KA, Sonabend R, Ghani AC, Ferguson NM. Interpreting estimates of coronavirus disease 2019 (COVID-19) vaccine efficacy and effectiveness to inform simulation studies of vaccine impact: a systematic review. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.16992.1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Background: The multiple efficacious vaccines authorised for emergency use worldwide represent the first preventative intervention against coronavirus disease 2019 (COVID-19) that does not rely on social distancing measures. The speed at which data are emerging and the heterogeneities in study design, target populations, and implementation make it challenging to interpret and assess the likely impact of vaccine campaigns on local epidemics. We reviewed available vaccine efficacy and effectiveness studies to generate working estimates that can be used to parameterise simulation studies of vaccine impact. Methods: We searched MEDLINE, the World Health Organization’s Institutional Repository for Information Sharing, medRxiv, and vaccine manufacturer websites for studies that evaluated the emerging data on COVID-19 vaccine efficacy and effectiveness. Studies providing an estimate of the efficacy or effectiveness of a COVID-19 vaccine using disaggregated data against SARS-CoV-2 infection, symptomatic disease, severe disease, death, or transmission were included. We extracted information on study population, variants of concern (VOC), vaccine platform, dose schedule, study endpoints, and measures of impact. We applied an evidence synthesis approach to capture a range of plausible and consistent parameters for vaccine efficacy and effectiveness that can be used to inform and explore a variety of vaccination strategies as the COVID-19 pandemic evolves. Results: Of the 602 articles and reports identified, 53 were included in the analysis. The availability of vaccine efficacy and effectiveness estimates varied by vaccine and were limited for VOCs. Estimates for non-primary endpoints such as effectiveness against infection and onward transmission were sparse. Synthesised estimates were relatively consistent for the same vaccine platform for wild-type, but was more variable for VOCs. Conclusions: Assessment of efficacy and effectiveness of COVID-19 vaccines is complex. Simulation studies must acknowledge and capture the uncertainty in vaccine effectiveness to robustly explore and inform vaccination policies and policy around the lifting of non-pharmaceutical interventions.
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Knock ES, Whittles LK, Lees JA, Perez-Guzman PN, Verity R, FitzJohn RG, Gaythorpe KAM, Imai N, Hinsley W, Okell LC, Rosello A, Kantas N, Walters CE, Bhatia S, Watson OJ, Whittaker C, Cattarino L, Boonyasiri A, Djaafara BA, Fraser K, Fu H, Wang H, Xi X, Donnelly CA, Jauneikaite E, Laydon DJ, White PJ, Ghani AC, Ferguson NM, Cori A, Baguelin M. Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England. Sci Transl Med 2021. [PMID: 34158411 DOI: 10.25561/85146] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
We fitted a model of SARS-CoV-2 transmission in care homes and the community to regional surveillance data for England. Compared with other approaches, our model provides a synthesis of multiple surveillance data streams into a single coherent modeling framework, allowing transmission and severity to be disentangled from features of the surveillance system. Of the control measures implemented, only national lockdown brought the reproduction number (Rt eff) below 1 consistently; if introduced 1 week earlier, it could have reduced deaths in the first wave from an estimated 48,600 to 25,600 [95% credible interval (CrI): 15,900 to 38,400]. The infection fatality ratio decreased from 1.00% (95% CrI: 0.85 to 1.21%) to 0.79% (95% CrI: 0.63 to 0.99%), suggesting improved clinical care. The infection fatality ratio was higher in the elderly residing in care homes (23.3%, 95% CrI: 14.7 to 35.2%) than those residing in the community (7.9%, 95% CrI: 5.9 to 10.3%). On 2 December 2020, England was still far from herd immunity, with regional cumulative infection incidence between 7.6% (95% CrI: 5.4 to 10.2%) and 22.3% (95% CrI: 19.4 to 25.4%) of the population. Therefore, any vaccination campaign will need to achieve high coverage and a high degree of protection in vaccinated individuals to allow nonpharmaceutical interventions to be lifted without a resurgence of transmission.
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Affiliation(s)
- Edward S Knock
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
- Modelling and Economics Unit, National Infection Service, Public Health England, London NW9 5EQ, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Pablo N Perez-Guzman
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Lucy C Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Alicia Rosello
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Nikolas Kantas
- Faculty of Natural Sciences, Department of Mathematics, Imperial College London, London SW7 2BX, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Charlie Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Adhiratha Boonyasiri
- Department of Infectious Disease, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Bimandra A Djaafara
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Keith Fraser
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Xiaoyue Xi
- Faculty of Natural Sciences, Department of Mathematics, Imperial College London, London SW7 2BX, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
- NIHR HPRU in Emerging and Zoonotic Infections, Liverpool, UK
| | - Elita Jauneikaite
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Peter J White
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
- Modelling and Economics Unit, National Infection Service, Public Health England, London NW9 5EQ, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK.
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK.
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
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8
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Knock ES, Whittles LK, Lees JA, Perez-Guzman PN, Verity R, FitzJohn RG, Gaythorpe KAM, Imai N, Hinsley W, Okell LC, Rosello A, Kantas N, Walters CE, Bhatia S, Watson OJ, Whittaker C, Cattarino L, Boonyasiri A, Djaafara BA, Fraser K, Fu H, Wang H, Xi X, Donnelly CA, Jauneikaite E, Laydon DJ, White PJ, Ghani AC, Ferguson NM, Cori A, Baguelin M. Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England. Sci Transl Med 2021; 13:eabg4262. [PMID: 34158411 PMCID: PMC8432953 DOI: 10.1126/scitranslmed.abg4262] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/14/2021] [Accepted: 06/16/2021] [Indexed: 01/06/2023]
Abstract
We fitted a model of SARS-CoV-2 transmission in care homes and the community to regional surveillance data for England. Compared with other approaches, our model provides a synthesis of multiple surveillance data streams into a single coherent modeling framework, allowing transmission and severity to be disentangled from features of the surveillance system. Of the control measures implemented, only national lockdown brought the reproduction number (Rt eff) below 1 consistently; if introduced 1 week earlier, it could have reduced deaths in the first wave from an estimated 48,600 to 25,600 [95% credible interval (CrI): 15,900 to 38,400]. The infection fatality ratio decreased from 1.00% (95% CrI: 0.85 to 1.21%) to 0.79% (95% CrI: 0.63 to 0.99%), suggesting improved clinical care. The infection fatality ratio was higher in the elderly residing in care homes (23.3%, 95% CrI: 14.7 to 35.2%) than those residing in the community (7.9%, 95% CrI: 5.9 to 10.3%). On 2 December 2020, England was still far from herd immunity, with regional cumulative infection incidence between 7.6% (95% CrI: 5.4 to 10.2%) and 22.3% (95% CrI: 19.4 to 25.4%) of the population. Therefore, any vaccination campaign will need to achieve high coverage and a high degree of protection in vaccinated individuals to allow nonpharmaceutical interventions to be lifted without a resurgence of transmission.
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Affiliation(s)
- Edward S Knock
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
- Modelling and Economics Unit, National Infection Service, Public Health England, London NW9 5EQ, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Pablo N Perez-Guzman
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Lucy C Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Alicia Rosello
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Nikolas Kantas
- Faculty of Natural Sciences, Department of Mathematics, Imperial College London, London SW7 2BX, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Charlie Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Adhiratha Boonyasiri
- Department of Infectious Disease, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Bimandra A Djaafara
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Keith Fraser
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Xiaoyue Xi
- Faculty of Natural Sciences, Department of Mathematics, Imperial College London, London SW7 2BX, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
- NIHR HPRU in Emerging and Zoonotic Infections, Liverpool, UK
| | - Elita Jauneikaite
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Peter J White
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
- Modelling and Economics Unit, National Infection Service, Public Health England, London NW9 5EQ, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK.
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London W2 1PG, UK.
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
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9
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Crump RE, Huang CI, Knock ES, Spencer SEF, Brown PE, Mwamba Miaka E, Shampa C, Keeling MJ, Rock KS. Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. PLoS Comput Biol 2021; 17:e1008532. [PMID: 33513134 PMCID: PMC7899378 DOI: 10.1371/journal.pcbi.1008532] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 02/22/2021] [Accepted: 11/12/2020] [Indexed: 11/18/2022] Open
Abstract
Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones (∼100,000 population size), which allows for calibration of a mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.14, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly-on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu-Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country.
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Affiliation(s)
- Ronald E. Crump
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
- The School of Life Sciences, The University of Warwick, Coventry, United Kingdom
- * E-mail:
| | - Ching-I Huang
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Edward S. Knock
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- The Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Simon E. F. Spencer
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- The Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Paul E. Brown
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
| | - Erick Mwamba Miaka
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, D.R.C.
| | - Chansy Shampa
- Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, D.R.C.
| | - Matt J. Keeling
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
- The School of Life Sciences, The University of Warwick, Coventry, United Kingdom
| | - Kat S. Rock
- Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom
- Mathematics Institute, The University of Warwick, Coventry, United Kingdom
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10
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Knock ES, Whittles LK, Perez-Guzman PN, Bhatia S, Guntoro F, Watson OJ, Whittaker C, Ferguson NM, Cori A, Baguelin M, FitzJohn RG, Lees JA. Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate. Wellcome Open Res 2020; 5:288. [DOI: 10.12688/wellcomeopenres.16466.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2020] [Indexed: 11/20/2022] Open
Abstract
State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user’s needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.
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11
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Sherrard-Smith E, Hogan AB, Hamlet A, Watson OJ, Whittaker C, Winskill P, Ali F, Mohammad AB, Uhomoibhi P, Maikore I, Ogbulafor N, Nikau J, Kont MD, Challenger JD, Verity R, Lambert B, Cairns M, Rao B, Baguelin M, Whittles LK, Lees JA, Bhatia S, Knock ES, Okell L, Slater HC, Ghani AC, Walker PGT, Okoko OO, Churcher TS. The potential public health consequences of COVID-19 on malaria in Africa. Nat Med 2020; 26:1411-1416. [PMID: 32770167 DOI: 10.1038/s41591-020-1025-y] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/23/2020] [Indexed: 01/08/2023]
Abstract
The burden of malaria is heavily concentrated in sub-Saharan Africa (SSA) where cases and deaths associated with COVID-19 are rising1. In response, countries are implementing societal measures aimed at curtailing transmission of SARS-CoV-22,3. Despite these measures, the COVID-19 epidemic could still result in millions of deaths as local health facilities become overwhelmed4. Advances in malaria control this century have been largely due to distribution of long-lasting insecticidal nets (LLINs)5, with many SSA countries having planned campaigns for 2020. In the present study, we use COVID-19 and malaria transmission models to estimate the impact of disruption of malaria prevention activities and other core health services under four different COVID-19 epidemic scenarios. If activities are halted, the malaria burden in 2020 could be more than double that of 2019. In Nigeria alone, reducing case management for 6 months and delaying LLIN campaigns could result in 81,000 (44,000-119,000) additional deaths. Mitigating these negative impacts is achievable, and LLIN distributions in particular should be prioritized alongside access to antimalarial treatments to prevent substantial malaria epidemics.
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Affiliation(s)
- Ellie Sherrard-Smith
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Alexandra B Hogan
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Charlie Whittaker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Fatima Ali
- National Malaria Elimination Programme, Abuja, Nigeria
| | | | | | | | | | - Jamilu Nikau
- National Malaria Elimination Programme, Abuja, Nigeria
| | - Mara D Kont
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Joseph D Challenger
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Ben Lambert
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Matthew Cairns
- Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK
| | - Bhargavi Rao
- Manson Unit, Médecins Sans Frontières (Operational Centre Amsterdam), London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Edward S Knock
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Hannah C Slater
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.,PATH, Seattle, WA, USA
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | | | - Thomas S Churcher
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
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12
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Abstract
This paper is concerned with a stochastic model for the spread of an epidemic with a contact tracing scheme, in which diagnosed individuals may name some of their infectious contacts, who are then removed if they have not been already. Traced individuals may or may not also be asked to name their own contacts. The epidemic is studied by considering an approximating, modified birth-death process with intersibling dependencies, for which a threshold parameter and expressions from which extinction probabilities may be calculated are derived. When all individuals can name their contacts, it is shown that this threshold parameter depends on the infectious period distribution only through its mean. Numerical studies show that the infectious period distribution choice can have a material effect on the threshold behaviour of an epidemic, while the dependencies help reduce spread.
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13
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Ball FG, Knock ES, O'Neill PD. Stochastic epidemic models featuring contact tracing with delays. Math Biosci 2015; 266:23-35. [PMID: 26037511 DOI: 10.1016/j.mbs.2015.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Revised: 05/13/2015] [Accepted: 05/15/2015] [Indexed: 10/23/2022]
Abstract
This paper is concerned with a stochastic model for the spread of an SEIR (susceptible → exposed (=latent) → infective → removed) epidemic with a contact tracing scheme, in which removed individuals may name some of their infectious contacts, who are then removed if they have not been already after some tracing delay. The epidemic is analysed via an approximating, modified birth-death process, for which a type-reproduction number is derived in terms of unnamed individuals, that is shown to be infinite when the contact rate is sufficiently large. We obtain explicit results under the assumption of either constant or exponentially distributed infectious periods, including the epidemic extinction probability in the former case. Numerical illustrations show that, while the distributions of latent periods and delays have an effect on the spread of the epidemic, the assumption of whether the delays experienced by individuals infected by the same individual are of the same or independent length makes little difference.
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Affiliation(s)
- Frank G Ball
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
| | - Edward S Knock
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
| | - Philip D O'Neill
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
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Abstract
This paper considers the problem of choosing between competing models for infectious disease final outcome data in a population that is partitioned into households. The epidemic models are stochastic individual-based transmission models of the susceptible-infective-removed type. The main focus is on various algorithms for the estimation of Bayes factors, of which a path sampling-based algorithm is seen to give the best results. We also explore theoretical properties in the case where the within-model prior distributions become increasingly uninformative, which show the need for caution when using Bayes factors as a model choice tool. A suitable form of deviance information criterion is also considered for comparison. The theory and methods are illustrated with both artificial data, and influenza data from the Tecumseh study of illness.
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Affiliation(s)
- Edward S Knock
- School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK
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Ball FG, Knock ES, O'Neill PD. Control of emerging infectious diseases using responsive imperfect vaccination and isolation. Math Biosci 2009; 216:100-13. [PMID: 18789951 DOI: 10.1016/j.mbs.2008.08.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2008] [Revised: 08/11/2008] [Accepted: 08/15/2008] [Indexed: 11/26/2022]
Abstract
This paper is concerned with a stochastic model for the spread of an SEIR (susceptible --> exposed (= latent) --> infective --> removed) epidemic among a population partitioned into households, featuring different rates of infection for within and between households. The model incorporates responsive vaccination and isolation policies, based upon the appearance of diagnosed cases in households. Different models for imperfect vaccine response are considered. A threshold parameter R*, which determines whether or not a major epidemic can occur, and the probability of a major epidemic are obtained for different infectious and latent period distributions. Simpler expressions for these quantities are obtained in the limiting case of infinite within-household infection rate. Numerical studies suggest that the choice of infectious period distribution and whether or not latent individuals are vaccine-sensitive have a material influence on the spread of the epidemic, while, for given vaccine efficacy, the choice of vaccine action model is less influential. They also suggest that an effective isolation policy has a more significant impact than vaccination. The results show that R* alone is not sufficient to summarise the potential for an epidemic.
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Affiliation(s)
- Frank G Ball
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
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