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IntroductionIn 2020, the UK Health Security Agency (UKHSA) established a large-scale testing programme to rapidly identify individuals in England who were infected with SARS-CoV-2 and had COVID-19. This comprised part of the UK governments COVID-19 response strategy, to protect those at risk of severe COVID-19 disease and death and to reduce the burden on the health system. To assess the success of this approach, UKHSA commissioned an independent evaluation of the activities delivered by the NHS testing programme in England. The primary purpose of this evaluation is to capture key learnings from the rollout of testing to different target populations via various testing services between October 2020 and March 2022 and to use these insights to formulate recommendations for future pandemic preparedness strategy. Methods and analysisThe proposed study involves a stepwise mixed-methods approach, aligned with established methods for the evaluation of complex interventions in health, with retrospective and prospective components. A bottom-up approach will be taken, focusing on each of nine population-specific service settings. We will use a Theory of Change to understand the causal pathways and intended and unintended outcomes of each service, also exploring the effect of context on each individual service settings intended outcomes. Subsequently, the insights gained will be synthesised to identify process and outcome indicators to evaluate how the combined aims of the testing programme were achieved. A forward-looking, prospective component of this work will aim to inform testing strategy in preparation for future pandemics, through a participatory modelling simulation and policy analysis exercise. DisclaimerThis is a provisional draft protocol that represents research in progress. This research was commissioned and funded by UKHSA, to be performed between August 2022 and March 2023. The scope and depth of testing services and channels covered by this research were pre-agreed with UKHSA and are limited to the availability and provision of data available at the time this protocol was written. Ethics and disseminationFindings arising from this evaluation will be used to inform lessons learnt and recommendations for UKHSA on appropriate pandemic preparedness testing programme designs; findings will also be disseminated in peer-reviewed journals and at academic conferences. Strengths and limitations of the studyO_LIStrengths of this mixed-methods evaluation protocol include the use of theory-based, complex evaluation approaches and an iterative and participatory approach with the stakeholder (UKHSA) to the evaluation process and prospective modelling. C_LIO_LIGiven the scale and complexity of the COVID-19 testing response in England, there is a scarcity of previous relevant research into this, either in England or appropriate international comparators, warranting the mixed-methods evaluation approach we are adopting. C_LIO_LIThis is the first national-scale evaluation of the testing response to COVID-19 in England to incorporate most service settings, a programme which formed an integral part of the UK pandemic response strategy. The approach proposed could be applied to the evaluation of pandemic responses in other contexts or to other types of interventions. C_LIO_LIWhereas most complex interventions are accompanied by a prospective evaluation design initiated at the time of the intervention or earlier, this study predominantly comprises a retrospective evaluation and is therefore limited by the quality of existing research and the data available to the research team at the time of conducting the evaluation within the specified period allocated by UKHSA. C_LI
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Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population.
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IntroductionA discussion of waves of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous. MethodsWe present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as observed waves. This provides an objective means of describing observed waves in time series. ResultsThe output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of non-pharmaceutical interventions correlates with a reduced number of observed waves and reduced mortality burden in those waves. ConclusionIt is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.
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ObjectivesIn December 2020, an unprecedented vaccination programme to deal with the COVID-19 pandemic was initiated worldwide. However, the vaccine provision is currently insufficient for most countries to vaccinate their entire eligible population, so it is essential to develop the most efficient vaccination strategies. COVID-19 disease severity and mortality vary by age, therefore age-dependent vaccination strategies must be developed. Study design/MethodsHere, we use an age-dependent SIERS (susceptible-infected-exposed-recovered-susceptible) deterministic model to compare four hypothetical age-dependent vaccination strategies and their potential impact on the COVID-19 epidemic in Kyrgyzstan. ResultsOver the short-term (until March 2022), a vaccination rollout strategy focussed on high-risk groups (aged >50 years) with some vaccination among high-incidence groups (aged 20-49 years) may decrease symptomatic cases and COVID-19-attributable deaths. However, there will be limited impact on the estimated overall number of COVID-19 cases with the relatively low coverage of high-incidence groups (15-25% based on current vaccine availability). Vaccination plus non-pharmaceutical interventions (NPIs), such as mask wearing and social distancing, will further decrease COVID-19 incidence and mortality and may have an indirect impact on all-cause mortality. ConclusionsOur results and other evidence suggest that vaccination is most effective in flattening the epidemic curve and reducing mortality if supported by NPIs. In the short-term, focussing on high-risk groups may reduce the burden on the health system and result in fewer deaths. However, the herd effect from delaying another peak may only be achieved by greater vaccination coverage in high-incidence groups.
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Since the emergence of the novel coronavirus disease, mathematical modelling has become an important tool for planning strategies to combat the pandemic by supporting decision-making and public policies, as well as allowing an assessment of the effect of different intervention scenarios. A proliferation of compartmental models was observed in the mathematical modelling community, aiming to understand and make predictions regarding the spread of COVID-19. Such approach has its own advantages and challenges: while compartmental models are suitable to simulate large populations, the underlying well-mixed population assumption might be problematic when considering non-pharmaceutical interventions (NPIs) which strongly affect the connectivity between individuals in the population. Here we propose a correction to an extended age-structured SEIR framework with dynamic transmission modelled using contact matrices for different settings in Brazil. By assuming that the mitigation strategies for COVID-19 affect the connections between different households, network percolation theory predicts that the connectivity across all households decreases drastically above a certain threshold of removed connections. We incorporated this emergent effect at population level by modulating the home contact matrices through a percolation correction function, with the few remaining parameters fitted to to hospitalisation and mortality data from the city of Sao Paulo. We found significant support for the model with implemented percolation effect using the Akaike Information Criteria (AIC). Besides better agreement to data, this improvement also allows for a more reliable assessment of the impact of NPIs on the epidemiological dynamics.
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Accurate knowledge of accurate levels of prior population exposure has critical ramifications for preparedness plans of subsequent SARS-CoV-2 epidemic waves and vaccine prioritization strategies. Serological studies can be used to estimate levels of past exposure and thus position populations in their epidemic timeline. To circumvent biases introduced by decaying antibody titers over time, population exposure estimation methods should account for seroreversion, to reflect that changes in seroprevalence measures over time are the net effect of increases due to recent transmission and decreases due to antibody waning. Here, we present a new method that combines multiple datasets (serology, mortality, and virus positivity ratios) to estimate seroreversion time and infection fatality ratios and simultaneously infer population exposure levels. The results indicate that the average time to seroreversion is six months, and that true exposure may be more than double the current seroprevalence levels reported for several regions of England.
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Kyrgyzstan was placed under a two-month, nationwide lockdown due to the COVID-19 epidemic, starting on March 25, 2020. Given the highly disruptive effects of the lockdown on the national economy and peoples lives, the government decided not to extend lockdown beyond the initially planned date of May 10, 2020. The strategy chosen by the government was close to the input parameters of our models baseline scenario, full lockdown release, which we presented to policymakers in April 2020, along with various other hypothetical scenarios with managed lockdown release options. To explore whether our model could accurately predict the actual course of the epidemic following the release of lockdown, we compared the outputs of the baseline scenario, such as new cases, deaths, and demand for and occupancy of hospital beds, with actual official reports. Our analysis revealed that the model could accurately predict the timing of the epidemic peak, with a difference of just two weeks, although the magnitude of the peak was overestimated compared with the official statistics. However, it is important to note that the accuracy of the official reports remains debatable, so outputs relating to the size of the epidemic and related pressures on the health system will need to be updated if new evidence becomes available.
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Dexamethasone has been shown to reduce mortality in hospitalised COVID-19 patients needing oxygen and ventilation by 18% and 36%, respectively. Here, we estimate the potential number of lives saved and life years gained if this treatment would be rolled out in the UK and globally, as well as its cost-effectiveness of implementing this intervention. We estimate that, for the UK, approximately 12,000 [4,250 - 27,000] lives could be saved by January 2021. Assuming that dexamethasone has a similar effect size in settings where access to oxygen therapies is limited, this would translate into approximately 650,000 [240,000 - 1,400,000] lives saved globally. If dexamethasone acts differently in these settings, the impact could be less than half of this value. To estimate the full potential of dexamethasone in the global fight against COVID-19, it is essential to perform clinical research in settings with limited access to oxygen and/or ventilators, e.g. in low and middle-income countries.
