scholarly journals Local prevalence of transmissible SARS-CoV-2 infection : an integrative causal model for debiasing fine-scale targeted testing data

2021 ◽  
Author(s):  
George Nicholson ◽  
Brieuc CL Lehmann ◽  
Tullia Padellini ◽  
Koen B Pouwels ◽  
Radka Jersakova ◽  
...  
Keyword(s):  
Causal Model ◽  
Reproduction Number ◽  
Reproductive Number ◽  
Fine Scale ◽  
Testing Data ◽  
Targeted Testing ◽  
Causal Framework ◽  
The Uk ◽  
Rapid Deployment ◽  
Selection Of

Targeted surveillance testing schemes for SARS-CoV-2 focus on certain subsets of the population, such as individuals experiencing one or more of a prescribed list of symptoms. These schemes have routinely been used to monitor the spread of SARS-CoV-2 in countries across the world. The number of positive tests in a given region can provide local insights into important epidemiological parameters, such as prevalence and effective reproduction number. Moreover, targeted testing data has been used inform the deployment of localised non-pharmaceutical interventions. However, surveillance schemes typically suffer from ascertainment bias; the individuals who are tested are not necessarily representative of the wider population of interest. Here, we show that data from randomised testing schemes, such as the REACT study in the UK, can be used to debias fine-scale targeted testing data in order to provide accurate localised estimates of the number of infectious individuals. We develop a novel, integrative causal framework that explicitly models the process underlying the selection of individuals for targeted testing. The output from our model can readily be incorporated into longitudinal analyses to provide local estimates of the reproduction number. We apply our model to characterise the size of the infectious population in England between June 2020 and January 2021. Our local estimates of the effective reproduction number are predictive of future changes in positive case numbers. We also capture local increases in both prevalence and effective reproductive number in the South East from November 2020 to December 2020, reflecting the spread of the Kent variant. Preparations for future epidemics should ensure the rapid deployment of both types of schemes to accurately monitor the spread of emerging and ongoing infectious diseases.

scholarly journals Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

2021 ◽  
Author(s):  
George Nicholson ◽  
Brieuc Lehmann ◽  
Tullia Padellini ◽  
Koen B. Pouwels ◽  
Radka Jersakova ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Infected Individual ◽  
Infection Prevalence ◽  
Statistical Accuracy ◽  
The United Kingdom ◽  
Population Prevalence ◽  
Prevalence Estimates ◽  
Targeted Testing ◽  
Causal Framework ◽  
True Population

AbstractGlobal and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.

A Court of Specialists

2020 ◽  
Author(s):  
Chris Hanretty
Keyword(s):  
Family Law ◽  
Public Law ◽  
Tax Law ◽  
Decision Making Process ◽  
Main Argument ◽  
Political Differences ◽  
Uk Supreme Court ◽  
The Uk ◽  
Initial Selection ◽  
Selection Of

This book explains how judges on the UK Supreme Court behave. It looks at different stages in the court's decision-making process—from the initial selection of cases, to the choice of judges to sit on panels, to the final outcome. The main argument of the book is that judges' behavior is strongly affected by their specialism in different areas of law. Cases in tax law (or family law, or public law) are more likely to be heard by specialists in that area, and those specialists are more likely to write the court's decision—or disagree with the decision when there is dissent. Legal factors like specialization in areas of law explains more of the court's work than do political differences between judges.

scholarly journals Modelling the impact of the tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e050346
Author(s):  
Daniel J Laydon ◽  
Swapnil Mishra ◽  
Wes R Hinsley ◽  
Pantelis Samartsidis ◽  
Seth Flaxman ◽  
...  
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Secondary Outcome ◽  
Bayesian Hierarchical ◽  
Time Reproduction ◽  
Tier System ◽  
The Uk ◽  
National Trends ◽  
Tier 3 ◽  
The Impact

ObjectiveTo measure the effects of the tier system on the COVID-19 pandemic in the UK between the first and second national lockdowns, before the emergence of the B.1.1.7 variant of concern.DesignThis is a modelling study combining estimates of real-time reproduction number Rt (derived from UK case, death and serological survey data) with publicly available data on regional non-pharmaceutical interventions. We fit a Bayesian hierarchical model with latent factors using these quantities to account for broader national trends in addition to subnational effects from tiers.SettingThe UK at lower tier local authority (LTLA) level. 310 LTLAs were included in the analysis.Primary and secondary outcome measuresReduction in real-time reproduction number Rt.ResultsNationally, transmission increased between July and late September, regional differences notwithstanding. Immediately prior to the introduction of the tier system, Rt averaged 1.3 (0.9–1.6) across LTLAs, but declined to an average of 1.1 (0.86–1.42) 2 weeks later. Decline in transmission was not solely attributable to tiers. Tier 1 had negligible effects. Tiers 2 and 3, respectively, reduced transmission by 6% (5%–7%) and 23% (21%–25%). 288 LTLAs (93%) would have begun to suppress their epidemics if every LTLA had gone into tier 3 by the second national lockdown, whereas only 90 (29%) did so in reality.ConclusionsThe relatively small effect sizes found in this analysis demonstrate that interventions at least as stringent as tier 3 are required to suppress transmission, especially considering more transmissible variants, at least until effective vaccination is widespread or much greater population immunity has amassed.

MODELING THE IMPACT OF VOLUNTARY TESTING AND TREATMENT ON TUBERCULOSIS TRANSMISSION DYNAMICS

2012 ◽  
Vol 05 (04) ◽  
pp. 1250029 ◽  
Author(s):  
S. MUSHAYABASA ◽  
C. P. BHUNU
Keyword(s):  
Deterministic Model ◽  
Reproduction Number ◽  
Positive Impact ◽  
Transmission Dynamics ◽  
Effective Control ◽  
Reproductive Number ◽  
Voluntary Testing ◽  
Manifold Theory ◽  
The Impact ◽  
Disease Free

A deterministic model for evaluating the impact of voluntary testing and treatment on the transmission dynamics of tuberculosis is formulated and analyzed. The epidemiological threshold, known as the reproduction number is derived and qualitatively used to investigate the existence and stability of the associated equilibrium of the model system. The disease-free equilibrium is shown to be locally-asymptotically stable when the reproductive number is less than unity, and unstable if this threshold parameter exceeds unity. It is shown, using the Centre Manifold theory, that the model undergoes the phenomenon of backward bifurcation where the stable disease-free equilibrium co-exists with a stable endemic equilibrium when the associated reproduction number is less than unity. The analysis of the reproduction number suggests that voluntary tuberculosis testing and treatment may lead to effective control of tuberculosis. Furthermore, numerical simulations support the fact that an increase voluntary tuberculosis testing and treatment have a positive impact in controlling the spread of tuberculosis in the community.

Fine-scale habitat selection of American marten at the southern fringe of the boreal forest

Ecoscience ◽  
2010 ◽  
Vol 17 (2) ◽  
pp. 175-185 ◽  
Author(s):  
Guillaume Godbout ◽  
Jean-Pierre Ouellet
Keyword(s):  
Habitat Selection ◽  
Boreal Forest ◽  
Fine Scale ◽  
American Marten ◽  
Southern Fringe ◽  
Selection Of

scholarly journals Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
James D. Munday ◽  
Christopher I. Jarvis ◽  
Amy Gimma ◽  
Kerry L. M. Wong ◽  
Kevin van Zandvoort ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Transmission Rate ◽  
Transmission Risk ◽  
Individual Level ◽  
Face To Face ◽  
School Settings ◽  
Contact Rates ◽  
Contact Survey ◽  
The Uk ◽  
The Impact

Abstract Background Schools were closed in England on 4 January 2021 as part of increased national restrictions to curb transmission of SARS-CoV-2. The UK government reopened schools on 8 March. Although there was evidence of lower individual-level transmission risk amongst children compared to adults, the combined effects of this with increased contact rates in school settings and the resulting impact on the overall transmission rate in the population were not clear. Methods We measured social contacts of > 5000 participants weekly from March 2020, including periods when schools were both open and closed, amongst other restrictions. We combined these data with estimates of the susceptibility and infectiousness of children compared with adults to estimate the impact of reopening schools on the reproduction number. Results Our analysis indicates that reopening all schools under the same measures as previous periods that combined lockdown with face-to-face schooling would be likely to increase the reproduction number substantially. Assuming a baseline of 0.8, we estimated a likely increase to between 1.0 and 1.5 with the reopening of all schools or to between 0.9 and 1.2 reopening primary or secondary schools alone. Conclusion Our results suggest that reopening schools would likely halt the fall in cases observed between January and March 2021 and would risk a return to rising infections, but these estimates relied heavily on the latest estimates or reproduction number and the validity of the susceptibility and infectiousness profiles we used at the time of reopening.

scholarly journals Forecasting of COVID-19 Reproduction Number by ARIMA Methodology and Quantile Estimation based on Best Fit Distribution by L- moments for Top-10 Affected Countries

2021 ◽  
Author(s):  
Muhammad Waqas ◽  
Songhua Xu ◽  
Linyun Zhou
Keyword(s):  
Reproduction Number ◽  
Quantile Estimation ◽  
The Usa ◽  
L Moments ◽  
Short And Long Term ◽  
Long Term Forecasting ◽  
Novel Coronavirus ◽  
The Uk ◽  
Health Experts

Abstract We utilized the average weekly estimated reproduction number data of COVID-19 from March (2020–2021). By applying ARIMA and L-moments methodology, short-and-long-term forecasting of R0 is made for Govt. officials and public health experts to take before-time policy measures to control the spread of novel coronavirus. This study helps medical staff to measure the expected demand of COVID-19 vaccine doses. We applied various ARIMA models on each country’s data and the best selected based on RMSE, AIC, and BIC for point and interval forecasting. Application L-Moments techniques selected GLO, GEV, and GNO distributions and quantile estimation with return period calculations. The forecasting shows that maximum countries mean R0 > 1, which is still a serious threat and can lead to heath disaster. The forecasting provided an alarming situation in the coming months for India, France, Turkey, and Spain; health experts should take strict measures because the cases rise due to the high R0 forecast. The USA, Russia, and the UK mean R0 will not suddenly increase; these countries consistent in COVID-19 R0 control. We find that even the significant population differences prevail among selected countries, the R0 is still high in maximum countries, so its a dire need to take strict control actions to minimize the R0 for public safety.

scholarly journals Reducing the Complexity of Casual Representation in Bayesian Belief Network

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1243
Author(s):  
Yit Yin Wee ◽  
Shing Chiang Tan ◽  
KuokKwee Wee
Keyword(s):  
Causal Model ◽  
Knowledge Engineering ◽  
Joint Probability ◽  
Bayesian Belief Network ◽  
Joint Probability Distribution ◽  
Causal Knowledge ◽  
Belief Network ◽  
Track Record ◽  
Probability Table ◽  
Causal Framework

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool.   Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model.  Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN.   Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

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