A Bayesian framework for incorporating multiple data sources and heterogeneity in the analysis of infectious disease outbreaks

When an outbreak of an infectious disease occurs, public health officials need to understand the dynamics of disease transmission in order to launch an effective response. Two quantities that are often used to describe transmission are the basic reproductive number and the distribution of the serial interval. The basic reproductive number, R0, is the average number of secondary cases a primary case will infect, assuming a completely susceptible population. The serial interval (SI) provides a measure of temporality, and is defined as the time between symptom onset between a primary case and its secondary case. Investigators typically collect outbreak data in the form of an epidemic curve that displays the number of cases by each day (or other time scale) of the outbreak. Occasionally the epidemic curve data is more expansive and includes demographic or other information. A contact trace sample may also be collected, which is based on a sample of the cases that have their contact patterns traced to determine the timing and sequence of transmission. In addition, numerous large scale social mixing surveys have been administered in recent years to collect information about contact patterns and infection rates among different age groups. These are readily available and are sometimes used to account for population heterogeneity. In this dissertation, we modify the methods presented in White and Pagano (2008) to account for additional data beyond the epidemic curve to estimate R0 and SI. We present two approaches that incorporate these data through the use of a Bayesian framework. First, we consider informing the prior distribution of the SI with contact trace data and examine implications of combining data that are in conflict. The second approach extends the first approach to account for heterogeneity in the estimation of R0. We derive a modification to the White and Pagano likelihood function and utilize social mixing surveys to inform the prior distributions of R0. Both approaches are assessed through a simulation study and are compared to alternative approaches, and are applied to real outbreak data from the 2003 SARS outbreak in Hong Kong and Singapore, and the influenza A(H1N1)2009pdm outbreak in South Africa

The impact of prior information on estimates of disease transmissibility using Bayesian tools

The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R0 = 1.36-1.46, μ = 2.0-2.7, μ = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample

Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials

Most clinical trials evaluating coronavirus disease 2019 (COVID-19) therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repeated measures (MMRM) with single imputation for results below assay lower limits of quantification (LLoQ). Analyzing changes in viral RNA levels with singly imputed values can lead to biased estimates of treatment effects. In this article, using an illustrative example from the ACTIV-2 trial, we highlight potential pitfalls of imputation when using ANCOVA or MMRM methods, and illustrate how these methods can be used when considering value

Long COVID After Bamlanivimab Treatment

BackgroundProspective evaluations of long COVID in outpatients with coronavirus disease 2019 (COVID-19) are lacking. We aimed to determine the frequency and predictors of long COVID after treatment with the monoclonal antibody bamlanivimab in ACTIV-2/A5401.MethodsData were analyzed from participants who received bamlanivimab 700 mg in ACTIV-2 from October 2020 to February 2021. Long COVID was defined as the presence of self-assessed COVID symptoms at week 24. Self-assessed return to pre-COVID health was also examined. Associations were assessed by regression models.ResultsAmong 506 participants, median age was 51 years. Half were female, 5% Black/African American, and 36% Hispanic/Latino. At 24 weeks, 18% reported long COVID and 15% had not returned to pre-COVID health. Smoking (adjusted risk ratio [aRR], 2.41 [95% confidence interval {CI}, 1.34- 4.32]), female sex (aRR, 1.91 [95% CI, 1.28-2.85]), non-Hispanic ethnicity (aRR, 1.92 [95% CI, 1.19-3.13]), and presence of symptoms 22-28 days posttreatment (aRR, 2.70 [95% CI, 1.63-4.46]) were associated with long COVID, but nasal severe acute respiratory syndrome coronavirus 2 RNA was not.ConclusionsLong COVID occurred despite early, effective monoclonal antibody therapy and was associated with smoking, female sex, and non-Hispanic ethnicity, but not viral burden. The strong association between symptoms 22-28 days after treatment and long COVID suggests that processes of long COVID start early and may need early intervention.Clinical trials registrationNCT04518410

B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information

Contact tracing data from influenza A(H1N1)2009pdm.

<p>Black: Confirmed ILL; Light Gray: Probable ILL; Dark Gray: Australia A(H1N1) Data.</p

Means and Ranges of the Epidemic Lengths for each Simulation Scenario.

<p>There are 300 Epidemics Generated for each Scenario.</p><p>Means and Ranges of the Epidemic Lengths for each Simulation Scenario.</p

Dirichlet Prior Distributions for the Serial Interval for the Simulation Study.

<p>Dirichlet Prior Distributions for the Serial Interval for the Simulation Study.</p

Summary of DIC-based Prior Selection for Simulated Outbreaks.

<p>Gray scale from dark to light: k = 5, k = 7, k = 10, k = 15, k = 20.</p

Machine learning models based on fluid immunoproteins that predict non-AIDS adverse events in people with HIV

Summary: Despite the success of antiretroviral therapy (ART), individuals with HIV remain at risk for experiencing non-AIDS adverse events (NAEs), including cardiovascular complications and malignancy. Several surrogate immune biomarkers in blood have shown predictive value in predicting NAEs; however, composite panels generated using machine learning may provide a more accurate advancement for monitoring and discriminating NAEs. In a nested case-control study, we aimed to develop machine learning models to discriminate cases (experienced an event) and matched controls using demographic and clinical characteristics alongside 49 plasma immunoproteins measured prior to and post-ART initiation. We generated support vector machine (SVM) classifier models for high-accuracy discrimination of individuals aged 30–50 years who experienced non-fatal NAEs at pre-ART and one-year post-ART. Extreme gradient boosting generated a high-accuracy model at pre-ART, while K-nearest neighbors performed poorly all around. SVM modeling may offer guidance to improve disease monitoring and elucidate potential therapeutic interventions