Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

Well parameterised epidemiological models including accurate representation of contacts, are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here we fit age-stratified models, including re-estimation of relative contact rates between age-classes, to public data describing the 2020-21 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions, and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing Approximate Bayesian Computation (ABC) methodology with model-based proposals (MBP) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalisation rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrisation of dynamic transmission models that can inform data-driven public health policy and interventions.

Download Full-text

A twelve-month projection to September 2022 of the Covid-19 epidemic in the UK using a Dynamic Causal Model

10.1101/2021.10.04.21262827 ◽

2021 ◽

Author(s):

Cam Bowie ◽

Karl Friston

Keyword(s):

Public Health ◽

Causal Model ◽

Hospital Admissions ◽

Economic Cost ◽

Model Parameters ◽

Health Measures ◽

Contact Rates ◽

The Future ◽

The Uk ◽

Dynamic Causal Model

Objectives Predicting the future UK Covid-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine programme. Methods A Dynamic Causal Model (DCM) is used to estimate the model parameters of the epidemic such as vaccine effectiveness and increased transmissibility of alpha and delta variants, the vaccine programme roll-out and changes in contact rates. The model predicts the future trends in infections, long-Covid, hospital admissions and deaths. Results Two dose vaccination given to 66% of the UK population prevents transmission following infection by 44%, serious illness by 86% and death by 93%. Despite this, with no other public health measures used, cases will increase from 37 million to 61 million, hospital admission from 536,000 to 684,000 and deaths from 136,000 to 142,000 over twelve months. Discussion Vaccination alone will not control the epidemic. Relaxation of mitigating public health measures carries several risks including overwhelming the health services, the creation of vaccine resistant variants and the economic cost of huge numbers of acute and chronic cases.

Download Full-text

Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

Journal of The Royal Society Interface ◽

10.1098/rsif.2017.0340 ◽

2017 ◽

Vol 14 (134) ◽

pp. 20170340 ◽

Cited By ~ 6

Author(s):

Aidan C. Daly ◽

Jonathan Cooper ◽

David J. Gavaghan ◽

Chris Holmes

Keyword(s):

Bayesian Inference ◽

Bayesian Methods ◽

Sequential Monte Carlo ◽

Model Parameters ◽

Biological Models ◽

Exact Inference ◽

Modelling Studies ◽

Approximate Bayesian ◽

Approximate Bayesian Inference ◽

Abc Methods

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

Download Full-text

Flexural behavior of composite structural insulated panels with magnesium oxide board facings

Archives of Civil and Mechanical Engineering ◽

10.1007/s43452-020-00109-y ◽

2020 ◽

Vol 20 (4) ◽

Author(s):

Łukasz Smakosz ◽

Ireneusz Kreja ◽

Zbigniew Pozorski

Keyword(s):

Magnesium Oxide ◽

Material Model ◽

Expanded Polystyrene ◽

Flexural Behavior ◽

Joint Analysis ◽

Model Parameters ◽

Fe Model ◽

Computational Results ◽

Four Point Bending ◽

Proposed Model

Abstract The current report is devoted to the flexural analysis of a composite structural insulated panel (CSIP) with magnesium oxide board facings and expanded polystyrene (EPS) core, that was recently introduced to the building industry. An advanced nonlinear FE model was created in the ABAQUS environment, able to simulate the CSIP’s flexural behavior in great detail. An original custom code procedure was developed, which allowed to include material bimodularity to significantly improve the accuracy of computational results and failure mode predictions. Material model parameters describing the nonlinear range were identified in a joint analysis of laboratory tests and their numerical simulations performed on CSIP beams of three different lengths subjected to three- and four-point bending. The model was validated by confronting computational results with experimental results for natural scale panels; a good correlation between the two results proved that the proposed model could effectively support the CSIP design process.

Download Full-text

On the reliability of PKIIKP phase identification at a single station

10.5194/egusphere-egu21-6554 ◽

2021 ◽

Author(s):

Olga Usoltseva ◽

Vladimir Ovtchinnikov

Keyword(s):

Inner Core ◽

Seismic Velocity ◽

Seismic Station ◽

Shear Velocity ◽

Outer Core ◽

Joint Analysis ◽

Time Range ◽

Model Parameters ◽

Phase Identification ◽

Single Station

Study of the contact zone between the inner and outer core represents considerable interest for understanding of properties, structures and dynamic of the Earth's core. One of the sources of the data about the processes proceeding in the top part of the inner core is the seismic wave PKIIKP once reflected from an undersize inner core boundary. Amplitudes of these waves are sensitive to the shear velocity in the top part of the inner core and are small. Therefore their identification at a single seismic station is not reliable without application of additional methods of analysis. Significant in this regard is the discussion about the source (in inner core or in mantle) of anomalous arrivals detected at the TAM station in North Africa [1,2] in the time range of PKIIKP phase.To estimate influence of model parameters (S and P seismic velocity) on the characteristics of PKIIKP wave (amplitude and travel time) we calculated sensitivity kernels for upper mantle and inner core for dominant period 1.2 s, azimuth step 0.2 degrees and radius step 20 km by using DSM Kernel Suite algorithm. It was revealed that PKIIKP amplitude is more sensitivities to mantle heterogeneities than to inner core ones. For reducing the effects of the overlying structures we suppose to use &#1072; joint analysis PKIIKP and pPKIIKP waves. With this approach, an incorrect identification of the PKIIKP wave is most likely excluded. We demonstrate the effectiveness of the approach on the example of processing the seismogram of the 11.02.2015 earthquake re&#1089;orded at the GZH station in China at a distance of 179.4 degrees.1. Wang W., Song X. Analyses of anomalous amplitudes of antipodal PKIIKP waves, EaPP. 2019. V. 3. P. 212-217. doi: 10.26464/epp20190232. Tsuboi S., Butler R. Inner core differential rotation inferred from antipodal seismic observations, PEPI, 2020. V.301. 106451.

Download Full-text

Virtual screening attributes male biased COVID-19 mortality to predicted antiviral activity of female sex hormones

Letters in Drug Design & Discovery ◽

10.2174/1570180818666210528153844 ◽

2021 ◽

Vol 18 ◽

Author(s):

Galal Yahya ◽

Basem Mansour ◽

Kristina Keuper ◽

Moataz Shaldam ◽

Ahmed El-Baz

Keyword(s):

Virtual Screening ◽

Sex Hormones ◽

Hospital Admissions ◽

Epidemiological Data ◽

Antiviral Effect ◽

Sex Hormone ◽

Female Sex ◽

Female Sex Hormones ◽

Disaggregated Data ◽

Male Sex

Background: Coronavirus disease-19 (COVID-19) is a newly emerged pandemic leading to a state of international alert with millions of infected individuals and thousands of deaths all over the world. Analysis of statistics and epidemiological data for the pandemic outcome pinpointed a puzzling influence of human sex on the heterogeneous outcome of COVID-19, where hospital admissions and mortality were higher among males than females. Two theories explained the observed male-biased COVID-19 mortality based on either dosage of immunoregulatory genes coded in X- chromosomes or on the abundance of the angiotensin-converting enzyme two (ACE2) receptors in males than females. Objective: In our study, we propose a third scenario through virtual screening of direct antiviral effects of sex hormones. Materials & Methods: Updated screening statistics from 47 countries displaying sex-disaggregated data on COVID-19 were employed and visualized in the form of heatmaps depicting sex difference effects on statistics of cases and deaths. Molecular docking and binding simulations of investigated sex steroids against COVID-19 specific proteins were investigated. Results: Analysis of COVID-19 sex-disaggregated data confirmed that male-biased mortality and computer-aided docking found unexpected female sex hormones biased binding against key targets implicated in the life cycle of COVID-19 compared to the male sex hormone testosterone. Other investigated steroids showed promising docking scores, while the male sex hormone exhibited the lowest affinity. Conclusion: Female sex hormones virtually exhibited direct COVID-19 effect. The proposed antiviral effect of sex hormones should be considered to explain the outcomes of mortality; moreover, the fluctuation of sex hormones influences sex and personal derived-differential response to COVID-19 infection.

Download Full-text

Session 3: Joint Nutrition Society and Irish Nutrition and Dietetic Institute Symposium on ‘Nutrition and autoimmune disease’ Nutrition in Crohn's disease

Proceedings of The Nutrition Society ◽

10.1017/s0029665109001025 ◽

2009 ◽

Vol 68 (2) ◽

pp. 127-134 ◽

Cited By ~ 12

Author(s):

Maria O'Sullivan

Keyword(s):

Vitamin D ◽

Crohn’S Disease ◽

Crohn's Disease ◽

Enteral Nutrition ◽

Hospital Admissions ◽

Epidemiological Data ◽

Overweight And Obesity ◽

Micronutrient Deficiencies ◽

Hospital Stays ◽

Current Guidelines

The exact aetiology of Crohn's disease remains unknown. The consensus is that the disease results from a complex interaction between genes, immunity and environmental factors. Diet is attractive, in theory, as an environmental risk factor in the aetiology of the disease. The epidemiological data, often impeded by methodological issues, have failed to confirm a direct link between pre-diet illness and the development of Crohn's disease. Once diagnosed, however, nutrition has an important role in disease management. Among the nutritional issues are malnutrition, weight loss and suboptimal nutritional status; these outcomes may be present at any stage of the disease but are likely to be overt during acute illness and hospitalisation. Malnutrition has been identified in approximately 40% of hospital admissions with Crohn's disease and is associated with higher mortality, longer hospital stays and higher healthcare costs. Patients in remission may indeed be overweight and appear to be influenced by the general population trends toward overweight and obesity. Irrespective of BMI, patients are at risk of micronutrient deficiencies. Vitamin D deficiency, for example, is common in Crohn's disease and has important implications for bone health. Moreover, newer evidence suggests that vitamin D has potential anti-inflammatory effects. Dietary approaches, in the form of enteral nutrition, have previously been shown to reduce inflammation and treat the active disease. Current guidelines now recommend that corticosteroids are more effective than enteral nutrition for treating adults. Enteral nutrition has important growth and developmental benefits and continues to be a recommended therapy for children with Crohn's disease.

Download Full-text

Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data

PLoS ONE ◽

10.1371/journal.pone.0243026 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243026

Author(s):

Rajiv Bhatia ◽

Jeffrey Klausner

Keyword(s):

Risk Estimation ◽

Secondary Infection ◽

Case Fatality ◽

Infection Risk ◽

Individual Risk ◽

Incidence Data ◽

Household Transmission ◽

Contact Rates ◽

Time Period ◽

Public Data

We describe a method to estimate individual risks of hospitalization and death attributable to non-household and household transmission of SARS-CoV-2 using available public data on confirmed-case incidence data along with estimates of the clinical fraction, timing of transmission, isolation adherence, secondary infection risks, contact rates, and case-hospitalization and case-fatality ratios. Using the method, we estimate that risks for a 90-day period at the median daily summertime U.S. county confirmed COVID-19 case incidence of 10.8 per 100,000 and pre-pandemic contact rates range from 0.4 to 8.9 per 100,000 for the four deciles of age between 20 and 60 years. The corresponding 90-day period risk of hospitalization ranges from 13.7 to 69.2 per 100,000. Assuming a non-household secondary infection risk of 4% and pre-pandemic contact rates, the share of transmissions attributable to household settings ranges from 73% to 78%. These estimates are sensitive to the parameter assumptions; nevertheless, they are comparable to the COVID-19 hospitalization and fatality rates observed over the time period. We conclude that individual risk of hospitalization and death from SARS-CoV-2 infection is calculable from publicly available data sources. Access to publicly reported infection incidence data by setting and other exposure characteristics along with setting specific estimates of secondary infection risk would allow for more precise individual risk estimation.

Download Full-text

A Flexible Data-Driven Framework for COVID-19 Case Forecasting Deployed in a Developing-world Public Health Setting

10.1101/2021.11.01.21260020 ◽

2021 ◽

Author(s):

Sansiddh Jain ◽

Avtansh Tiwari ◽

Nayana Bannur ◽

Ayush Deva ◽

Siddhant Shingi ◽

...

Keyword(s):

Bayesian Model Averaging ◽

Model Averaging ◽

Ground Truth ◽

Model Parameters ◽

Forecasting System ◽

Critical Components ◽

Approximate Bayesian ◽

Operational Constraints ◽

Best Fit ◽

Public Health Setting

Forecasting infection case counts and estimating accurate epidemiological parameters are critical components of managing the response to a pandemic. This paper describes a modular, extensible framework for a COVID-19 forecasting system, primarily deployed in Mumbai and Jharkhand, India. We employ a variant of the SEIR compartmental model motivated by the nature of the available data and operational constraints. We estimate best-fit parameters using sequential Model-Based Optimization (SMBO) and describe the use of a novel, fast, and approximate Bayesian model averaging method (ABMA) for parameter uncertainty estimation that compares well with a more rigorous Markov Chain Monte Carlo (MCMC) approach in practice. We address on-the-ground deployment challenges such as spikes in the reported input data using a novel weighted smooth-ing method. We describe extensive empirical analyses to evaluate the accuracy of our method on ground truth as well as against other state-of-the-art approaches. Finally, we outline deployment lessons and describe how inferred model parameters were used by government partners to interpret the state of the epidemic and how model forecasts were used to estimate staffing and planning needs essential for addressing COVID-19 hospital burden.

Download Full-text

Analysis of the H.E.S.S. public data release with ctools

Astronomy and Astrophysics ◽

10.1051/0004-6361/201936010 ◽

2019 ◽

Vol 632 ◽

pp. A102

Author(s):

J. Knödlseder ◽

L. Tibaldo ◽

D. Tiziani ◽

A. Specovius ◽

J. Cardenzana ◽

...

Keyword(s):

Crab Nebula ◽

Real Data ◽

Joint Analysis ◽

Background Rate ◽

Cherenkov Telescopes ◽

Analysis Methods ◽

Public Data ◽

Data Release ◽

Open Source Software Package ◽

The Crab Nebula

The ctools open-source software package was developed for the scientific analysis of astronomical data from Imaging Air Cherenkov Telescopes (IACTs), such as H.E.S.S., VERITAS, MAGIC, and the future Cherenkov Telescope Array (CTA). To date, the software has been mainly tested using simulated CTA data; however, upon the public release of a small set of H.E.S.S. observations of the Crab nebula, MSH 15–52, RX J1713.7–3946, and PKS 2155–304 validation using real data is now possible. We analysed the data of the H.E.S.S. public data release using ctools version 1.6 and compared our results to those published by the H.E.S.S. Collaboration for the respective sources. We developed a parametric background model that satisfactorily describes the expected background rate as a function of reconstructed energy and direction for each observation. We used that model, and tested all analysis methods that are supported by ctools, including novel unbinned and joint or stacked binned analyses of the measured event energies and reconstructed directions, and classical On-Off analysis methods that are comparable to those used by the H.E.S.S. Collaboration. For all analysis methods, we found a good agreement between the ctools results and the H.E.S.S. Collaboration publications considering that they are not always directly comparable due to differences in the datatsets and event processing software. We also performed a joint analysis of H.E.S.S. and Fermi-LAT data of the Crab nebula, illustrating the multi-wavelength capacity of ctools. The joint Crab nebula spectrum is compatible with published literature values within the systematic uncertainties. We conclude that the ctools software is mature for the analysis of data from existing IACTs, as well as from the upcoming CTA.

Download Full-text

Using quasar X-ray and UV flux measurements to constrain cosmological model parameters

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa1855 ◽

2020 ◽

Vol 497 (1) ◽

pp. 263-278 ◽

Cited By ~ 3

Author(s):

Narayan Khadka ◽

Bharat Ratra

Keyword(s):

Cosmological Parameters ◽

Hubble Constant ◽

Acoustic Oscillation ◽

Joint Analysis ◽

Model Parameters ◽

Density Parameter ◽

Current Standard ◽

X Ray ◽

Flux Measurements ◽

Parameter Constraints

ABSTRACT Risaliti and Lusso have compiled X-ray and UV flux measurements of 1598 quasars (QSOs) in the redshift range 0.036 ≤ z ≤ 5.1003, part of which, z ∼ 2.4 − 5.1, is largely cosmologically unprobed. In this paper we use these QSO measurements, alone and in conjunction with baryon acoustic oscillation (BAO) and Hubble parameter [H(z)] measurements, to constrain cosmological parameters in six different cosmological models, each with two different Hubble constant priors. In most of these models, given the larger uncertainties, the QSO cosmological parameter constraints are mostly consistent with those from the BAO + H(z) data. A somewhat significant exception is the non-relativistic matter density parameter Ωm0 where QSO data favour Ωm0 ∼ 0.5 − 0.6 in most models. As a result, in joint analyses of QSO data with H(z) + BAO data the 1D Ωm0 distributions shift slightly towards larger values. A joint analysis of the QSO + BAO + H(z) data is consistent with the current standard model, spatially-flat ΛCDM, but mildly favours closed spatial hypersurfaces and dynamical dark energy. Since the higher Ωm0 values favoured by QSO data appear to be associated with the z ∼ 2 − 5 part of these data, and conflict somewhat with strong indications for Ωm0 ∼ 0.3 from most z < 2.5 data as well as from the cosmic microwave background anisotropy data at z ∼ 1100, in most models, the larger QSO data Ωm0 is possibly more indicative of an issue with the z ∼ 2 − 5 QSO data than of an inadequacy of the standard flat ΛCDM model.

Download Full-text