scholarly journals A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality

2021 ◽  
Vol 376 (1829) ◽  
pp. 20200272
Author(s):  
Leon Danon ◽  
Ellen Brooks-Pollock ◽  
Mick Bailey ◽  
Matt Keeling
Keyword(s):  
Seasonal Changes ◽  
Capacity Planning ◽  
Census Data ◽  
Transmission Rate ◽  
Parameter Estimates ◽  
Peak Time ◽  
England And Wales ◽  
Minimal Impact ◽  
Starting Location ◽  
The Impact

An outbreak of a novel coronavirus was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales. We used 2011 census data to inform population sizes and movements, together with parameter estimates from the outbreak in China. We predict that the epidemic will peak 126 to 147 days (approx. 4 months) after the start of person-to-person transmission in the absence of controls. Assuming biological parameters remain unchanged and transmission persists from February, we expect the peak to occur in June. Starting location and model stochasticity have a minimal impact on peak timing. However, realistic parameter uncertainty leads to peak time estimates ranging from 78 to 241 days following sustained transmission. Seasonal changes in transmission rate can substantially impact the timing and size of the epidemic. We provide initial estimates of the epidemic potential of COVID-19. These results can be refined with more precise parameters. Seasonal changes in transmission could shift the timing of the peak into winter, with important implications for healthcare capacity planning. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK.

scholarly journals A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing

2020 ◽  
Author(s):  
Leon Danon ◽  
Ellen Brooks-Pollock ◽  
Mick Bailey ◽  
Matt Keeling
Keyword(s):  
Seasonal Changes ◽  
Capacity Planning ◽  
Census Data ◽  
Transmission Rate ◽  
Control Measures ◽  
Parameter Estimates ◽  
Peak Time ◽  
England And Wales ◽  
Minimal Impact ◽  
Starting Location

AbstractBackgroundAn outbreak of a novel coronavirus, named CoVID-19, was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable cases of human-to-human transmission in England.MethodsWe adapted an existing national-scale metapopulation model to capture the spread of CoVID-19 in England and Wales. We used 2011 census data to capture population sizes and population movement, together with parameter estimates from the current outbreak in China.ResultsWe predict that a CoVID-19 outbreak will peak 126 to 147 days (∼4 months) after the start of person-to-person transmission in England and Wales in the absence of controls, assuming biological parameters remain unchanged. Therefore, if person-to-person transmission persists from February, we predict the epidemic peak would occur in June. The starting location has minimal impact on peak timing, and model stochasticity varies peak timing by 10 days. Incorporating realistic parameter uncertainty leads to estimates of peak time ranging from 78 days to 241 days after person-to-person transmission has been established. Seasonal changes in transmission rate substantially impact the timing and size of the epidemic peak, as well as the total attack rate.DiscussionWe provide initial estimates of the potential course of CoVID-19 in England and Wales in the absence of control measures. These results can be refined with improved estimates of epidemiological parameters, and permit investigation of control measures and cost effectiveness analyses. Seasonal changes in transmission rate could shift the timing of the peak into winter months, which will have important implications for health-care capacity planning.

scholarly journals Reassessing the evidence for universal school-age BCG vaccination in England and Wales: re-evaluating and updating a modelling study

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e031573
Author(s):  
Sam Abbott ◽  
Hannah Christensen ◽  
Ellen Brooks-Pollock
Keyword(s):  
Policy Change ◽  
Parameter Uncertainty ◽  
Incidence Rates ◽  
Parameter Estimates ◽  
School Age ◽  
England And Wales ◽  
Bcg Vaccination ◽  
Methodological Flaw ◽  
Notification Data ◽  
The Impact

ObjectivesIn 2005, England and Wales switched from universal BCG vaccination against tuberculosis (TB) disease for school-age children to targeted vaccination of neonates. We aimed to recreate and re-evaluate a previously published model, the results of which informed this policy change.DesignWe recreated an approach for estimating the impact of ending the BCG schools scheme, correcting a methodological flaw in the model, updating the model with parameter uncertainty and improving parameter estimates where possible. We investigated scenarios for the assumed annual decrease in TB incidence rates considered by the UK’s Joint Committee on Vaccination and Immunisation and explored alternative scenarios using notification data.SettingEngland and Wales.Outcome measuresThe number of vaccines needed to prevent a single notification and the average annual additional notifications caused by ending the policy change.ResultsThe previously published model was found to contain a methodological flaw and to be spuriously precise. It greatly underestimated the impact of ending school-age vaccination compared with our updated, corrected model. The updated model produced predictions with wide CIs when parameter uncertainty was included. Model estimates based on an assumption of an annual decrease in TB incidence rates of 1.9% were closest to those estimated using notification data. Using this assumption, we estimate that 1600 (2.5; 97.5% quantiles: 1300, 2000) vaccines would have been required to prevent a single notification in 2004.ConclusionsThe impact of ending the BCG schools scheme was found to be greater than previously thought when notification data were used. Our results highlight the importance of independent evaluations of modelling evidence, including uncertainty, and evaluating multiple scenarios when forecasting the impact of changes in vaccination policy.

scholarly journals Disentangling the roles of human mobility and deprivation on the transmission dynamics of COVID-19 using a spatially explicit simulation model

2020 ◽  
Author(s):  
C.J. Banks ◽  
E. Colman ◽  
T. Doherty ◽  
O. Tearne ◽  
M. Arnold ◽  
...  
Keyword(s):  
Simulation Model ◽  
Transmission Rate ◽  
Human Mobility ◽  
Long Distance ◽  
Substantial Impact ◽  
Minimal Impact ◽  
Early Stages ◽  
Health Related ◽  
Approximate Bayesian ◽  
The Impact

SummaryRestrictions on mobility are a key component of infectious disease controls, preventing the spread of infections to as yet unexposed areas, or to regions which have previously eliminated outbreaks. However, even under the most severe restrictions, some travel must inevitably continue, at the very minimum to retain essential services. For COVID-19, most countries imposed severe restrictions on travel at least as soon as it was clear that containment of local outbreaks would not be possible. Such restrictions are known to have had a substantial impact on the economy and other aspects of human health, and so quantifying the impact of such restrictions is an essential part of evaluating the necessity for future implementation of similar measures.In this analysis, we built a simulation model using National statistical data to record patterns of movements to work, and implement levels of mobility recorded in real time via mobile phone apps. This model was fitted to the pattern of deaths due to COVID-19 using approximate Bayesian inference. Our model is able to recapitulate mortality considering the number of deaths and datazones (DZs, which are areas containing approximately 500-1000 residents) with deaths, as measured across 32 individual council areas (CAs) in Scotland. Our model recreates a trajectory consistent with the observed data until 1st of July. According to the model, most transmission was occurring “locally” (i.e. in the model, 80% of transmission events occurred within spatially defined “communities” of approximately 100 individuals). We show that the net effect of the various restrictions put into place in March can be captured by a reduction in transmission down to 12% of its pre-lockdown rate effective 28th March. By comparing different approaches to reducing transmission, we show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not reduce death rates significantly. As this movement of individuals from more infected areas to less infected areas has a minimal impact on transmission, this suggests that the fraction of population already immune in infected communities was not a significant factor in these early stages of the national epidemic even when local clustering of infection is taken into account.The best fit model also shows a considerable influence of the health index of deprivation (part of the “index of multiple deprivations”) on mortality. The most likely value has the CA with the highest level of health-related deprivation to have on average, a 2.45 times greater mortality rate due to COVID-19 compared to the CA with the lowest, showing the impact of health-related deprivation even in the early stages of the COVID-19 national epidemic.

scholarly journals Bayesian Source Attribution of Salmonella Typhimurium Isolates From Human Patients and Farm Animals in England and Wales

2021 ◽  
Vol 12 ◽  
Author(s):  
Mark Arnold ◽  
Richard Piers Smith ◽  
Yue Tang ◽  
Jaromir Guzinski ◽  
Liljana Petrovska
Keyword(s):  
Genetic Distance ◽  
Sequence Data ◽  
Human Infection ◽  
Farm Animals ◽  
Clustering Methods ◽  
Source Attribution ◽  
England And Wales ◽  
Minimal Impact ◽  
Frequency Matching ◽  
The Impact

The purpose of the study was to apply a Bayesian source attribution model to England and Wales based data on Salmonella Typhimurium (ST) and monophasic variants (MST), using different subtyping approaches based on sequence data. The data consisted of laboratory confirmed human cases and mainly livestock samples collected from surveillance or monitoring schemes. Three different subtyping methods were used, 7-loci Multi-Locus Sequence Typing (MLST), Core-genome MLST, and Single Nucleotide Polymorphism distance, with the impact of varying the genetic distance over which isolates would be grouped together being varied for the latter two approaches. A Bayesian frequency matching method, known as the modified Hald method, was applied to the data from each of the subtyping approaches. Pigs were found to be the main contributor to human infection for ST/MST, with approximately 60% of human cases attributed to them, followed by other mammals (mostly horses) and cattle. It was found that the use of different clustering methods based on sequence data had minimal impact on the estimates of source attribution. However, there was an impact of genetic distance over which isolates were grouped: grouping isolates which were relatively closely related increased uncertainty but tended to have a better model fit.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

The Impact of a Potential Public/Private Initiative on a Defined Region of the South Suburban Chicago Metropolitan Area

2019 ◽  
pp. 14-32
Keyword(s):  
Supply Chain ◽  
Economic Impact ◽  
Impact Analysis ◽  
Census Data ◽  
Economic Benefits ◽  
Local Economy ◽  
Market Area ◽  
State And Local ◽  
The University ◽  
The Impact

The university is considered one of the engines of growth in a local economy or its market area, since its direct contributions consist of 1) employment of faculty and staff, 2) services to students, and supply chain links vendors, all of which define the University’s Market area. Indirect contributions consist of those agents associated with the university in terms of community and civic events. Each of these activities represent economic benefits to their host communities and can be classified as the economic impact a university has on its local economy and whose spatial market area includes each of the above agents. In addition are the critical links to the University, which can be considered part of its Demand and Supply chain. This paper contributes to the field of Public/Private Impact Analysis, which is used to substantiate the social and economic benefits of cooperating for economic resources. We use Census data on Output of Goods and Services, Labor Income on Salaries, Wages and Benefits, Indirect State and Local Taxes, Property Tax Revenue, Population, and Inter-Industry to measure economic impact (Implan, 2016).

The Digital Divide and K-12 Student Computer Use

10.28945/2926 ◽  
2005 ◽  
Author(s):  
James N. Morgan ◽  
Craig A. VanLengen
Keyword(s):  
Digital Divide ◽  
Computer Use ◽  
Census Data ◽  
Internet Access ◽  
Demographic Characteristics ◽  
The Internet ◽  
Substantial Impact ◽  
Limited Basis ◽  
K 12 ◽  
The Impact

The divide between those who have computer and Internet access and those who do not appears to be narrowing, however overall statistics may be misleading. Measures of computer availability in schools often include cases where computers are only available for administration or are available only on a very limited basis (Gootman, 2004). Access to a computer and the Internet outside of school helps to reinforce student learning and emphasize the importance of using technology. Recent U.S. statistics indicate that ethnic background and other demographic characteristics still have substantial impact on the availability and use of computers by students outside of the classroom. This paper examines recent census data to determine the impact of the household on student computer use outside of the classroom. Encouragingly, the findings of this study suggest that use of a computer at school substantially increases the chance that a student will use a computer outside of class. Additionally, this study suggests that computer use outside of the classroom is positively and significantly impacted by being in a household with adults who either use a computer at work or work in an industry where computers are extensively used.

scholarly journals Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xi Huo ◽  
Jing Chen ◽  
Shigui Ruan
Keyword(s):  
Disease Transmission ◽  
Large Scale ◽  
Transmission Rate ◽  
Control Strategies ◽  
Herd Immunity ◽  
Antibody Prevalence ◽  
Screening Process ◽  
Vaccination Programs ◽  
Prevalence Level ◽  
The Impact

Abstract Background The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. Methods By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. Results We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. Conclusions We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.

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