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The development of Ibu Kota Nusantara, the new capital city of Indonesia, is one of the largest infrastructure projects in Southeast Asia. Targeted surveillance and adaptive control measures are needed to mitigate the risks of malaria and other infectious diseases. Indonesia is developing a new capital city, Ibu Koata Nusantara, which is located in a malaria and biodiversity hotspot. In this Comment, the authors outline some of the potential infectious disease-related risks of the new project – primarily malaria and other vector-borne diseases. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has caused over 7.02 million deaths as of January 2024 and profoundly affected most countries' Gross Domestic Product (GDP). Here, we study the interaction of SARS-CoV-2 transmission, mortality, and economic output between January 2020 and December 2022 across 25 European countries. Methods: We use a Bayesian mixed effects model with auto-regressive terms to estimate the temporal relationships between disease transmission, excess deaths, changes in economic output, transit mobility and non-pharmaceutical interventions (NPIs) across countries. Results: Disease transmission intensity (logRt) decreases GDP and increases excess deaths, where the latter association is longer-lasting. Changes in GDP as well as prior week transmission intensity are both negatively associated with each other (-0.241, 95% CrI: -0.295 - -0.189). We find evidence of risk-averse behaviour, as changes in transit and prior week transmission intensity are negatively associated (-0.055, 95% CrI: -0.074 to -0.036). Our results highlight a complex cost-benefit trade-off from individual NPIs. For example, banning international travel is associated with both increases in GDP (0.014, 0.002—0.025) and decreases in excess deaths (-0.014, 95% CrI: -0.028 - -0.001). Country-specific random effects, such as the poverty rate, are positively associated with excess deaths while the UN government effectiveness index is negatively associated with excess deaths. Interpretation: The interplay between transmission intensity, excess deaths, population mobility and economic output is highly complex, and none of these factors can be considered in isolation. Our results reinforce the intuitive idea that significant economic activity arises from diverse person-to-person interactions. Our analysis quantifies and highlights that the impact of disease on a given country is complex and multifaceted. Long-term economic impairments are not fully captured by our model, as well as long-term disease effects (Long COVID). [ABSTRACT FROM AUTHOR]

The mining operation is a challenging field for human development. Deep underground mining has increased as a result of the lack of economic minerals close to the surface of the ground. Mining companies are now thinking of backfilling mine wastes to prevent mine collapse during later and deeper extraction stages due to safety and environmental concerns. The production of waste products (for example, fly ash, slag, and tailings) during the processing of minerals, the production of electricity, and the production of metals is tremendous. There is a wealth of information available on employing fly ash, slag, and mine tailings as a partial substitute for cement, as well as for paste backfilling. Nevertheless, a thorough analysis of the use of industrial waste and mining tailings as mine backfill is mostly absent. This article offers a grave evaluation of the use of mine and industrial. Geotechnical characteristics, backfill material features, and issues relating to strength prediction are all examined. In addition to the aforementioned, the effects of substitute binders, such as industrial waste, are considered when modifying paste backfills utilizing admixture. The paper finished with future paths for mine paste backfilling study, specifically utilizing ordinary and non-natural essences for primary strength growth. Hence, continued use of such engineering waste supplies for mill paste backfilling might benefit from this research. [ABSTRACT FROM AUTHOR]

This study is focused on the effect of vibration induced by moving trains in tunnels on the surrounding ground and structures. A three-dimensional finite element model is established for a one-track railway tunnel and an adjacent twelve-storey building frame by using commercial software Midas GTS-NX (2019) and Midas Gen. This study considered the moving load effect of a complete train, which varies with space as well as with time. The effect of factors such as train speed, overburden pressure on the tunnel and variation in soil properties are studied in the time domain. As a result, the variations in horizontal and vertical acceleration for two different sites, i.e., the free ground surface (without structure) and the area containing the structure, are compared. Also, the displacement pattern of the raft foundation is plotted for different train velocities. At lower speeds, the heaving phenomenon is negligible, but as the speed increases, both the heaving and differential settlement increase in the foundation. This study demonstrates that the effect of moving train vibrations should be considered in the design of new nearby structures and proper ground improvement should be considered for existing structures. [ABSTRACT FROM AUTHOR]

We propose a general Bayesian approach to modelling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular, an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries. The model parameterises the time-varying reproduction number R t through a multilevel regression framework in which covariates can be governmental interventions, changes in mobility patterns, or other behavioural measures. Bayesian multilevel modelling allows a joint fit across regions, with partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics: estimates from countries at later stages in their epidemics informed those of countries at earlier stages. Originally released as Imperial College Reports, the validity of this approach was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and derived observations, including deaths, cases, hospitalizations, ICU admissions, and seroprevalence surveys. In this article, we additionally explore the confounded nature of NPIs and mobility. Versions of our model were used by New York State, Tennessee, and Scotland to estimate the current epidemic situation and make policy decisions. [ABSTRACT FROM AUTHOR]

Evaluating normalising constants is important across a range of topics in statistical learning, notably Bayesian model selection. However, in many realistic problems this involves the integration of analytically intractable, high-dimensional distributions, and therefore requires the use of stochastic methods such as thermodynamic integration (TI). In this paper we apply a simple but under-appreciated variation of the TI method, here referred to as referenced TI, which computes a single model's normalising constant in an efficient way by using a judiciously chosen reference density. The advantages of the approach and theoretical considerations are set out, along with pedagogical 1 and 2D examples. The approach is shown to be useful in practice when applied to a real problem —to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density. [ABSTRACT FROM AUTHOR]

Background: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables. Methods: This is a population-based cohort study of 48,944 Danish citizens 65 years of age and older suffering bereavement within 2013–2016. Individuals were followed from date of spousal loss until death from all causes or 31st of December 2016, whichever came first. Healthcare expenditures were available on weekly basis for each person during the follow-up and used as predictors for mortality risk in Extreme Gradient Boosting models. The extent to which medical spending trajectories improved mortality predictions compared to models with sociodemographics, was assessed with respect to discrimination (AUC), overall prediction error (Brier score), calibration, and clinical benefit (decision curve analysis). Results: The AUC of age and sex for mortality the year after spousal loss was 70.8% [95% CI 68.8, 72.8]. The addition of sociodemographic variables led to an increase of AUC ranging from 0.9% to 3.1% but did not significantly reduce the overall prediction error. The AUC of the model combining the variables above plus medical spending usage was 80.8% [79.3, 82.4] also exhibiting smaller Brier score and better calibration. Overall, patterns of healthcare expenditures improved mortality predictions the most, also exhibiting the highest clinical benefit among the rest of the models. Conclusion: Temporal patterns of medical spending have the potential to significantly improve our assessment on who is at high risk of dying after suffering spousal loss. The proposed methodology can assist in a more efficient risk profiling and prognosis of bereaved individuals. [ABSTRACT FROM AUTHOR]

Objective To develop a prognostic model of 1-year mortality for individuals aged 65+ presenting at the emergency department (ED) with a fall based on health care spending patterns to guide clinical decision-making. Design Population-based cohort study (n  = 35,997) included with a fall in 2013 and followed 1 year. Methods Health care spending indicators (dynamical indicators of resilience, DIORs) 2 years before admission were evaluated as potential predictors, along with age, sex and other clinical and sociodemographic covariates. Multivariable logistic regression models were developed and internally validated (10-fold cross-validation). Performance was assessed via discrimination (area under the receiver operating characteristic curve, AUC), Brier scores, calibration and decision curve analysis. Results The AUC of age and sex for mortality was 72.5% [95% confidence interval 71.8 to 73.2]. The best model included age, sex, number of medications and health care spending DIORs. It exhibited high discrimination (AUC: 81.1 [80.5 to 81.6]), good calibration and potential clinical benefit for various threshold probabilities. Overall, health care spending patterns improved predictive accuracy the most while also exhibiting superior performance and clinical benefit. Conclusions Patterns of health care spending have the potential to significantly improve assessments on who is at high risk of dying following admission to the ED with a fall. The proposed methodology can assist in predicting the prognosis of fallers, emphasising the added predictive value of longitudinal health-related information next to clinical and sociodemographic predictors. [ABSTRACT FROM AUTHOR]

Landslides are one of the extensive and destructive natural hazards in the mountainous areas and can cause loss of life and infrastructure. Slope stabilization methods can be adopted to minimize the losses due to landslides. The aim of this study is to investigate the failed slope due to landslides and suggest the site-specific ground improvement solutions capable to increase the factor of safety and reduce the displacement. In this study, a slope on a National Highway connecting the ridge to the foot hills in the Darjeeling Himalayas India is selected as the study site due to occurrence of landslides. The study site is investigated and the slope stability analyses are carried out by two-dimensional finite-element analyses. Comparisons of four different slope stabilization methods are introduced with the understanding of behavior of support system. Different slope stabilization methods along with or without ground improvement techniques like benching, retaining wall, soil nails, micropiles, shotcrete, and geogrid are attempted. Factors of safety along with displacements are computed for all the different combinations with and without rainfall effect. Parametric study is also carried out to investigate the optimum configuration for the suggested slope stabilization technique. After comparing and assessing different ground improvement techniques, the results suggest that the combination of soil nails with shotcrete and geogrid on stepped cut slope face along with retaining walls supported by micropiles and soil nails at the bottom has performed well and satisfies the stability conditions for the selected slope. The suggested combination provides an optimal solution and remediation option for stabilizing the slope. [ABSTRACT FROM AUTHOR]

Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump–Mode–Jagers branching process. This model accommodates a time-varying reproduction number and a time-varying distribution for the generation interval. We then derive renewal-like integral equations for incidence, cumulative incidence and prevalence under this model. We show that the equations for incidence and prevalence are consistent with the so-called back-calculation relationship. We analyse two particular cases of these integral equations, one that arises from a Bellman–Harris process and one that arises from an inhomogeneous Poisson process model of transmission. We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling. We present a numerical discretisation scheme to solve these equations, and use this scheme to estimate rates of transmission from serological prevalence of SARS-CoV-2 in the UK and historical incidence data on Influenza, Measles, SARS and Smallpox. [ABSTRACT FROM AUTHOR]

Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a time-varying general branching process. Our framework explicitly decomposes aleatoric variance into mechanistic components, quantifying the contribution to uncertainty produced by each factor in the epidemic process, and how these contributions vary over time. The aleatoric variance of an outbreak is itself a renewal equation where past variance affects future variance. We find that, superspreading is not necessary for substantial uncertainty, and profound variation in outbreak size can occur even without overdispersion in the offspring distribution (i.e. the distribution of the number of secondary infections an infected person produces). Aleatoric forecasting uncertainty grows dynamically and rapidly, and so forecasting using only epistemic uncertainty is a significant underestimate. Therefore, failure to account for aleatoric uncertainty will ensure that policymakers are misled about the substantially higher true extent of potential risk. We demonstrate our method, and the extent to which potential risk is underestimated, using two historical examples. Intrinsic randomness is a critical source of uncertainty in infectious disease outbreaks. The authors show in a series of analytical results how this source of uncertainty can be better characterised. [ABSTRACT FROM AUTHOR]

Background: Spousal bereavement is a life event that affects older people differently. We investigated the impact of spousal bereavement on medical expenditures and mortality in the general population, emphasizing on age and sex. Methods: Data are from a population-based, retrospective cohort study following 924,958 Danish citizens over the age of 65 years, within 2011–2016. Changes in health care expenditures in those who suffer bereavement were compared with time matched changes among those who did not. Mortality hazards were analysed with time to event analysis. Results: A total of 77,722 (~8.4%) individuals experienced bereavement, 65.8% being females. Among males, bereavement was associated with increase of expenditures the year after, that was 42 Euros per week (95% CI, 36 to 48) larger than the non-bereaved group. The corresponding increase for females was 35 Euros per week (95% CI, 30 to 40). The increase of mortality hazards was highest in the first year after bereavement, higher in males than females, in young old and almost absent in the oldest old. Compared with the reference, mortality the year after spousal loss was 70% higher (HR 1.70 [95% CI 1.40 to 2.08]) for males aged 65–69 years and remained elevated for a period of six years. Mortality for females aged 65–69 years was 27% higher in the first year (HR 1.27, [1.07 to 1.52]), normalizing thereafter. Conclusion: Bereavement affects older people differently with younger males being most frail with limited recovery potential. [ABSTRACT FROM AUTHOR]

Stochastic processes provide a mathematically elegant way to model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. However, in practice efficient inference by optimisation or marginalisation is difficult, a problem further exacerbated with big data and high dimensional input spaces. We propose a novel variational autoencoder (VAE) called the prior encoding variational autoencoder (π VAE). π VAE is a new continuous stochastic process. We use π VAE to learn low dimensional embeddings of function classes by combining a trainable feature mapping with generative model using a VAE. We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions such as their integrals. For popular tasks, such as spatial interpolation, π VAE achieves state-of-the-art performance both in terms of accuracy and computational efficiency. Perhaps most usefully, we demonstrate an elegant and scalable means of performing fully Bayesian inference for stochastic processes within probabilistic programming languages such as Stan. [ABSTRACT FROM AUTHOR]

We propose a new framework to model the COVID‐19 epidemic of the United Kingdom at the local authority level. The model fits within a general framework for semi‐mechanistic Bayesian models of the epidemic based on renewal equations, with some important innovations, including a random walk modelling the reproduction number, incorporating information from different sources, including surveys to estimate the time‐varying proportion of infections that lead to reported cases or deaths, and modelling the underlying infections as latent random variables. The model is designed to be updated daily using publicly available data. We envisage the model to be useful for now‐casting and short‐term projections of the epidemic as well as estimating historical trends. The model fits are available on a public website: https://imperialcollegelondon.github.io/covid19local. The model is currently being used by the Scottish government to inform their interventions. [ABSTRACT FROM AUTHOR]

Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement. [ABSTRACT FROM AUTHOR]

During the second half of 2020, many European governments responded to the resurging transmission of SARS-CoV-2 with wide-ranging non-pharmaceutical interventions (NPIs). These efforts were often highly targeted at the regional level and included fine-grained NPIs. This paper describes a new dataset designed for the accurate recording of NPIs in Europe's second wave to allow precise modelling of NPI effectiveness. The dataset includes interventions from 114 regions in 7 European countries during the period from the 1st August 2020 to the 9th January 2021. The paper includes NPI definitions tailored to the second wave following an exploratory data collection. Each entry has been extensively validated by semi-independent double entry, comparison with existing datasets, and, when necessary, discussion with local epidemiologists. The dataset has considerable potential for use in disentangling the effectiveness of NPIs and comparing the impact of interventions across different phases of the pandemic. Measurement(s) Government non-pharmaceutical interventions against Covid-19 Technology Type(s) Interpretation by researchers Sample Characteristic - Organism Homo sapiens [ABSTRACT FROM AUTHOR]

This paper presents the effect of impact load and weathering of surrounding rockmass on the deformation behavior of urban underground structures. The FEM based numerical simulation is carried out by varying overburden depths, impact energy, and rock weathering grade. Based on this study, it is suggested that subsidence in tunnel under dynamic loading conditions are basically the function of impact energies, engineering characteristics of surrounding rock mass, depth and diameter of U/G structure, and induced stresses. Overburden depth and weathering grade are found to be the critical parameters in the study. It is observed that the tunnel crown deformation increases with increase in weathering grade. It is also found that the weathering of rock affects the depth of penetration of hammer. The depth of penetration of hammer is large in case of highly weathered Basalt rock as compared to Fresh rock. It is noticed that the increase in cover depth decreases the tunnel deformation and mises stresses around the tunnel periphery. Hence, the tunnels at deeper depth are more stable as compare to the tunnels with lesser overburden depth under dynamic loading conditions. In present work, parametric study is carried out for different rockmasses, subjected to impact loading. The findings of this work suggests that the synthetic rockmass, which is prepared in the laboratory, can be used to replicate in-situ conditions by representing weathered rockmass. Finally, the investigation is performed for the prototype model, and the dynamic response of tunnel lining is computed. The tunnel reinforcement is safe against impact loading, whereas the concrete lining is failed completely at the crown of tunnel and the significant amount of failure is observed along the tunnel length. Hence, the improvement in the tunnel lining material and thickness is required. [ABSTRACT FROM AUTHOR]

The B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in the state of Maharashtra in late 2020 and spread throughout India, outcompeting pre-existing lineages including B.1.617.1 (Kappa) and B.1.1.7 (Alpha)1. In vitro, B.1.617.2 is sixfold less sensitive to serum neutralizing antibodies from recovered individuals, and eightfold less sensitive to vaccine-elicited antibodies, compared with wild-type Wuhan-1 bearing D614G. Serum neutralizing titres against B.1.617.2 were lower in ChAdOx1 vaccinees than in BNT162b2 vaccinees. B.1.617.2 spike pseudotyped viruses exhibited compromised sensitivity to monoclonal antibodies to the receptor-binding domain and the amino-terminal domain. B.1.617.2 demonstrated higher replication efficiency than B.1.1.7 in both airway organoid and human airway epithelial systems, associated with B.1.617.2 spike being in a predominantly cleaved state compared with B.1.1.7 spike. The B.1.617.2 spike protein was able to mediate highly efficient syncytium formation that was less sensitive to inhibition by neutralizing antibody, compared with that of wild-type spike. We also observed that B.1.617.2 had higher replication and spike-mediated entry than B.1.617.1, potentially explaining the B.1.617.2 dominance. In an analysis of more than 130 SARS-CoV-2-infected health care workers across three centres in India during a period of mixed lineage circulation, we observed reduced ChAdOx1 vaccine effectiveness against B.1.617.2 relative to non-B.1.617.2, with the caveat of possible residual confounding. Compromised vaccine efficacy against the highly fit and immune-evasive B.1.617.2 Delta variant warrants continued infection control measures in the post-vaccination era.A study of SARS-CoV-2 variants examining their transmission, infectivity, and potential resistance to therapies provides insights into the biology of the Delta variant and its role in the global pandemic. [ABSTRACT FROM AUTHOR]

The UK and Sweden have among the worst per-capita COVID-19 mortality in Europe. Sweden stands out for its greater reliance on voluntary, rather than mandatory, control measures. We explore how the timing and effectiveness of control measures in the UK, Sweden and Denmark shaped COVID-19 mortality in each country, using a counterfactual assessment: what would the impact have been, had each country adopted the others' policies? Using a Bayesian semi-mechanistic model without prior assumptions on the mechanism or effectiveness of interventions, we estimate the time-varying reproduction number for the UK, Sweden and Denmark from daily mortality data. We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country's first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic. [ABSTRACT FROM AUTHOR]

Insecticide-treated nets (ITNs) are one of the most widespread and impactful malaria interventions in Africa, yet a spatially-resolved time series of ITN coverage has never been published. Using data from multiple sources, we generate high-resolution maps of ITN access, use, and nets-per-capita annually from 2000 to 2020 across the 40 highest-burden African countries. Our findings support several existing hypotheses: that use is high among those with access, that nets are discarded more quickly than official policy presumes, and that effectively distributing nets grows more difficult as coverage increases. The primary driving factors behind these findings are most likely strong cultural and social messaging around the importance of net use, low physical net durability, and a mixture of inherent commodity distribution challenges and less-than-optimal net allocation policies, respectively. These results can inform both policy decisions and downstream malaria analyses. Insecticide treated nets (ITNs) are an important part of malaria control in Africa and WHO targets aim for 80% coverage. This study estimates the spatio-temporal access and use of ITNs in Africa from 2000-2020, and shows that both metrics have improved over time but access remains below WHO targets. [ABSTRACT FROM AUTHOR]

Background: Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM).Methods: Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson's and Lin's correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model.Results: For active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63-8.80), while eSIR yields 8.35 (7.19-9.60), SAPHIRE returns 8.17 (7.90-8.52) and SEIR-fansy projects 8.51 (8.18-8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy.Conclusions: In this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the "total" number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 lineage B.1.1.7, designated variant of concern (VOC) 202012/01 by Public Health England1, was first identified in the UK in late summer to early autumn 20202. Whole-genome SARS-CoV-2 sequence data collected from community-based diagnostic testing for COVID-19 show an extremely rapid expansion of the B.1.1.7 lineage during autumn 2020, suggesting that it has a selective advantage. Here we show that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S gene target failures (SGTF) in community-based diagnostic PCR testing. Analysis of trends in SGTF and non-SGTF case numbers in local areas across England shows that B.1.1.7 has higher transmissibility than non-VOC lineages, even if it has a different latent period or generation time. The SGTF data indicate a transient shift in the age composition of reported cases, with cases of B.1.1.7 including a larger share of under 20-year-olds than non-VOC cases. We estimated time-varying reproduction numbers for B.1.1.7 and co-circulating lineages using SGTF and genomic data. The best-supported models did not indicate a substantial difference in VOC transmissibility among different age groups, but all analyses agreed that B.1.1.7 has a substantial transmission advantage over other lineages, with a 50% to 100% higher reproduction number.Genetic and testing data from England show that the SARS-CoV-2 variant of concern B.1.1.7 has a transmission advantage over other lineages. [ABSTRACT FROM AUTHOR]

The ammonium salt of diisopropyl dithiophosphate (A1), O,O′- diisopropanediyl S-(N-phthalimido methyl) dithiophosphate (L1) and zinc diisopropyl dithiophosphate (L2) were isolated in non-aqueous medium and well-characterized by UV–visible spectroscopy, infrared (IR) spectroscopy, NMR (1H, 13C and 31P) spectra and single-crystal X-ray diffraction analysis. The isopropyl group attached to the metal in compound L2 functions as the inter- and intra-chelating group which deviate slightly from planarity with the metal atoms. The central eight-membered ring possesses the "cradle" configuration. The fluorescence spectra of the three compounds A1, L1 and L2 were compared and it was observed that derivatives show excitation due to electron-donor and -acceptor groups. The intramolecular charge transfer (ICT) occurs from P = S to one of the C = O groups in the phthalimide ring and thiophosphate when these molecules are excited electronically in a polar solvent and, therefore, ICT state displays a fluorescence property. The molecular docking study was also carried out for the three compounds. Based on docking studies, a zinc-based metal complex of isopropyl dithiophosphate showed good antifungal activity whereas phthalimide-based isopropyl dithiophosphate derivative showed better antibacterial activity. [ABSTRACT FROM AUTHOR]

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study. Estimating the effects of non-pharmaceutical interventions for COVID-19 is challenging, partly due to variations in testing. Here, the authors use viral sequence data as an alternative means of inferring intervention effects, and show that delays in implementation resulted in more severe epidemics. [ABSTRACT FROM AUTHOR]

Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based. Author summary: This paper introduces a mathematically well-founded method for infectious disease outbreaks known as Hawkes Processes. These semi-mechanistic models are relatively new to the infectious diseases toolkit and enable us to combine disease specific information such as the infectious profile with statistical rigour to recreate temporal disease transmission. We show that these methods are very suited to modelling malaria in communities close to eliminating malaria—in particular China and Eswatini—where we are able to disentangle the contribution of exogenous (external) transmission and endogenous (person-to-person) transmission. This is particularly important for developing policies when counties are approaching elimination. [ABSTRACT FROM AUTHOR]

In the present world, the need for underground structures has grown many folds due to the increasing population, the advancement of public infrastructure, and the scarcity of land. Underground structures also provide attractive alternatives for storage of explosives and other military hardware. Being at shallow depth, their potential impact on the environment and surrounding structures can be significant. Therefore, it is crucial to understand the surrounding material behaviour for the safe and economical design of underground facilities. In the present work, an attempt is made to simulate the in situ condition using finite element, which has been validated by experimental results, to understand the tunnel deformation behaviour under static loading condition in soft rocks. 3D non-linear finite element analysis has been carried out by using Abaqus. Rockmass-tunnel model considered in this study has dimensions of 30 cm × 30 cm and 35 cm. The diameter of the tunnel has been varied from 2.5 cm, 3.5 cm to 5.0 cm. Similarly, the overburden depth is taken as 2.5 cm, 3.5 cm and 5.0 cm. Both the lined and unlined cases of the tunnel have been considered. Geo-material has been prepared in the laboratory having four different compositions of POP, sand, clay and mica. The weathering effect of the rockmass is also considered in the study. Fresh, slightly weathered, medium weathered, and highly weathered are the four different weathering stages of basalt rock taken into consideration. The elasto-plastic behaviour is considered for the natural and synthetic rock, and a Mohr–Coulomb constitutive model is incorporated. Stress and deformation behaviour is monitored for different rocks and geo-materials. Longitudinal and transverse profiles of the tunnel have been plotted to understand the response of tunnel lining and the surrounding rockmass, along and across the point of loading. The paper concludes that the diameter of the tunnel, overburden depth of the tunnel, and weathering of rock has a significant effect on the stability of tunnels under severe loading conditions. [ABSTRACT FROM AUTHOR]

Background: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. We develop a novel predictive framework to understand the temporal dynamics of hospital demand.Methods: We compare and combine state-of-the-art forecasting methods to predict hospital demand 1, 3 or 7 days into the future. In particular, our analysis compares machine learning algorithms to more traditional linear models as measured in a mean absolute error (MAE) and we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators.Results: We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. Our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of ±14 and ±10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively.Conclusions: Simple linear methods like generalized linear models are often better or at least as good as ensemble learning methods like the gradient boosting or random forest algorithm. However, though sophisticated machine learning methods are not necessarily better than linear models, they improve the diversity of model predictions so that stacked predictions can be more robust than any single model including the best performing one. [ABSTRACT FROM AUTHOR]

Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that—for all of the countries we consider here—current interventions have been sufficient to drive Rt below 1 (probability Rt < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions—and lockdowns in particular—have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control. Modelling based on pooled data from 11 European countries indicates that non-pharmaceutical interventions—particularly lockdowns—have had a marked effect on SARS-CoV-2 transmission, driving the reproduction number of the infection below 1. [ABSTRACT FROM AUTHOR]

In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation. Author summary: Although malaria is still responsible for a great deal of death and illness in many parts of the world, many national control programmes have made great strides in controlling malaria and now are in a position to aim for elimination. However, in order to monitor progress towards elimination and plan interventions, it is crucial to measure malaria transmission and how it varies over space and time. However, traditional metrics used to measure malaria transmission are not suitable in elimination settings. China is one example of a country approaching elimination, with aims to eliminate the disease by 2020. Using a detailed individual level dataset of the times and locations of people showing symptoms of malaria, we use approaches adapted from the study of how information spreads through social networks to estimate the likelihood of transmission occurring between cases. This information is used to estimate how many people we expect each case to go on to infect. In elimination settings, this number is an indication of how quickly elimination will be reached and how likely we are to see a resurgence in cases once elimination is achieved. Our results show a decline in this metric over time, as well as seasonal changes in transmission which are different to the patterns in when the most cases were observed. [ABSTRACT FROM AUTHOR]

If it is well understood how models differ this gives a more varied understanding of the epidemic in these places - for example, one model which relies more on spatial correlation would assume that a local outbreak will start spreading whereas another model with less spatial correlation, such as ours, might not predict this. We experimented with mobility data ourselves, but we chose not to use it, as, in our experience, it tended to large swings in the estimates that were not necessarily reflected in the case data later on, and as mobility data has been, at least when we developed the model, only been available with substantial delays. As far as the use of predetermined generation distribution is concerned, we agree a model that can account for changing generation distribution would be ideal. [Extracted from the article]

Background: Hepatic venous outflow tract obstruction (HVOTO) and extrahepatic portal venous obstruction (EHPVO) are important causes of portal hypertension and related complications in India. Both these conditions result from splanchnic venous thrombosis. In recent years, a V617F somatic mutation in Janus kinase 2 (JAK2) gene which is highly specific for myeloproliferative disorders has been detected in 40 % to 50 % and 30 % to 35 % of Western patients with HVOTO and EHPVO, respectively. However, data on this mutation in these conditions from Asian countries are limited. Methods: We looked for JAK2 V617F mutation in Indian patients with HVOTO ( n = 40, median age 31 [range 17-51] years, 21 female) and EHPVO ( n = 50, median age 23 [15-70] years, 25 female) by using two separate methods. Both the methods involved polymerase chain reaction using allele-specific primers. Positive results on one or both of these techniques were confirmed using DNA sequencing. Results: None of the 40 patients with HVOTO and only 1 of 50 patients with EHPVO was found to have JAK2 V617F mutation. In the one patient who was found to have this mutation, both the PCR methods and DNA sequencing showed positive results. Conclusion: Hypercoagulability associated with JAK2 V617F mutation and associated chronic myeloproliferative disorders was not a major cause of HVOTO and EHPVO in this population. [ABSTRACT FROM AUTHOR]

Background: The prevalence of hepatitis D virus (HDV) infection among persons with hepatitis B virus (HBV) infection shows geographic variation and has declined in recent times in several regions. In India, studies during the 1990s showed highly variable anti-HDV prevalence rates among HBV-infected persons; however, data using molecular testing and recent data are not available. We therefore studied the prevalence of HDV infection in HBV-infected patients using tests for anti-HDV and HDV ribonucleic acid (RNA). Methods: Two cohorts of patients with HBV infection were enrolled (cohort A, n = 150, January to December 2012; cohort B, n = 168, October 2013 to April 2014). Sera from cohort A were tested for IgG anti-HDV using three enzyme immunoassays and those from cohort B for IgG anti-HDV using an enzyme immunoassay and for HDV RNA using a real-time amplification assay. Results: Of the 318 subjects (259 male; mean age 36.9 years), 161 (50.6 %) had chronic hepatitis B, 101 (31.8 %) had cirrhosis, 52 (16.3 %) had acute viral hepatitis, and 4 (1.3 %) had acute liver failure. In cohort A, all specimens tested negative for anti-HDV antibodies using all the three assays. In cohort B, all specimens tested negative for anti-HDV IgG as well as HDV RNA. Conclusion: Our data indicate that HDV infection is uncommon in northern India. [ABSTRACT FROM AUTHOR]

As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that Rt was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%–4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals. High numbers of COVID-19-related deaths have been reported in the United States, but estimation of the true numbers of infections is challenging. Here, the authors estimate that on 1 June 2020, 3.7% of the US population was infected with SARS-CoV-2, and 0.01% was infectious, with wide variation by state. [ABSTRACT FROM AUTHOR]