No abstract

BackgroundEngland’s COVID-19 “roadmap out of lockdown” set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued. Here we assess the roadmap, the impact of the Delta variant, and potential future epidemic trajectories.MethodsWe extended a 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 using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs.FindingsThe roadmap was successful in offsetting the increased transmission resulting from lifting NPIs with increasing population immunity through vaccination. However due to the emergence of Delta, with an estimated transmission advantage of 73% (95%CrI: 68-79) over Alpha, fully lifting NPIs on 21 June 2021 as originally planned may have led to 3,400 (95%CrI: 1,300-4,400) peak daily hospital admissions under our central parameter scenario. Delaying until 19 July reduced peak hospitalisations by three-fold to 1,400 (95%CrI: 700-1,500) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to estimates of vaccine effectiveness and the intrinsic transmissibility of Delta.InterpretationOur findings show that the risk of a large wave of COVID hospitalisations resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with Delta, it may not be possible to fully lift NPIs without a third wave of hospitalisations 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.FundingNational Institute for Health Research, UK Medical Research Council, Wellcome Trust, UK Foreign, Commonwealth & Development Office.Research in contextEvidence before this studyWe searched PubMed up to 23 July 2021 with no language restrictions using the search terms: (COVID-19 or SARS-CoV-2 or 2019-nCoV or “novel coronavirus”) AND (vaccine or vaccination) AND (“non pharmaceutical interventions” OR “non-pharmaceutical interventions) AND (model*). We found nine studies that analysed the relaxation of controls with vaccination roll-out. However, none explicitly analysed real-world evidence balancing lifting of interventions, vaccination, and emergence of the Delta variant.Added value of this studyOur data synthesis approach combines real-world evidence from multiple data sources to retrospectively evaluate how relaxation of COVID-19 measures have been balanced with vaccination roll-out. We explicitly capture the emergence of the Delta variant, its transmissibility over Alpha, and quantify its impact on the roadmap. We show the benefits of maintaining NPIs whilst vaccine coverage continues to increase and capture key uncertainties in the epidemic trajectory after NPIs are lifted.Implications of all the available evidenceOur study shows that lifting interventions must be balanced carefully and cautiously with vaccine roll-out. In the presence of a new, highly transmissible variant, vaccination alone may not be enough to control COVID-19. Careful monitoring of vaccine uptake, effectiveness, variants, and changes in contact patterns as restrictions are lifted will be critical in any exit strategy.

This paper presents a novel approach to detect abnormalities in dynamic systems based on multisensory data and feature selection. The proposed method produces multiple inference models by considering several features of the observed data. This work facilitates the obtainment of the most precise features for predicting future instances and detecting abnormalities. Growing neural gas (GNG) is employed for clustering multisensory data into a set of nodes that provide a semantic interpretation of data and define local linear models for prediction purposes. Our method uses a Markov Jump particle filter (MJPF) for state estimation and abnormality detection. The proposed method can be used for selecting the optimal set features to be shared in networking operations such that state prediction, decision-making, and abnormality detection processes are favored. This work is evaluated by using a real dataset consisting of a moving vehicle performing some tasks in a controlled environment.

No abstract

As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.1 (95% credible interval (CrI) 6.8-9.3). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of protection in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.8%, 95% CrI 2.3-3.2), followed by Delta (2.0%, 95% CrI 1.5-2.4), Wildtype (1.2%, 95% CrI 1.0-1.3), and Omicron (0.6%, 95% CrI 0.4-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.