The aim of this study was to explore the frequency and distribution of gene mutations that are related to isoniazid (INH) and rifampin (RIF)-resistance in the strains of the multidrug-resistant tuberculosis (MDR-TB) Mycobacterium tuberculosis (M.tb) in Beijing, China. In this retrospective study, the genotypes of 173 MDR-TB strains were analysed by spoligotyping. The katG, inhA genes and the promoter region of inhA, in which genetic mutations confer INH resistance; and the rpoB gene, in which genetic mutations confer RIF resistance, were sequenced. The percentage of resistance-associated nucleotide alterations among the strains of different genotypes was also analysed. In total, 90.8% (157/173) of the MDR strains belonged to the Beijing genotype. Population characteristics were not significantly different among the strains of different genotypes. In total, 50.3% (87/173) strains had mutations at codon S315T of katG; 16.8% (29/173) of strains had mutations in the inhA promoter region; of them, 5.5% (15/173) had point mutations at −15 base (C→T) of the inhA promoter region. In total, 86.7% (150/173) strains had mutations at rpoB gene; of them, 40% (69/173) strains had mutations at codon S531L of rpoB. The frequency of mutations was not significantly higher in Beijing genotypic MDR strains than in non-Beijing genotypes. Beijing genotypic MDR-TB strains were spreading in Beijing and present a major challenge to TB control in this region. A high prevalence of katG Ser315Thr, inhA promoter region (−15C→T) and rpoB (S531L) mutations was observed. Molecular diagnostics based on gene mutations was a useful method for rapid detection of MDR-TB in Beijing, China.

This article derives a closed-form pricing formula for the European exchange option in a stochastic volatility framework. Firstly, with the Feynman–Kac theorem's application, we obtain a relation between the price of the European exchange option and a European vanilla call option with unit strike price under a doubly stochastic volatility model. Then, we obtain the closed-form solution for the vanilla option using the characteristic function. A key distinguishing feature of the proposed simplified approach is that it does not require a change of numeraire in contrast with the usual methods to price exchange options. Finally, through numerical experiments, the accuracy of the newly derived formula is verified by comparing with the results obtained using Monte Carlo simulations.

This study aims to locate the knots of cumulative coronavirus disease 2019 (COVID-19) case number during the first-level response to public health emergency in the provinces of China except Hubei. The provinces were grouped into three regions, namely eastern, central and western provinces, and the trends between adjacent knots were compared among the three regions. COVID-19 case number, migration scale index, Baidu index, demographic, economic and public health resource data were collected from 22 Chinese provinces from 19 January 2020 to 12 March 2020. Spline regression was applied to the data of all included, eastern, central and western provinces. The research period was divided into three stages by two knots. The first stage (from 19 January to around 25 January) was similar among three regions. However, in the second stage, growth of COVID-19 case number was flatter and lasted longer in western provinces (from 25 January to 18 February) than in eastern and central provinces (from 26 February to around 11 February). In the third stage, the growth of COVID-19 case number slowed down in all the three regions. Included covariates were different among the three regions. Overall, spline regression with covariates showed the different change patterns in eastern, central and western provinces, which provided a better insight into regional characteristics of COVID-19 pandemic.

This paper establishes a new version of integration by parts formula of Markov chains for sensitivity computation, under much lower restrictions than the existing researches. Our approach is more fundamental and applicable without using Girsanov theorem or Malliavin calculus as did by past papers. Numerically, we apply this formula to compute sensitivity regarding the transition rate matrix and compare with a recent research by an IPA (infinitesimal perturbation analysis) method and other approaches.

Hypertension represents one of the most common pre-existing conditions and comorbidities in Coronavirus disease 2019 (COVID-19) patients. To explore whether hypertension serves as a risk factor for disease severity, a multi-centre, retrospective study was conducted in COVID-19 patients. A total of 498 consecutively hospitalised patients with lab-confirmed COVID-19 in China were enrolled in this cohort. Using logistic regression, we assessed the association between hypertension and the likelihood of severe illness with adjustment for confounders. We observed that more than 16% of the enrolled patients exhibited pre-existing hypertension on admission. More severe COVID-19 cases occurred in individuals with hypertension than those without hypertension (21% vs. 10%, P = 0.007). Hypertension associated with the increased risk of severe illness, which was not modified by other demographic factors, such as age, sex, hospital geological location and blood pressure levels on admission. More attention and treatment should be offered to patients with underlying hypertension, who usually are older, have more comorbidities and more susceptible to cardiac complications.

The coronavirus disease 2019 (COVID-19) epidemic is spreading globally. Studies revealed that obesity may affect the progression and prognosis of COVID-19 patients. The aim of the meta-analysis is to identify the prevalence and impact of obesity on COVID-19. Studies on obese COVID-19 patients were obtained by searching PubMed, Cochrane Library databases and Web of Science databases, up to date to 5 June 2020. And the prevalence rate and the odds ratio (OR) of obesity with 95% confidence interval (CI) were used as comprehensive indicators for analysis using a random-effects model. A total of 6081 patients in 11 studies were included. The prevalence of obesity in patients with COVID-19 was 30% (95% CI 21–39%). Obese patients were 1.79 times more likely to develop severe COVID-19 than non-obese patients (OR 1.79, 95% CI 1.52–2.11, P < 0.0001, I2 = 0%). However obesity was not associated with death in COVID-19 patients (OR 1.05, 95% CI 0.65–1.71, P = 0.84, I2 = 66.6%). In dose−response analysis, it was estimated that COVID-19 patients had a 16% increased risk of invasive mechanical ventilation (OR 1.16, 95% CI 1.10–1.23, P < 0.0001) and a 20% increased risk of admission to ICU (OR 1.20, 95% CI 1.11–1.30, P < 0.0001) per 5 kg/m2 increase in BMI. In conclusion, obesity in COVID-19 patients is associated with severity, but not mortality.

A pooled sample analysis strategy for novel coronavirus (severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)) is proposed for a large population in this paper. The population to be tested is divided into divisions based on earlier observed detection rate of SARS-CoV-2 first. Samples collected are then grouped in appropriate pooled size. The number of tests per person in that population is expressed as a function of two variables: the observed detection rate and the pooled size or number of samples grouped. The minimum number of tests per person can be further shown to be a function of only one of these two variables, because these two parameters are found to be related at this minimum. A management scheme on grouping the samples is proposed in order to reduce the number of tests, to save time, which is of utmost importance in fighting an epidemic. The proposed testing scheme will be useful for supporting the government in making decisions to handle regular routine detection tests for identifying asymptomatic patients and implementing health code system in large population of millions of citizens. Another important point is to use smaller number of test kits, allowing more resources to speed up the mass screening tests, particularly in places not so rich.

In this paper, we discuss the problem of pricing discretely sampled variance swaps under a hybrid stochastic model. Our modeling framework is a combination with a double Heston stochastic volatility model and a Cox–Ingersoll–Ross stochastic interest rate process. Due to the application of the T-forward measure with the stochastic interest process, we can only obtain an efficient semi-closed form of pricing formula for variance swaps instead of a closed-form solution based on the derivation of characteristic functions. The practicality of this hybrid model is demonstrated by numerical simulations.

This paper deals with the multivariate tail conditional expectation (MTCE) for generalized skew-elliptical distributions. We present tail conditional expectation for univariate generalized skew-elliptical distributions and MTCE for generalized skew-elliptical distributions. There are many special cases for generalized skew-elliptical distributions, such as generalized skew-normal, generalized skew Student-t, generalized skew-logistic and generalized skew-Laplace distributions.

As characterization and modeling of complex materials by phenomenological models remains challenging, data-driven computing that performs physical simulations directly from material data has attracted considerable attention. Data-driven computing is a general computational mechanics framework that consists of a physical solver and a material solver, based on which data-driven solutions are obtained through minimization procedures. This work develops a new material solver built upon the local convexity-preserving reconstruction scheme by He and Chen (2020) A physics-constrained data-driven approach based on locally convex reconstruction for noisy database. Computer Methods in Applied Mechanics and Engineering 363, 112791 to model anisotropic nonlinear elastic solids. In this approach, a two-level local data search algorithm for material anisotropy is introduced into the material solver in online data-driven computing. A material anisotropic state characterizing the underlying material orientation is used for the manifold learning projection in the material solver. The performance of the proposed data-driven framework with noiseless and noisy material data is validated by solving two benchmark problems with synthetic material data. The data-driven solutions are compared with the constitutive model-based reference solutions to demonstrate the effectiveness of the proposed methods.

Anomaly detection in asset condition data is critical for reliable industrial asset operations. But statistical anomaly classifiers require certain amount of normal operations training data before acceptable accuracy can be achieved. The necessary training data are often not available in the early periods of assets operations. This problem is addressed in this paper using a hierarchical model for the asset fleet that systematically identifies similar assets, and enables collaborative learning within the clusters of similar assets. The general behavior of the similar assets are represented using higher level models, from which the parameters are sampled describing the individual asset operations. Hierarchical models enable the individuals from a population, comprising of statistically coherent subpopulations, to collaboratively learn from one another. Results obtained with the hierarchical model show a marked improvement in anomaly detection for assets having low amount of data, compared to independent modeling or having a model common to the entire fleet.

This paper presents the development process of a digital twin of a unique hydroponic underground farm in London, Growing Underground (GU). Growing 12x more per unit area than traditional greenhouse farming in the UK, the farm also consumes 4x more energy per unit area. Key to the ongoing operational success of this farm and similar enterprises is finding ways to minimize the energy use while maximizing crop growth by maintaining optimal growing conditions. As such, it belongs to the class of Controlled Environment Agriculture, where indoor environments are carefully controlled to maximize crop growth by using artificial lighting and smart heating, ventilation, and air conditioning systems. We tracked changing environmental conditions and crop growth across 89 different variables, through a wireless sensor network and unstructured manual records, and combined all the data into a database. We show how the digital twin can provide enhanced outputs for a bespoke site like GU, by creating inferred data fields, and show the limitations of data collection in a commercial environment. For example, we find that lighting is the dominant environmental factor for temperature and thus crop growth in this farm, and that the effects of external temperature and ventilation are confounded. We combine information learned from historical data interpretation to create a bespoke temperature forecasting model (root mean squared error < 1.3°C), using a dynamic linear model with a data-centric lighting component. Finally, we present how the forecasting model can be integrated into the digital twin to provide feedback to the farmers for decision-making assistance.

Using monthly data from the Ebola-outbreak 2013–2016 in West Africa, we compared two calibrations for data fitting, least-squares (SSE) and weighted least-squares (SWSE) with weights reciprocal to the number of new infections. To compare (in hindsight) forecasts for the final disease size (the actual value was observed at month 28 of the outbreak) we fitted Bertalanffy–Pütter growth models to truncated initial data (first 11, 12, …, 28 months). The growth curves identified the epidemic peak at month 10 and the relative errors of the forecasts (asymptotic limits) were below 10%, if 16 or more month were used; for SWSE the relative errors were smaller than for SSE. However, the calibrations differed insofar as for SWSE there were good fitting models that forecasted reasonable upper and lower bounds, while SSE was biased, as the forecasts of good fitting models systematically underestimated the final disease size. Furthermore, for SSE the normal distribution hypothesis of the fit residuals was refuted, while the similar hypothesis for SWSE was not refuted. We therefore recommend considering SWSE for epidemic forecasts.

Previous studies have revealed associations of meteorological factors with tuberculosis (TB) cases. However, few studies have examined their lag effects on TB cases. This study was aimed to analyse nonlinear lag effects of meteorological factors on the number of TB notifications in Hong Kong. Using a 22-year consecutive surveillance data in Hong Kong, we examined the association of monthly average temperature and relative humidity with temporal dynamics of the monthly number of TB notifications using a distributed lag nonlinear models combined with a Poisson regression. The relative risks (RRs) of TB notifications were >1.15 as monthly average temperatures were between 16.3 and 17.3 °C at lagged 13–15 months, reaching the peak risk of 1.18 (95% confidence interval (CI) 1.02–1.35) when it was 16.8 °C at lagged 14 months. The RRs of TB notifications were >1.05 as relative humidities of 60.0–63.6% at lagged 9–11 months expanded to 68.0–71.0% at lagged 12–17 months, reaching the highest risk of 1.06 (95% CI 1.01–1.11) when it was 69.0% at lagged 13 months. The nonlinear and delayed effects of average temperature and relative humidity on TB epidemic were identified, which may provide a practical reference for improving the TB warning system.

Although testing is widely regarded as critical to fighting the COVID-19 pandemic, what measure and level of testing best reflects successful infection control remains unresolved. Our aim was to compare the sensitivity of two testing metrics – population testing number and testing coverage – to population mortality outcomes and identify a benchmark for testing adequacy. We aggregated publicly available data through 12 April on testing and outcomes related to COVID-19 across 36 OECD (Organization for Economic Development) countries and Taiwan. Spearman correlation coefficients were calculated between the aforementioned metrics and following outcome measures: deaths per 1 million people, case fatality rate and case proportion of critical illness. Fractional polynomials were used to generate scatter plots to model the relationship between the testing metrics and outcomes. We found that testing coverage, but not population testing number, was highly correlated with population mortality (rs = −0.79, P = 5.975 × 10−9 vs. rs = −0.3, P = 0.05) and case fatality rate (rs = −0.67, P = 9.067 × 10−6 vs. rs = −0.21, P = 0.20). A testing coverage threshold of 15–45 signified adequate testing: below 15, testing coverage was associated with exponentially increasing population mortality; above 45, increased testing did not yield significant incremental mortality benefit. Taken together, testing coverage was better than population testing number in explaining country performance and can serve as an early and sensitive indicator of testing adequacy and disease burden.

Amplifying the testing capacity and making better use of testing resources is a crucial measure when fighting any pandemic. A pooled testing strategy for SARS-CoV-2 has theoretically been shown to increase the testing capacity of a country, especially when applied in low prevalence settings. Experimental studies have shown that the sensitivity of reverse transcription-polymerase chain reaction is not affected when implemented in small groups. Previous models estimated the optimum group size as a function of the historical prevalence; however, this implies a homogeneous distribution of the disease within the population. This study aimed to explore whether separating individuals by age groups when pooling samples results in any further savings on test kits or affects the optimum group size estimation compared to Dorfman's pooling, based on historical prevalence. For this evaluation, age groups of interest were defined as 0–19 years, 20–59 years and over 60 years old. Generalisation of Dorfman's pooling was performed by adding statistical weight to the age groups based on the number of confirmed cases and tests performed in the segment. The findings showed that when the pooling samples are based on age groups, there is a decrease in the number of tests per subject needed to diagnose one subject. Although this decrease is minuscule, it might account for considerable savings when applied on a large scale. In addition, the savings are considerably higher in settings where there is a high standard deviation among the positivity rate of the age segments of the general population.

Tuberculosis (TB) remains a global public health threat. Misdiagnosis and delayed therapy of sputum smear-negative TB can affect the treatment outcomes and promote pathogen transmission. The application of Xpert MTB/RIF assay in bronchoalveolar lavage fluid (BALF) has been recommended but needs clinical evidence. We carried out a prospective study in the Nanjing Public Health Medical Center from September 2018 to August 2019. Pulmonary tuberculosis (PTB) patients were enrolled in the study if they had negative results of sputum smear. We compared the performance of Xpert MTB/RIF assay in sputum and BALF using sputum culture as the reference. In addition to this, we applied parallel tests using sputum culture, sputum-based Xpert MTB/RIF assay and BALF-based Xpert MTB/RIF assay to jointly detect smear-negative PTB using clinical diagnosis as the reference. With mycobacterial culture as the reference standard, Xpert MTB/RIF of BALF showed a higher sensitivity (14/16, 87.5%), but a relatively lower specificity (57/92, 62.0%). Xpert MTB/RIF of sputum showed relatively lower sensitivity (6/10, 60.0%) and higher specificity (63/88, 71.6%). Compared with sputum culture, Xpert MTB /RIF assay reduced the median detection time of MTB from 30 to 0 days, which significantly shortened the diagnosis time of the smear-negative TB patients. Among the combined detections, the positive detection proportion was improved with significant differences comparing with sputum culture only, from 11.1% (10/90) to 46.7% (42/90) (P < 0.05). Our study showed Xpert MTB/RIF in BALF had a better performance in detecting MTB of smear-negative patients.

With the rapid rise in the prevalence of non-tuberculous mycobacteria (NTM) diseases across the world, the microbiological diagnosis of NTM isolates is becoming increasingly important for the diagnosis and treatment of NTM disease. In this study, the clinical presentation, species distribution and drug susceptibility of patients with NTM disease visiting the Chongqing Public Health Medical Centre during March 2016–April 2019 were retrospectively analysed. Among the 146 patients with NTM disease, eight NTM species (complex) were identified. The predominant NTM species in these patients were identified to be Mycobacterium abscessus complex (53, 36.3%), M. intracellulare (38, 26%) and M. fortuitum (17, 11.7%). In addition, two or more species were isolated from 7.5% of the patients. Pulmonary NTM disease (142, 97.3%) showed the highest prevalence among the patients. It was observed that 40.1% of the patients with pulmonary NTM disease had chronic pulmonary obstructive disease and bronchiectasis, while 22.5% had prior tuberculosis. Male patients showed more association with the conditions of cough and haemoptysis than the female patients. In an in vitro antimicrobial susceptibility testing, most of the species showed susceptibility to linezolid, amikacin and clarithromycin, while M. fortuitum exhibited low susceptibility to tobramycin. In conclusion, the prevalence of NTM disease, especially that of the pulmonary NTM disease, is common in Southwest China. Species identification and drug susceptibility testing are thus extremely important to ensure appropriate treatment regimens for patient care and management.