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

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.

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

10.1101/2021.05.17.21256818 ◽

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.

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A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures

Epidemiology and Psychiatric Sciences ◽

10.1017/s2045796020001080 ◽

2021 ◽

Vol 30 ◽

Author(s):

Jordan Edwards ◽

A. Demetri Pananos ◽

Amardeep Thind ◽

Saverio Stranges ◽

Maria Chiu ◽

...

Keyword(s):

Monte Carlo ◽

Anxiety Disorders ◽

Administrative Data ◽

Population Based ◽

Accurate Estimate ◽

Service Planning ◽

Prevalence Estimate ◽

Population Prevalence ◽

Mood And Anxiety Disorders ◽

Prevalence Estimates

Abstract Aims There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. Methods We used data from the 2012 Canadian Community Health Survey – Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. Results The combined prevalence mean was 8.6%, with a credible interval of 6.8–10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. Conclusions Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

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Design and Estimation for the Population Prevalence of Infectious Diseases

10.1101/2021.02.05.21251231 ◽

2021 ◽

Author(s):

Eric J. Oh ◽

Alyssa Mikytuck ◽

Vicki Lancaster ◽

Joshua Goldstein ◽

Sallie Keller

Keyword(s):

Numerical Study ◽

Disease Outbreaks ◽

Sensitivity Analyses ◽

Multilevel Regression ◽

Infectious Disease Outbreaks ◽

Population Prevalence ◽

Prevalence Estimates ◽

Relative Robustness ◽

Biased Estimates ◽

Limited Testing

AbstractUnderstanding the prevalence of infections in the population of interest is critical for making data-driven public health responses to infectious disease outbreaks. Accurate prevalence estimates, however, can be difficult to calculate due to a combination of low population prevalence, imperfect diagnostic tests, and limited testing resources. In addition, strategies based on convenience samples that target only symptomatic or high-risk individuals will yield biased estimates of the population prevalence. We present Bayesian multilevel regression and poststratification models that incorporate probability sampling designs, the sensitivity and specificity of a diagnostic test, and specimen pooling to obtain unbiased prevalence estimates. These models easily incorporate all available prior information and can yield reasonable inferences even with very low base rates and limited testing resources. We examine the performance of these models with an extensive numerical study that varies the sampling design, sample size, true prevalence, and pool size. We also demonstrate the relative robustness of the models to key prior distribution assumptions via sensitivity analyses.

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Abstract TP86: Prevalence of Bicuspid Aortic Valves and Thoracic Ascending Aortic Aneurysms in Patients Treated for Intracranial Aneurysms

Stroke ◽

10.1161/str.44.suppl_1.atp86 ◽

2013 ◽

Vol 44 (suppl_1) ◽

Author(s):

Manu S Goyal ◽

Ravi Gottumukkala ◽

Sanjeev Bhalla ◽

Andrew M Kates ◽

Gregory J Zipfel ◽

...

Keyword(s):

Intracranial Aneurysms ◽

Aortic Aneurysms ◽

Chest Imaging ◽

Aortic Valves ◽

Cross Sectional ◽

Population Prevalence ◽

Prevalence Estimates ◽

Bicuspid Aortic Valves ◽

And Gender ◽

Clinical Records

INTRODUCTION: Prior studies have suggested an increased prevalence of intracranial aneurysms (ICA) in patients with bicuspid aortic valves (BAV) and thoracic ascending aortic aneurysms (TAA). We tested the hypothesis that if true, conversely, the prevalence of BAV and TAA should be increased in patients with ICA. METHODS: We identified and reviewed the clinical records of patients treated for an ICA at our institution between 2002 and 2011. Only those with an echocardiogram report in their clinical record were included for further analysis. The prevalence of BAV and TAA in this cohort was determined based on the echocardiogram reports, medical records, and, when available, cross-sectional chest imaging. Proportion confidence intervals were calculated and compared to population prevalence estimates. RESULTS: Of 1051 patients with ICA, 321 had available echocardiogram reports that included assessment of the aortic valve. Of these, 85 also had cross-sectional chest imaging. Of the 321 patients, 2 were reported to have a BAV (0.6%, 95% CI 0 - 2.5%), similar to population prevalence estimates for this condition. In this same cohort, there were 15 (4.7%, 95% CI 2.7 - 7.8%) patients with a TAA. Among the 85 patients with cross-sectional chest imaging, the prevalence of TAA exceeded that expected for a community age- and gender-matched population (see Figure, gray lines represent upper and lower limits of normal). CONCLUSIONS: This retrospective analysis suggests an increased prevalence of TAA, but not BAV, in patients with ICA.

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The basic reproduction number

Mathematical Tools for Understanding Infectious Disease Dynamics ◽

10.23943/princeton/9780691155395.003.0007 ◽

2012 ◽

Author(s):

Odo Diekmann ◽

Hans Heesterbeek ◽

Tom Britton

Keyword(s):

Infectious Disease ◽

Basic Reproduction Number ◽

Reproduction Number ◽

Infected Individual ◽

Vital Role ◽

Point Of View ◽

Epidemiological Models ◽

Disease Epidemiology ◽

Infectious Disease Epidemiology ◽

The Basic Reproduction Number

The basic reproduction number (or ratio) R₀ is arguably the most important quantity in infectious disease epidemiology. It is among the quantities most urgently estimated for infectious diseases in outbreak situations, and its value provides insight when designing control interventions for established infections. From a theoretical point of view R₀ plays a vital role in the analysis of, and consequent insight from, infectious disease models. There is hardly a paper on dynamic epidemiological models in the literature where R₀ does not play a role. R₀ is defined as the average number of new cases of an infection caused by one typical infected individual, in a population consisting of susceptibles only. This chapter shows how R₀ can be characterized mathematically and provides detailed examples of its calculation in terms of parameters of epidemiological models, culminating in a set of algorithms (or “recipes”) for the calculation for compartmental epidemic systems.

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Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17093054 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3054 ◽

Cited By ~ 14

Author(s):

Oyelola A. Adegboye ◽

Adeshina I. Adekunle ◽

Ezra Gayawan

Keyword(s):

Doubling Time ◽

Reproduction Number ◽

Infected Individual ◽

Credible Interval ◽

World Health ◽

Time Interval ◽

Serial Interval ◽

Imported Cases ◽

Novel Coronavirus ◽

Health Organization

On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05–0.10) with a doubling time of 9.84 days (95% CI: 7.28–15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65–8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83–7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26–1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

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GPT2 mutations in autosomal recessive developmental disability: extending the clinical phenotype and population prevalence estimates

Human Genetics ◽

10.1007/s00439-019-02057-x ◽

2019 ◽

Vol 138 (10) ◽

pp. 1183-1200

Author(s):

Qing Ouyang ◽

Brian C. Kavanaugh ◽

Lena Joesch-Cohen ◽

Bethany Dubois ◽

Qing Wu ◽

...

Keyword(s):

Developmental Disability ◽

Autosomal Recessive ◽

Clinical Phenotype ◽

Population Prevalence ◽

Prevalence Estimates

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Relative Importance of Ixodes ricinus and Ixodes trianguliceps as Vectors for Anaplasma phagocytophilum and Babesia microti in Field Vole (Microtus agrestis) Populations

Applied and Environmental Microbiology ◽

10.1128/aem.00625-08 ◽

2008 ◽

Vol 74 (23) ◽

pp. 7118-7125 ◽

Cited By ~ 81

Author(s):

K. J. Bown ◽

X. Lambin ◽

G. R. Telford ◽

N. H. Ogden ◽

S. Telfer ◽

...

Keyword(s):

United Kingdom ◽

Ixodes Ricinus ◽

Anaplasma Phagocytophilum ◽

Babesia Microti ◽

Infection Prevalence ◽

Microtus Agrestis ◽

Relative Importance ◽

The United Kingdom ◽

Ixodes Trianguliceps ◽

Larval Burden

ABSTRACT The importance of Ixodes ricinus in the transmission of tick-borne pathogens is well recognized in the United Kingdom and across Europe. However, the role of coexisting Ixodes species, such as the widely distributed species Ixodes trianguliceps, as alternative vectors for these pathogens has received little attention. This study aimed to assess the relative importance of I. ricinus and I. trianguliceps in the transmission of Anaplasma phagocytophilum and Babesia microti among United Kingdom field voles (Microtus agrestis), which serve as reservoir hosts for both pathogens. While all instars of I. trianguliceps feed exclusively on small mammals, I. ricinus adults feed primarily on larger hosts such as deer. The abundance of both tick species and pathogen infection prevalence in field voles were monitored at sites surrounded with fencing that excluded deer and at sites where deer were free to roam. As expected, fencing significantly reduced the larval burden of I. ricinus on field voles and the abundance of questing nymphs, but the larval burden of I. trianguliceps was not significantly affected. The prevalence of A. phagocytophilum and B. microti infections was not significantly affected by the presence of fencing, suggesting that I. trianguliceps is their principal vector. The prevalence of nymphal and adult ticks on field voles was also unaffected, indicating that relatively few non-larval I. ricinus ticks feed upon field voles. This study provides compelling evidence for the importance of I. trianguliceps in maintaining these enzootic tick-borne infections, while highlighting the potential for such infections to escape into alternative hosts via I. ricinus.

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The Global Prevalence of Strongyloides stercoralis Infection

Pathogens ◽

10.3390/pathogens9060468 ◽

2020 ◽

Vol 9 (6) ◽

pp. 468 ◽

Cited By ~ 6

Author(s):

Dora Buonfrate ◽

Donal Bisanzio ◽

Giovanni Giorli ◽

Peter Odermatt ◽

Thomas Fürst ◽

...

Keyword(s):

Strongyloides Stercoralis ◽

South East Asia ◽

Climatic Zones ◽

Prevalence Data ◽

Infected People ◽

Global Prevalence ◽

Population Prevalence ◽

And Control ◽

High Uncertainty ◽

True Population

Strongyloidiasis is a common neglected tropical disease in tropical and sub-tropical climatic zones. At the worldwide level, there is high uncertainty about the strongyloidiasis burden. This uncertainty represents an important knowledge gap since it affects the planning of interventions to reduce the burden of strongyloidiasis in endemic countries. This study aimed to estimate the global strongyloidiasis prevalence. A literature review was performed to obtain prevalence data from endemic countries at a worldwide level from 1990 to 2016. For each study, the true population prevalence was calculated by accounting for the specificity and the sensitivity of testing and age of tested individuals. Prediction of strongyloidiasis prevalence for each country was performed using a spatiotemporal statistical modeling approach. The country prevalence obtained from the model was used to estimate the number of infected people per country. We estimate the global prevalence of strongyloidiasis in 2017 to be 8.1% (95% CI: 4.2–12.4%), corresponding to 613.9 (95% CI: 313.1–910.1) million people infected. The South-East Asia, African, and Western Pacific Regions accounted for 76.1% of the global infections. Our results could be used to identify those countries in which strongyloidiasis prevalence is highest and where mass drug administration (MDA) should be deployed for its prevention and control.

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