Publications
18 results found
McCabe R, Danelian G, Panovska-Griffiths J, et al., 2024, Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey., Infect Dis Model, Vol: 9, Pages: 299-313
Key epidemiological parameters, including the effective reproduction number, R(t), and the instantaneous growth rate, r(t), generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the "emergency" to "endemic" phase of the pandemic. The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the "ONS-based" R(t) and r(t) across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters. Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates. Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public h
McCabe R, Donnelly CA, 2023, Public awareness of and opinions on the use of mathematical transmission modelling to inform public health policy in the United Kingdom., J R Soc Interface, Vol: 20
Mathematical modelling is used to inform public health policy, particularly so during the COVID-19 pandemic. As the public are key stakeholders, understanding the public perceptions of these tools is vital. To complement our previous study on the science-policy interface, novel survey data were collected via an online panel ('representative' sample) and social media ('non-probability' sample). Many questions were asked twice, in reference to the period 'prior to' (retrospectively) and 'during' the COVID-19 pandemic. Respondents reported being increasingly aware of modelling in informing policy during the pandemic, with higher levels of awareness among social media respondents. Modelling informing policy was perceived as more reliable during the pandemic than in reference to the pre-pandemic period in both samples. Trust in government public health advice remained high within both samples but was lower during the pandemic in comparison with the (retrospective) pre-pandemic period. The decay in trust was greater among social media respondents. Many respondents explicitly made the distinction that their trust was reserved for 'scientists' and not 'politicians'. Almost all respondents believed governments have responsibility for communicating modelling to the public. These results provide a reminder of the skewed conclusions that could be drawn from non-representative samples.
Cuomo-Dannenburg G, McCain K, McCabe R, et al., 2023, Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review, Lancet Infectious Diseases, ISSN: 1473-3099
Recent Marburg virus disease (MVD) outbreaks in Equatorial Guinea and Tanzania highlighted the importance of better understanding this highly lethal infectious pathogen. We conducted a systematic review (PROSPERO CRD42023393345), reported according to PRISMA guidelines, of peer-reviewed papers reporting historical outbreaks, modelling studies and epidemiological parameters focused on MVD. We searched PubMed and Web of Science until 31/03/2023. Two reviewers evaluated all titles and abstracts, with consensus-based decision-making. To ensure agreement, 31% (13/42) of studies were double-extracted and a custom-designed quality assessment questionnaire was used for risk of bias assessment. We present detailed information on 478 reported cases and 385 deaths from MVD. Analysis of historical outbreaks and seroprevalence estimates suggests the possibility of undetected MVD outbreaks, asymptomatic transmission and/or cross-reactivity with other pathogens. Only one study presented a mathematical model of MVD transmission. We estimate an unadjusted, pooled total random effect case fatality ratio for MVD of 61.9% (95% CI: 38.8-80.6%, I^2=93%). We identify important epidemiological parameters relating to transmission and natural history for which there are few estimates. This review and the accompanying database provide a comprehensive overview of MVD epidemiology, and identify key knowledge gaps, contributing crucial information for mathematical models to support future MVD epidemic responses.
McCabe R, Sheppard R, Abdelmagid N, et al., 2023, Alternative epidemic indicators for COVID-19 in three settings with incomplete death registration systems, Science Advances, Vol: 23, Pages: 1-10, ISSN: 2375-2548
Not all COVID-19 deaths are officially reported, and particularly in low-income and humanitarian settings, the magnitude of reporting gaps remains sparsely characterized. Alternative data sources, including burial site worker reports, satellite imagery of cemeteries, and social media–conducted surveys of infection may offer solutions. By merging these data with independently conducted, representative serological studies within a mathematical modeling framework, we aim to better understand the range of underreporting using examples from three major cities: Addis Ababa (Ethiopia), Aden (Yemen), and Khartoum (Sudan) during 2020. We estimate that 69 to 100%, 0.8 to 8.0%, and 3.0 to 6.0% of COVID-19 deaths were reported in each setting, respectively. In future epidemics, and in settings where vital registration systems are limited, using multiple alternative data sources could provide critically needed, improved estimates of epidemic impact. However, ultimately, these systems are needed to ensure that, in contrast to COVID-19, the impact of future pandemics or other drivers of mortality is reported and understood worldwide.
Some BM, Da DF, McCabe R, et al., 2022, Adapting field-mosquito collection techniques in a perspective of near-infrared spectroscopy implementation, PARASITES & VECTORS, Vol: 15, ISSN: 1756-3305
- Author Web Link
- Cite
- Citations: 1
Mullins E, McCabe R, Bird SM, et al., 2022, Tracking the incidence and risk factors for SARS-CoV-2 infection using historical maternal booking serum samples, PLoS One, Vol: 17, Pages: e0273966-e0273966, ISSN: 1932-6203
The early transmission dynamics of SARS-CoV-2 in the UK are unknown but their investigation is critical to aid future pandemic planning. We tested over 11,000 anonymised, stored historic antenatal serum samples, given at two north-west London NHS trusts in 2019 and 2020, for total antibody to SARS-CoV-2 receptor binding domain (anti-RBD). Estimated prevalence of seroreactivity increased from 1% prior to mid-February 2020 to 17% in September 2020. Our results show higher prevalence of seroreactivity to SARS-CoV-2 in younger, non-white ethnicity, and more deprived groups. We found no significant interaction between the effects of ethnicity and deprivation. Derived from prevalence, the estimated incidence of seroreactivity reflects the trends observed in daily hospitalisations and deaths in London that followed 10 and 13 days later, respectively. We quantified community transmission of SARS-CoV-2 in London, which peaked in late March / early April 2020 with no evidence of community transmission until after January 2020. Our study was not able to determine the date of introduction of the SARS-CoV-2 virus but demonstrates the value of stored antenatal serum samples as a resource for serosurveillance during future outbreaks.
Gray L, Asay BC, Hephaestus B, et al., 2022, Back to the Future: Quantifying Wing Wear as a Method to Measure Mosquito Age, AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE, Vol: 107, Pages: 689-700, ISSN: 0002-9637
- Author Web Link
- Cite
- Citations: 1
McCabe R, Kont MD, Watson O, et al., 2021, Communicating uncertainty in epidemic models, Epidemics: the journal of infectious disease dynamics, Vol: 37, Pages: 1-6, ISSN: 1755-4365
While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.
McCabe R, Donnelly C, 2021, Disease transmission and control modelling at the science-policy interface, Interface Focus, Vol: 11, Pages: 1-13, ISSN: 2042-8901
The coronavirus disease 2019 (COVID-19) pandemic has disrupted the lives of billions across the world. Mathematical modelling has been a key tool deployed throughout the pandemic to explore the potential public health impact of an unmitigated epidemic. The results of such studies have informed government’s decisions to implement non-pharmaceutical interventions to control the spread of the virus.In this article we explore the complex relationships between models, decision-making, the media and the public during the COVID-19 pandemic in the United Kingdom of Great Britain and Northern Ireland (UK). Doing so not only provides important historical context of COVID-19 modelling and how it has shaped the UK response, but as the pandemic continues and looking towards future pandemic preparedness, understanding these relationships and how they might be improved is critical. As such, we have synthesised information gathered via three methods: a survey to publicly listed attendees of SAGE, SPI-M and other comparable advisory bodies, interviews with science communication experts and former scientific advisors, and reviewing some of the key COVID-19 modelling literature from 2020. Our research highlights the desire for increased bidirectional communication between modellers, decision-makers and the public, as well as the need to convey uncertainty inherent in transmission models in a clear manner. These aspects should be considered carefully ahead of the next emergency response.
McCabe R, Kont M, Schmit N, et al., 2021, Modelling ICU capacity under different epidemiological scenarios of the COVID-19 pandemic in three western European countries, International Journal of Epidemiology, Vol: 50, Pages: 753-767, ISSN: 0300-5771
Background: The coronavirus disease 2019 (COVID-19) pandemic has placed enormous strain on intensive care units (ICUs) in Europe. Ensuring access to care, irrespective of COVID-19 status, in winter 2020/21 is essential.Methods: An integrated model of hospital capacity planning and epidemiological projections of COVID-19 patients is used to estimate the demand for and resultant spare capacity of ICU beds, staff, and ventilators under different epidemic scenarios in France, Germany, and Italy across the 2020/21 winter period. The effect of implementing lockdowns triggered by different numbers of COVID-19 patients in ICU under varying levels of effectiveness is examined, using a ‘dual-demand’ (COVID-19 and non-COVID-19) patient model.Results: Without sufficient mitigation, we estimate that COVID-19 ICU patient numbers will exceed those seen in the first peak, resulting in substantial capacity deficits, with beds being consistently found to be the most constrained resource. Reactive lockdowns could lead to large improvements in ICU capacity during the winter season, with pressure being most effectively alleviated when lockdown is triggered early and sustained under a higher level of suppression. The success of such interventions also depends on baseline bed numbers and average non-COVID-19 patient occupancy.Conclusions: Reductions in capacity deficits under different scenarios must be weighed against the feasibility and drawbacks of further lockdowns. Careful, continuous decision-making by national policymakers will be required across the winter period 2020/21.
Da DF, McCabe R, Somé BM, et al., 2021, Detection of Plasmodium falciparum in laboratory-reared and naturally infected wild mosquitoes using near-infrared spectroscopy., Scientific Reports, Vol: 11, Pages: 10289-10289, ISSN: 2045-2322
There is an urgent need for high throughput, affordable methods of detecting pathogens inside insect vectors to facilitate surveillance. Near-infrared spectroscopy (NIRS) has shown promise to detect arbovirus and malaria in the laboratory but has not been evaluated in field conditions. Here we investigate the ability of NIRS to identify Plasmodium falciparum in Anopheles coluzzii mosquitoes. NIRS models trained on laboratory-reared mosquitoes infected with wild malaria parasites can detect the parasite in comparable mosquitoes with moderate accuracy though fails to detect oocysts or sporozoites in naturally infected field caught mosquitoes. Models trained on field mosquitoes were unable to predict the infection status of other field mosquitoes. Restricting analyses to mosquitoes of uninfectious and highly-infectious status did improve predictions suggesting sensitivity and specificity may be better in mosquitoes with higher numbers of parasites. Detection of infection appears restricted to homogenous groups of mosquitoes diminishing NIRS utility for detecting malaria within mosquitoes.
Christen P, D'Aeth J, Lochen A, et al., 2021, The J-IDEA pandemic planner: a framework for implementing hospital provision interventions during the COVID-19 pandemic, Medical Care, Vol: 59, Pages: 371-378, ISSN: 0025-7079
Background : Planning for extreme surges in demand for hospital care of patientsrequiring urgent life-saving treatment for COVID-19, whilst retaining capacity for otheremergency conditions, is one of the most challenging tasks faced by healthcareproviders and policymakers during the pandemic. Health systems must be wellpreparedto cope with large and sudden changes in demand by implementinginterventions to ensure adequate access to care. We developed the first planning toolfor the COVID-19 pandemic to account for how hospital provision interventions (suchas cancelling elective surgery, setting up field hospitals, or hiring retired staff) will affectthe capacity of hospitals to provide life-saving care.Methods : We conducted a review of interventions implemented or considered in 12 European countries in March-April 2020, an evaluation of their impact on capacity, anda review of key parameters in the care of COVID-19 patients. This information wasused to develop a planner capable of estimating the impact of specific interventions ondoctors, nurses, beds and respiratory support equipment. We applied this to ascenario-based case study of one intervention, the set-up of field hospitals in England,under varying levels of COVID-19 patients.Results : The J-IDEA pandemic planner is a hospital planning tool that allows hospitaladministrators, policymakers and other decision-makers to calculate the amount ofcapacity in terms of beds, staff and crucial medical equipment obtained byimplementing the interventions. Flexible assumptions on baseline capacity, the numberof hospitalisations, staff-to-beds ratios, and staff absences due to COVID-19 make theplanner adaptable to multiple settings. The results of the case study show that whilefield hospitals alleviate the burden on the number of beds available, this intervention isfutile unless the deficit of critical care nurses is addressed first.Discussion : The tool supports decision-makers in delivering a fast and effectiveresponse to
Fu H, Wang H, Xi X, et al., 2021, A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China, International Journal of Infectious Diseases, Vol: 102, Pages: 463-471, ISSN: 1201-9712
Objectives: This data collation effort aims to provide a comprehensive database to describe the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19)across main provinces in China. Methods: From mid-January to March 2020, we extracted publicly available data on the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted a descriptive analysis of the epidemics in the six most-affected provinces. Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends were different across provinces. Compared to Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as local transmission of COVID-19 declined, switching the focus of measures to testing and quarantine of inbound travellers could help to sustain the control of the epidemic. Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database with these indicators and information on control measures provides useful source for exploring further research and policy planning for response to the COVID-19 epidemic.
McCabe R, Kont M, Schmit N, et al., 2020, Report 36: Modelling ICU capacity under different epidemiological scenarios of the COVID-19 pandemic in three western European countries
The coronavirus disease 2019 (COVID-19) pandemic has placed enormous strain on healthcare systems, particularly intensive care units (ICUs), with COVID-19 patient care being a key concern of healthcare system planning for winter 2020/21. Ensuring that all patients who require intensive care, irrespective of COVID-19 status, can access it during this time is essential. This study uses an integrated model of hospital capacity planning and epidemiological projections of COVID-19 patients to estimate the spare capacity of key ICU resources under different epidemic scenarios in France, Germany and Italy across the winter period of 2020/21. In particular, we examine the effect of implementing suppression strategies of varying effectiveness, triggered by different numbers of COVID-19 patients in ICU. The use of a ‘dual-demand’ (COVID-19 and non-COVID-19) patient model and the consideration of multiple ICU resources that determine capacity (beds, doctors, nurses and ventilators) and the interdependencies between them, provides a detailed insight into potential capacity constraints this winter. Without sufficient mitigation, we estimate that COVID-19 ICU patient numbers will exceed those seen in the first peak, resulting in substantial capacity deficits, with beds being consistently found to be the most constrained resource across countries. Lockdowns triggered based on ICU capacity could lead to large improvements in spare capacity during the winter season, with pressure being most effectively alleviated when lockdown is triggered early and implemented at a higher level of suppression. In many cases, maximum deficits are reduced to lower levels which can then be managed by expanding supply-side hospital capacity, to ensure that all patients can receive treatment. The success of such interventions also depends on baseline ICU bed numbers and average non-COVID-19 patient occupancy. We find that lockdowns of longer duration reduce the total number of days in defic
McCabe R, Schmit N, Christen P, et al., 2020, Adapting hospital capacity to meet changing demands during the COVID-19 pandemic, BMC Medicine, Vol: 18, Pages: 1-12, ISSN: 1741-7015
BackgroundTo calculate hospital surge capacity, achieved via hospital provision interventions implemented for the emergency treatment of coronavirus disease 2019 (COVID-19) and other patients through March to May 2020; to evaluate the conditions for admitting patients for elective surgery under varying admission levels of COVID-19 patients.MethodsWe analysed National Health Service (NHS) datasets and literature reviews to estimate hospital care capacity before the pandemic (pre-pandemic baseline) and to quantify the impact of interventions (cancellation of elective surgery, field hospitals, use of private hospitals, deployment of former medical staff and deployment of newly qualified medical staff) for treatment of adult COVID-19 patients, focusing on general and acute (G&A) and critical care (CC) beds, staff and ventilators.ResultsNHS England would not have had sufficient capacity to treat all COVID-19 and other patients in March and April 2020 without the hospital provision interventions, which alleviated significant shortfalls in CC nurses, CC and G&A beds and CC junior doctors. All elective surgery can be conducted at normal pre-pandemic levels provided the other interventions are sustained, but only if the daily number of COVID-19 patients occupying CC beds is not greater than 1550 in the whole of England. If the other interventions are not maintained, then elective surgery can only be conducted if the number of COVID-19 patients occupying CC beds is not greater than 320. However, there is greater national capacity to treat G&A patients: without interventions, it takes almost 10,000 G&A COVID-19 patients before any G&A elective patients would be unable to be accommodated.ConclusionsUnless COVID-19 hospitalisations drop to low levels, there is a continued need to enhance critical care capacity in England with field hospitals, use of private hospitals or deployment of former and newly qualified medical staff to allow some or all elective surge
Fu H, Xi X, Wang H, et al., 2020, Report 30: The COVID-19 epidemic trends and control measures in mainland China
McCabe R, Schmit N, Christen P, et al., 2020, Report 27 Adapting hospital capacity to meet changing demands during the COVID-19 pandemic
To meet the growing demand for hospital care due to the COVID-19 pandemic, England implemented a range of hospital provision interventions including the procurement of equipment, the establishment of additional hospital facilities and the redeployment of staff and other resources. Additionally, to further release capacity across England’s National Health Service (NHS), elective surgery was cancelled in March 2020, leading to a backlog of patients requiring care. This created a pressure on the NHS to reintroduce elective procedures, which urgently needs to be addressed. Population-level measures implemented in March and April 2020 reduced transmission of SARS-CoV-2, prompting a gradual decline in the demand for hospital care by COVID-19 patients after the peak in mid-April. Planning capacity to bring back routine procedures for non-COVID-19 patients whilst maintaining the ability to respond to any potential future increases in demand for COVID-19 care is the challenge currently faced by healthcare planners.In this report, we aim to calculate hospital capacity for emergency treatment of COVID-19 and other patients during the pandemic surge in April and May 2020; to evaluate the increase in capacity achieved via five interventions (cancellation of elective surgery, field hospitals, use of private hospitals, and deployment of former and newly qualified medical staff); and to determine how to re-introduce elective surgery considering continued demand from COVID-19 patients. We do this by modelling the supply of acute NHS hospital care, considering different capacity scenarios, namely capacity before the pandemic (baseline scenario) and after the implementation of capacity expansion interventions that impact available general and acute (G&A) and critical care (CC) beds, staff and ventilators. Demand for hospital care is accounted for in terms of non-COVID-19 and COVID-19 patients. Our results suggest that NHS England would not have had sufficient daily capacity
Christen P, D'Aeth J, Lochen A, et al., 2020, Report 15: Strengthening hospital capacity for the COVID-19 pandemic
Planning for extreme surges in demand for hospital care of patients requiring urgent life-saving treatment for COVID-19, and other conditions, is one of the most challenging tasks facing healthcare commissioners and care providers during the pandemic. Due to uncertainty in expected patient numbers requiring care, as well as evolving needs day by day, planning hospital capacity is challenging. Health systems that are well prepared for the pandemic can better cope with large and sudden changes in demand by implementing strategies to ensure adequate access to care. Thereby the burden of the pandemic can be mitigated, and many lives saved. This report presents the J-IDEA pandemic planner, a hospital planning tool to calculate how much capacity in terms of beds, staff and ventilators is obtained by implementing healthcare provision interventions affecting the management of patient care in hospitals. We show how to assess baseline capacity, and then calculate how much capacity is gained by various healthcare interventions using impact estimates that are generated as part of this study. Interventions are informed by a rapid review of policy decisions implemented or being considered in 12 European countries over the past few months , an evaluation of the impact of the interventions on capacity using a variety of research methods, and by a review of key parameters in the care of COVID-19 patients.The J-IDEA planner is publicly available, interactive and adaptable to different and changing circumstances and newly emerging evidence. The planner estimates the additional number of beds, medical staff and crucial medical equipment obtained under various healthcare interventions using flexible inputs on assumptions of existing capacities, the number of hospitalisations, beds-to-staff ratios, and staff absences due to COVID-19. A detailed user guide accompanies the planner. The planner was developed rapidly and has limitations which we will address in future iterations. It support
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.