Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies

Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.     

Uncertainty quantification and sensitivity analysis of an arterial wall mechanics model for evaluation of vascular drug therapies

Quantification of the uncertainty in constitutive model predictions describing arterial wall mechanics is vital towards non-invasive assessment of vascular drug therapies. Therefore, we perform uncertainty quantification to determine uncertainty in mechanical characteristics describing the vessel wall response upon loading. Furthermore, a global variance-based sensitivity analysis is performed to pinpoint measurements that are most rewarding to be measured more precisely. We used previously published carotid diameter–pressure and intima–media thickness (IMT) data (measured in triplicate), and Holzapfel–Gasser–Ogden models. A virtual data set containing 5000 diastolic and systolic diameter–pressure points, and IMT values was generated by adding measurement error to the average of the measured data. The model was fitted to single-exponential curves calculated from the data, obtaining distributions of constitutive parameters and constituent load bearing parameters. Additionally, we (1) simulated vascular drug treatment to assess the relevance of model uncertainty and (2) evaluated how increasing the number of measurement repetitions influences model uncertainty. We found substantial uncertainty in constitutive parameters. Simulating vascular drug treatment predicted a 6% point reduction in collagen load bearing (\(L_\mathrm {coll}\)), approximately 50% of its uncertainty. Sensitivity analysis indicated that the uncertainty in \(L_{\mathrm {coll}}\) was primarily caused by noise in distension and IMT measurements. Spread in \(L_{\mathrm {coll}}\) could be decreased by 50% when increasing the number of measurement repetitions from 3 to 10. Model uncertainty, notably that in \(L_{\mathrm {coll}}\), could conceal effects of vascular drug therapy. However, this uncertainty could be reduced by increasing the number of measurement repetitions of distension and wall thickness measurements used for model parameterisation.     

An individual-based model of hepatitis A transmission

Viral hepatitis A, as other endemic diseases, represents a public health priority worldwide. To study long-time scale human pathogens through individual-based simulations, the development of a dynamic network of contacts is required. In this work, we introduce an individual-based model accounting for the birth and death of the individuals, the generation of new households, and the educational career of the individuals, in order to investigate viral hepatitis A dynamics in the most affected Italian areas. Intervention options such as targeted vaccination, social distancing measures (e.g., closure of day care centers and kindergartens) and improvements in standards of living and hygiene are evaluated. Results show that a very low vaccination coverage is sufficient to control hepatitis A in Italy, while its elimination is not possible since new cases are continuously imported from high endemicity areas outside the country. Finally, the considered social distancing measures can be counterproductive since the fraction of recovered individuals does not decline while the age at infection increases, thus augmenting the probability of developing acute symptoms.     

Microsimulation based quantitative analysis of COVID-19 management strategies

Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation framework PanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.     

Evolving the selfish herd: emergence of distinct aggregating strategies in an individual-based model

From zebra to starlings, herring and even tadpoles, many creatures move in an organized group. The emergent behaviour arises from simple underlying movement rules, but the evolutionary pressure which favours these rules has not been conclusively identified. Various explanations exist for the advantage to the individual of group formation: reduction of predation risk; increased foraging efficiency or reproductive success. Here, we adopt an individual-based model for group formation and subject it to simulated predation and foraging; the haploid individuals evolve via a genetic algorithm based on their relative success under such pressure. Our work suggests that flock or herd formation is likely to be driven by predator avoidance. Individual fitness in the model is strongly dependent on the presence of other phenotypes, such that two distinct types of evolved group can be produced by the same predation or foraging conditions, each stable against individual mutation. We draw analogies with multiple Nash equilibria theory of iterated games to explain and categorize these behaviours. Our model is sufficient to capture the complex behaviour of dynamic collective groups, yet is flexible enough to manifest evolutionary behaviour.     

COVID-19 prevention and control strategies: learning from the Macau model

Background: Macau is a densely populated international tourist city. Compared to most tensely populated countries/territories, the prevalence and mortality of COVID-19 in Macau are lower. The experiences in Macau could be helpful for other areas to combat the COVID-19 pandemic. This article introduced the endeavours and achievements of Macau in combatting the COVID-19 pandemic.Method: Both qualitative and quantitative analysis methods were used to explore the work, measures, and achievements of Macau in dealing with the COVID-19 pandemic.Results: The results revealed that Macau has provided undifferentiated mask purchase reservation services, COVID-19 vaccination services to all residents and non-residents in Macau along with delivering multilingual services, in Chinese, English and Portuguese, to different groups of the population. To facilitate the travels of people, business and trades between Macau and mainland China, the Macau government launched the Macau Health Code System, which uses the health status declaration, residence history declaration, contact history declaration of the declarant to match various relevant backend databases within the health authority and provide a risk-related colour code operations. The Macau Health Code System connects to the Chinese mainland''s own propriety health code system seamlessly, whilst effectively protecting the privacy of the residents. Macau has also developed the COVID-19 Vaccination Appointment system, the Nucleic Acid Test Appointment system, the Port and Entry/Exit Quarantine system, the medical and other supporting systems.Conclusion: The efforts in Macau have achieved remarkable results in COVID-19 prevention and control, effectively safeguarding the lives and health of the people and manifesting the core principle of “serving the public”. The measures used are sustainable and can serve as an important reference for other countries/regions.     

Trait evolution in an individual-based model of herbaceous vegetation

Many theoretical studies of evolution are based upon the concepts of the evolutionary stable strategy and optimal life-history solutions. An individual based model of vegetation is used to simulate life-history evolution under two different sets of environmental conditions. At one level the results suggest that optimal life-history solutions do appear to evolve. At the end of the simulations the vegetation that evolved in a fertile and uncut environment was taller, thinner and germinated later than that which developed in a less fertile and cut habitat. However, between simulation variation was observed to be high, particularly for the parameter regulating the timing of reproduction, and it showed no indication of reaching fixation. When this trait was prevented from mutating, the variances of other traits were seen to increase. Although at the population level between simulation variation was high, some traits achieved a degree of stability within simulations, suggesting that multiple adaptive peaks may be being approached. However, there was little evidence of trait fixation occurring within the most abundant genotype. It is considered that frequency dependent selection/Red Queen dynamics may be acting to prevent the most abundant genotype from reaching fixation. It is argued that if such processes prevent optimal genetic solutions from being achieved then the search for evolutionary stable strategies within the evolution of life-histories may be over simplistic.     

Individual variability and population regulation: an individual-based model

To study the influence of individual variability on population dynamics an individual-based model of the dynamics of a single population consisting of different individuals is constructed. The model is based on differences in individual assimilation rates due to intraspecific competition and variability of initial weights. The model exhibits "imperfect regulation", i.e., the number of individuals in the population oscillates and sooner or later the population becomes extinct. When individual variability is included, the model produces longer population extinction times than without individual variability. The average extinction time is not however a monotonic function of the degree of individual variability.     

COVID-19 infection and transmission includes complex sequence diversity

SARS-CoV-2 whole genome sequencing has played an important role in documenting the emergence of polymorphisms in the viral genome and its continuing evolution during the COVID-19 pandemic. Here we present data from over 360 patients to characterize the complex sequence diversity of individual infections identified during multiple variant surges (e.g., Alpha and Delta). Across our survey, we observed significantly increasing SARS-CoV-2 sequence diversity during the pandemic and frequent occurrence of multiple biallelic sequence polymorphisms in all infections. This sequence polymorphism shows that SARS-CoV-2 infections are heterogeneous mixtures. Convention for reporting microbial pathogens guides investigators to report a majority consensus sequence. In our study, we found that this approach would under-report sequence variation in all samples tested. As we find that this sequence heterogeneity is efficiently transmitted from donors to recipients, our findings illustrate that infection complexity must be monitored and reported more completely to understand SARS-CoV-2 infection and transmission dynamics. Many of the nucleotide changes that would not be reported in a majority consensus sequence have now been observed as lineage defining SNPs in Omicron BA.1 and/or BA.2 variants. This suggests that minority alleles in earlier SARS-CoV-2 infections may play an important role in the continuing evolution of new variants of concern.     

Diagnosis-wide analysis of COVID-19 complications: an exposure-crossover study

BACKGROUND:Many studies reporting coronavirus disease 2019 (COVID-19) complications have involved case series or small cohorts that could not establish a causal association with COVID-19 or provide risk estimates in different care settings. We sought to study all possible complications of COVID-19 to confirm previously reported complications and to identify potential complications not yet known.METHODS:Using United States health claims data, we compared the frequency of all International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis codes occurring before and after the onset of the COVID-19 pandemic in an exposure-crossover design. We included patients who received a diagnosis of COVID-19 between Mar. 1, 2020, and Apr. 30, 2020, and computed risk estimates and odds ratios (ORs) of association with COVID-19 for every ICD-10-CM diagnosis code.RESULTS:Among 70 288 patients with COVID-19, 69 of 1724 analyzed ICD-10-CM diagnosis codes were significantly associated with COVID-19. Disorders showing both strong association with COVID-19 and high absolute risk included viral pneumonia (OR 177.63, 95% confidence interval [CI] 147.19–214.37, absolute risk 27.6%), respiratory failure (OR 11.36, 95% CI 10.74–12.02, absolute risk 22.6%), acute kidney failure (OR 3.50, 95% CI 3.34–3.68, absolute risk 11.8%) and sepsis (OR 4.23, 95% CI 4.01–4.46, absolute risk 10.4%). Disorders showing strong associations with COVID-19 but low absolute risk included myocarditis (OR 8.17, 95% CI 3.58–18.62, absolute risk 0.1%), disseminated intravascular coagulation (OR 11.83, 95% CI 5.26–26.62, absolute risk 0.1%) and pneumothorax (OR 3.38, 95% CI 2.68–4.26, absolute risk 0.4%).INTERPRETATION:We confirmed and provided risk estimates for numerous complications of COVID-19. These results may guide prognosis, treatment decisions and patient counselling.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel strain of coronavirus that has been identified as the cause of the coronavirus disease 2019 (COVID-19) pandemic. As of Nov. 20, 2020, more than 50 million people have received a diagnosis of COVID-19 globally.¹ The clinical spectrum of disease is wide and can range from symptoms typical of the common cold to respiratory failure and death.² Most patients have mild symptoms and can be managed as outpatients, but as many as 20% have a severe form of the disease requiring admission to hospital, commonly presenting with hypoxia secondary to pneumonia.³Studies also show that COVID-19 is associated with a wide variety of nonrespiratory sequelae, including endothelial, thrombotic, cardiac, inflammatory, neurologic and other complications. ^4–9 Whether these associations are causal is not well established, as many of these findings originate from case reports, which are prone to publication bias and cannot provide risk estimates, or from cohort studies that often do not provide relative risk estimates.An alternative strategy for identifying potential complications of COVID-19 is studying all possible complications as captured in International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10 CM) diagnosis codes, which allows for the discovery of unreported complications and can confirm previously identified ones. The objective of our study was to analyze all diagnoses associated with COVID-19, to identify those that could be complications of the disease and to present both the absolute risk and relative odds of any complications identified.     

新型冠状病毒肺炎细胞治疗的关键策略和研究进展

新型冠状病毒肺炎病毒感染性强,感染后的重型、危重型患者病死率高,尚无特效治疗方法。间充质干细胞具有免疫调节和组织修复功能,一方面可以通过分泌抑炎因子减少炎性因子表达,降低细胞因子风暴和急性呼吸窘迫综合征发生的风险,从而降低重症患者的死亡率;另一方面间充质干细胞可分泌营养因子且具有多向分化能力,能修复肺部组织损伤,阻止肺部纤维化进程并使其恢复,从而治疗病毒感染肺炎后引起的难治性肺损伤相关疾病。此外自然杀伤细胞等也可在病毒感染性疾病预防、减少轻症患者向重型患者转化等方面发挥作用。本文总结并分析了细胞治疗新型冠状病毒肺炎最新研究进展。     

COVID-19 transmission: economy-boosting investment should target innovation in pandemic containment strategies to minimize restrictions of civil liberties

Imposition of restrictions on civil liberties in response to epi/pandemic crises provokes collateral health, economic and social crises. Moreover, as a result of the societal distress engendered, they become less effective over time, reflected in reducing acceptability, public protests, lack of compliance and civil disobedience, as evidenced by current events in some countries. There is an urgent need to evolve new containment strategies that minimize civil liberty restrictions. This requires strategic economic policies to invest in what might be termed pandemic containment innovation, particularly in the development of new means of reducing virus concentrations in closed spaces, and of precision exclusion of virus transmitters from public assemblies. Such innovations and their implementation will in turn create significant employment and boost economies. And, because such investments aim at increasing the resilience of society, healthcare and the economy to pandemics (and indeed outbreaks of respiratory infections in general), they are particularly sustainable.     

Evolution of cell motility in an individual-based model of tumour growth

Tumour invasion is driven by proliferation and importantly migration into the surrounding tissue. Cancer cell motility is also critical in the formation of metastases and is therefore a fundamental issue in cancer research. In this paper we investigate the emergence of cancer cell motility in an evolving tumour population using an individual-based modelling approach. In this model of tumour growth each cell is equipped with a micro-environment response network that determines the behaviour or phenotype of the cell based on the local environment. The response network is modelled using a feed-forward neural network, which is subject to mutations when the cells divide. With this model we have investigated the impact of the micro-environment on the emergence of a motile invasive phenotype. The results show that when a motile phenotype emerges the dynamics of the model are radically changed and we observe faster growing tumours exhibiting diffuse morphologies. Further we observe that the emergence of a motile subclone can occur in a wide range of micro-environmental growth conditions. Iterated simulations showed that in identical growth conditions the evolutionary dynamics either converge to a proliferating or migratory phenotype, which suggests that the introduction of cell motility into the model changes the shape of fitness landscape on which the cancer cell population evolves and that it now contains several local maxima. This could have important implications for cancer treatments which focus on the gene level, as our results show that several distinct genotypes and critically distinct phenotypes can emerge and become dominant in the same micro-environment.     

Structural validation of an individual-based model for plague epidemics simulation

Plague remains endemic in many countries in the world and Madagascar is currently the country where the highest number of human plague cases is reported every year. The investigation of causal factors, which command the disease dynamics in rodent populations, is a crucial step to forecast, control and anticipate the infection extension to humans. This paper presents simulation results obtained from an epidemic model, SIMPEST, designed to simulate bubonic plague in a rodent population at a high level of spatial and temporal resolution. We developed a structurally realistic individual-based model, mobilizing knowledge about fleas and rats behaviour, inter-individual plague transmission, and disease evolution in individual organisms, so that the model reflects the way the real system operates and to generate spatial and temporal patterns of disease spread. To assess the structural validity of our simulations, we perform sensitivity analyses on the initial population size and spatial distribution, and compare our results with theoretical statements, garnered from both previous modelling experiences and repeated field observations. We show our results are consistent with referents about population size conditions for a disease to invade and persist and the effect of the contact network on disease dynamics.     

Density distribution and size sorting in fish schools: an individual-based model

In fish schools the density varies per location and often individualsare sorted according to familiarity and/or body size. High densityis considered advantageous for protection against predatorsand this sorting is believed to be advantageous not only toavoid predators but also for finding food. In this paper, welist a number of mechanisms and we study, with the help of anindividual-based model of schooling agents, which spatial patternsmay result from them. In our model, schooling is regulated bythe following rules: avoiding those that are close by, aligningto those at intermediate distances, and moving towards othersfurther off. Regarding kinship/familiarity, we study patternsthat come about when agents actively choose to be close to relatedagents (i.e., ‘active sorting’). Regarding bodysize, we study what happens when agents merely differ in sizebut behave according to the usual schooling rules (‘sizedifference model’), when agents choose to be close tothose of similar size, and when small agents avoid larger ones(‘risk avoidance’). Several spatial configurationsresult: during ‘active sorting’ familiar agentsgroup together anywhere in the shoal, but agents of differentsize group concentrically, whereby the small agents occupy thecenter and the large ones the periphery (‘size differencemodel’ and ‘active sorting’). If small agentsavoid the risk of being close to large ones, however, smallagents end up at the periphery and large ones occupy the center(‘risk avoidance’). Spatial configurations are alsoinfluenced by the composition of the group, namely the percentageof agents of each type. Furthermore, schools are usually oblongand their density is always greatest near the front. We explainthe way in which these patterns emerge and indicate how resultsof our model may guide the study of spatial patterns in realanimals.     

Qualitative analysis of an HIV transmission model.

In 1988, a multiple-group model for HIV transmission with preferred mixing was proposed by Jacquez and coworkers. In the present paper, the work done by Jacquez et al. is extended. It is shown that the stability modulus of the Jacobian matrix at the no-disease equilibrium is a threshold for this model. Furthermore, if the no-disease equilibrium is unstable, the number of infected individuals will remain above a certain positive level regardless of initial levels; that is, the disease will persist uniformly. The stability of the endemic equilibrium in the case of restricted mixing is also studied. A series of sufficient conditions for local and global asymptotic stability of the endemic equilibrium are stated.     

Diffusion-limited predator-prey dynamics in Euclidean environments: an allometric individual-based model

We claim that diffusion-limited rates of reaction can be an explanation for the altered population dynamics predicted by models incorporating local interactions and limited individual mobility. We show that the predictions of a spatially explicit, individual-based model result from reduced rates of predation and reproduction caused by limited individual mobility and patchiness. When these reduced rates are used in a mean-field model, there is better agreement with the predictions of the simulation model incorporating local interactions. We also explain previous findings regarding the effects of dimensionality on population dynamics in light of diffusion-limited reactions and Pólya random walks. In particular, we demonstrate that 3D systems are better "stirred" than 2D systems and consequently have a reduced tendency for diffusion-limited interaction rates.