A Predictive Modelling Framework for COVID-19 Transmission to Inform the Management of Mass Events (Preprint)

2021 ◽  
Author(s):  
Claire Donnat
Keyword(s):  
Prediction Interval ◽  
Contextual Information ◽  
Predictive Modelling ◽  
Transmission Dynamics ◽  
Background Rate ◽  
Vaccination Rates ◽  
Modelling Framework ◽  
R Shiny ◽  
Order Of Magnitude ◽  
Main Components

BACKGROUND Modelling COVID-19 transmission at live events and public gatherings is essential to control the probability of subsequent outbreaks and communicate to participants their personalised risk. Yet, despite the fast-growing body of literature on COVID transmission dynamics, current risk models either neglect contextual information on vaccination rates or disease prevalence or do not attempt to quantitatively model transmission. OBJECTIVE This paper attempts to bridge this gap by providing informative risk metrics for live public events, along with a measure of their uncertainty. METHODS Building upon existing models, our approach ties together three main components: (a) reliable modelling of the number of infectious cases at the time of the event, (b) evaluation of the efficiency of pre-event screening, and (c) modelling of the event’s transmission dynamics and their uncertainty along using Monte Carlo simulations. RESULTS We illustrate the application of our pipeline for a concert at the Royal Albert Hall and highlight the risk’s dependency on factors such as prevalence, mask wearing, or event duration. We demonstrate how this event held on three different dates (August 3rd 2020, January 18th 2021, and March 8th 2021) would likely lead to transmission events only slightly above background rates (0.5 vs 0.2, 6.7 vs 3.5, and 5.4 vs 2.5, respectively. However, the 97.5 percentile of the prediction interval for the infections would likely be substantially higher than the background rate (6.8 vs 2, 89 vs 8, and 71 vs 7), further demonstrating that sole reliance on vaccination and antigen testing to gain entry would likely significantly underestimate the tail risk of the event. CONCLUSIONS Despite the unknowns surrounding COVID-19 transmission, our estimation pipeline opens the discussion on contextualized risk assessment by combining the best tools at hand to assess the order of magnitude of the risk. Our model can be applied to any future event, and is presented in a user-friendly R Shiny interface.

scholarly journals A Predictive Modelling Framework for COVID-19 Transmission to Inform the Management of Mass Events

2021 ◽  
Author(s):  
Claire Donnat ◽  
Freddy Bunbury ◽  
Jack Kreindler ◽  
Filippos T. Filippidis ◽  
Austen El-Osta ◽  
...  
Keyword(s):  
Risk Mitigation ◽  
Contextual Information ◽  
Predictive Modelling ◽  
Transmission Dynamics ◽  
Risk Models ◽  
Vaccination Rates ◽  
Public Events ◽  
Modelling Framework ◽  
Main Components ◽  
And Control

Modelling COVID-19 transmission at live events and public gatherings is essential to evaluate and control the probability of subsequent outbreaks. Model estimates can be used to inform event organizers about the possibility of super-spreading and the predicted efficacy of safety protocols, as well as to communicate to participants their personalised risk so that they may choose whether to attend. Yet, despite the fast-growing body of literature on COVID transmission dynamics, current risk models either neglect contextual information on vaccination rates or disease prevalence or do not attempt to quantitatively model transmission, thus limiting their potential to provide insightful estimates. This paper attempts to bridge this gap by providing informative risk metrics for live public events, along with a measure of their associated uncertainty. Starting with a thorough review of the literature and building upon existing models, our approach ties together three main components: (a) reliable modelling of the number of infectious cases at the time of the event, (b) evaluation of the efficiency of pre-event screening and risk mitigation protocols, and (c) modelling the transmission dynamics during the event. We demonstrate how uncertainty in the input parameters can be included in the model using Monte Carlo simulations. We discuss the underlying assumptions and limitations of our approach and implications for policy around live events management.

A controller for safe, convenient operation of LaB6 emitters on electron-beam instruments

1983 ◽  
Vol 41 ◽  
pp. 408-409
Author(s):  
W. J. Abramson ◽  
H. W. Estry ◽  
L. F. Allard
Keyword(s):  
Electron Beam ◽  
Control System ◽  
Thermal Shock ◽  
Appropriate Care ◽  
Electron Guns ◽  
Tungsten Filaments ◽  
Order Of Magnitude ◽  
Main Components

LaB6 emitters are becoming increasingly popular as direct replacements for tungsten filaments in the electron guns of modern electron-beam instruments. These emitters offer order of magnitude increases in beam brightness, and, with appropriate care in operation, a corresponding increase in source lifetime. They are, however, an order of magnitude more expensive, and may be easily damaged (by improper vacuum conditions and thermal shock) during saturation/desaturation operations. These operations typically require several minutes of an operator's attention, which becomes tedious and subject to error, particularly since the emitter must be cooled during sample exchanges to minimize damage from random vacuum excursions. We have designed a control system for LaBg emitters which relieves the operator of the necessity for manually controlling the emitter power, minimizes the danger of accidental improper operation, and makes the use of these emitters routine on multi-user instruments.Figure 1 is a block schematic of the main components of the control system, and Figure 2 shows the control box.

scholarly journals Modeling infection dynamics and mitigation strategies to support K-6 in-person instruction during the COVID-19 pandemic

2021 ◽  
Author(s):  
Douglas E. Morrison ◽  
Roch Nianogo ◽  
Vladimir Manuel ◽  
Onyebuchi A. Arah ◽  
Nathaniel Anderson ◽  
...  
Keyword(s):  
School Attendance ◽  
Secondary Infection ◽  
Mitigation Strategies ◽  
Transmission Dynamics ◽  
Model Parameters ◽  
Safety Education ◽  
Vaccination Rates ◽  
Health Authorities ◽  
Infection Dynamics ◽  
Public Health Authorities

AbstractObjectiveTo support safer in-person K-6 instruction during the coronavirus disease 2019 (COVID- 19) pandemic by providing public health authorities and school districts with a practical model of transmission dynamics and mitigation strategies.MethodsWe developed an agent-based model of infection dynamics and preventive mitigation strategies such as distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. The model parameters can be updated as the science evolves and are adjustable via an online user interface, enabling users to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.ResultsUnder default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.ConclusionsOur model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model’s parameters can be immediately updated in response to changes in epidemiological conditions, science of COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

scholarly journals IMPLEMENTASI R-SHINY UNTUK ANALISIS BIPLOT KOMPONEN UTAMA (Studi Kasus: Penggunaan Alat Kontrasepsi pada Peserta Aktif KB di Provinsi Jawa Tengah Tahun 2019)

2022 ◽  
Vol 10 (4) ◽  
pp. 499-507
Author(s):  
Andreanto Andreanto ◽  
Hasbi Yasin ◽  
Agus Rusgiyono
Keyword(s):  
Open Source Software ◽  
Birth Rate ◽  
Principal Component ◽  
Contraceptive Device ◽  
Biplot Analysis ◽  
Contraceptive Devices ◽  
The Family ◽  
R Shiny ◽  
Main Components ◽  
The Relationship

The population problem is a fairly complex and complicated problem. Therefore, Indonesia seeks to control the birth rate with the Family Planning program. The implementation of this program can be evaluated through statistical data. The statistical analysis used is biplot principal component analysis to see the relationship between districts/cities in choosing the contraceptive device/method used, the variance of each contraceptive device/method, the correlation between contraceptive devices/methods, and the superiority value of the contraceptive device/method in the population. each district/city. The problem with performing the analysis is the limitations of easy-to-use open source software. As with R, users must understand writing code to perform data analysis. Therefore, to perform a biplot analysis of the principal components, an RShiny application has been created using RStudio. The R-Shiny that has been made has many  advantages,  including  complete  results  which  include  data  display,  data transformation, SVD matrix, to graphs along with plot graph interpretation. The results of the principal component biplot analysis using R-Shiny with α =1 have the advantage of a good principal component biplot, which is 95.63%. This shows that the biplot interpretation of the main components produced can be explained well the relationship between the district/city and the contraceptive methods/devices used. 

scholarly journals FOCAL REGION-BASED VOLUME RENDERING

2006 ◽  
Vol 20 (05) ◽  
pp. 665-677 ◽  
Author(s):  
JIANLONG ZHOU ◽  
ZHIYAN WANG ◽  
KLAUS D. TÖNNIES
Keyword(s):  
Volume Rendering ◽  
Contextual Information ◽  
Focal Region ◽  
Ray Casting ◽  
Volumetric Data ◽  
Data Set ◽  
Volume Of Interest ◽  
Bounded Volume ◽  
Volume Ray Casting ◽  
Main Components

In this paper, a new approach named focal region-based volume rendering for visualizing internal structures of volumetric data is presented. This approach presents volumetric information through integrating context information as the structure analysis of the data set with a lens-like focal region rendering to show more detailed information. This feature-based approach contains three main components: (i) A feature extraction model using 3D image processing techniques to explore the structure of objects to provide contextual information; (ii) An efficient ray-bounded volume ray casting rendering to provide the detailed information of the volume of interest in the focal region; (iii) The tools used to manipulate focal regions to make this approach more flexible. The approach provides a powerful framework for producing detailed information from volumetric data. Providing contextual information and focal region renditions at the same time has the advantages of easy to understand and comprehend volume information for the scientist. The interaction techniques provided in this approach make the focal region-based volume rendering more flexible and easy to use.

scholarly journals Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2

2021 ◽  
Vol 288 (1963) ◽  
Author(s):  
Ilya R. Fischhoff ◽  
Adrian A. Castellanos ◽  
João P. G. L. M. Rodrigues ◽  
Arvind Varsani ◽  
Barbara A. Han
Keyword(s):  
Protein Interactions ◽  
Three Dimensional ◽  
Predictive Modelling ◽  
Molecular Data ◽  
Transmission Risk ◽  
Virus Protein ◽  
Biological Traits ◽  
Animal Populations ◽  
Order Of Magnitude

Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein–protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals—an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.

scholarly journals Integrating Transposable Elements in the 3D Genome

2019 ◽  
Author(s):  
Alexandros Bousios ◽  
Hans-Wilhelm Nuetzmann ◽  
Dorothy Buck ◽  
Davide Michieletto
Keyword(s):  
Transposable Elements ◽  
Cell Fate ◽  
Large Scale ◽  
Spatial Information ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Predictive Modelling ◽  
Polymer Physics ◽  
Modelling Framework ◽  
Polymer Folding

Chromosome organisation is increasingly recognised as an essential component of genome regulation, cell fate and cell health. Within the realm of transposable elements (TEs) however, the spatial information of how genomes are folded is still only rarely integrated in experimental studies or accounted for in modelling. Here, we propose a new predictive modelling framework for the study of the integration patterns of TEs based on extensions of widely employed polymer models for genome organisation. Whilst polymer physics is recognised as an important tool to understand the mechanisms of genome folding, we now show that it can also offer orthogonal and generic insights into the integration and distribution profiles (or “topography”) of TEs across organisms. Here, we present polymer physics arguments and molecular dynamics simulations on TEs inserting into heterogeneously flexible polymers and show with a simple model that polymer folding and local flexibility affects TE integration patterns. The preliminary discussion presented herein lay the foundations for a large-scale analysis of TE integration dynamics and topography as a function of the three-dimensional host genome.

scholarly journals Modelling infectious diseases with herd immunity in a randomly mixed population

2021 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M. Peariasamy ◽  
Hishamshah Mohd. Ibrahim ◽  
Noor Hisham Abdullah
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Transmission Rate ◽  
Herd Immunity ◽  
Sir Model ◽  
Mixed Population ◽  
Transmission Dynamics ◽  
Transmission Model ◽  
Infection Model ◽  
Vaccination Rates

Abstract Background The conventional susceptible-infectious-recovered (SIR) model tends to overestimate the transmission dynamics of infectious diseases and ends up with total infections and total immunized population exceeding the threshold required for control and eradication of infectious diseases. The study aims to overcome the limitation by allowing the transmission rate of infectious disease to decline along with the reducing risk of contact infection. Methods Two new SIR models were developed to mimic the declining transmission rate of infectious diseases at different stages of transmission. Model A mimicked the declining transmission rate along with the reducing risk of transmission following infection, while Model B mimicked the declining transmission rate following recovery. Then, the conventional SIR model, Model A and Model B were used to simulate an infectious disease with a basic reproduction number (r0) of 3.0 and a herd immunity threshold (HIT) of 0.667 with and without vaccination. The infectious disease was expected to be controlled or eradicated when the total immunized population either through infection or vaccination reached the level predicted by the HIT. Outcomes of simulations were assessed at the time when the total immunized population reached the level predicted by the HIT, and at the end of simulations. Findings All three models performed likewise at the beginning of the transmission when sizes of infectious and recovered were relatively small as compared with the population size. The infectious disease modelled using the conventional SIR model appeared completely out of control even when the HIT was achieved in all scenarios with and without vaccination. The infectious disease modelled using Model A appeared to be controlled at the level predicted by the HIT in all scenarios with and without vaccination. Model B projected the infectious disease to be controlled at the level predicted by the HIT only at high vaccination rates. At lower vaccination rates or without vaccination, the level at which the infectious disease was controlled cannot be accurately predicted by the HIT. Conclusion Transmission dynamics of infectious diseases with herd immunity can accurately be modelled by allowing the transmission rate of infectious disease to decline along with the combined risk of contact infection. Model B provides a more credible framework for modelling infectious diseases with herd immunity in a randomly mixed population.

Predictive Modeling of Vaccination Uptake in U.S. Counties: A Machine Learning-based Approach (Preprint)

2021 ◽  
Author(s):  
Queena Cheong ◽  
Martin Au-yeung ◽  
Stephanie Quon ◽  
Katsy Concepcion ◽  
Jude Dzevela Kong
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Predictive Modelling ◽  
Sociodemographic Factors ◽  
Vaccine Uptake ◽  
Vaccination Rates ◽  
Vaccination Uptake ◽  
Sociodemographic Data ◽  
Public Health Officials ◽  
The U.S

BACKGROUND While the COVID-19 pandemic has left an unprecedented impact globally, countries such as the United States of America have reported the most significant incidence of COVID-19 cases worldwide. Within the U.S., various sociodemographic factors have played an essential role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between U.S. counties, underscoring the need for efficient and accurate predictive modelling strategies to inform public health officials and reduce the burden on healthcare systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the U.S., vaccination rates have become stagnant, necessitating predictive modelling to identify important factors impacting vaccination uptake. OBJECTIVE To determine the association between sociodemographic factors and vaccine uptake across counties in the U.S. METHODS Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases, such as the U.S. Centre for Disease Control and U.S. Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data. RESULTS Our model predicted COVID-19 vaccination uptake across U.S. countries with 59% accuracy. In addition, it identified location, education, ethnicity, and income as the most critical sociodemographic features in predicting vaccination uptake in U.S. counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by healthcare authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns. CONCLUSIONS Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rate across counties in the U.S. and if leveraged appropriately can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.

scholarly journals Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology

Processes ◽  
2018 ◽  
Vol 6 (9) ◽  
pp. 167 ◽  
Author(s):  
Philipp Boeing ◽  
Miriam Leon ◽  
Darren Nesbeth ◽  
Anthony Finkelstein ◽  
Chris Barnes
Keyword(s):  
Synthetic Biology ◽  
Contextual Information ◽  
Dependent Manner ◽  
System Construction ◽  
Modelling Framework ◽  
Contextual Design ◽  
Reaction Models ◽  
Synthetic Circuit ◽  
Oriented Approach ◽  
Proof Of Principle

Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issues—either compositional, host or environmental—will be key to realising more complex applications. Here, we present a design framework that radically steps away from a purely parts-based paradigm by using aspect-oriented software engineering concepts. We believe that the notion of concerns is a powerful and biologically credible way of thinking about system synthesis. By adopting this approach, we can separate core concerns, which represent modular aims of the design, from cross-cutting concerns, which represent system-wide attributes. The explicit handling of cross-cutting concerns allows for contextual information to enter the design process in a modular way. As a proof-of-principle, we implemented the aspect-oriented approach in the Python tool, SynBioWeaver, which enables the combination, or weaving, of core and cross-cutting concerns. The power and flexibility of this framework is demonstrated through a number of examples covering the inclusion of part context, combining circuit designs in a context dependent manner, and the generation of rule, logic and reaction models from synthetic circuit designs.

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