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There are two prominent paradigms for the modelling of networks: in the first, referred to as the mechanistic approach, one specifies a set of domain-specific mechanistic rules that are used to grow or evolve the network over time; in the second, referred to as the probabilistic approach, one describes a model that specifies the likelihood of observing a given network. Mechanistic models (models developed based on the mechanistic approach) are appealing because they capture scientific processes that are believed to be responsible for network generation; however, they do not easily lend themselves to the use of inferential techniques when compared with probabilistic models. We introduce a general framework for converting a mechanistic network model (MNM) to a probabilistic network model (PNM). The proposed framework makes it possible to identify the essential network properties and their joint probability distribution for some MNMs; doing so makes it possible to address questions such as whether two different mechanistic models generate networks with identical distributions of properties, or whether a network property, such as clustering, is over- or under-represented in the networks generated by the model of interest compared with a reference model. The proposed framework is intended to bridge some of the gap that currently exists between the formulation and representation of mechanistic and PNMs. We also highlight limitations of PNMs that need to be addressed in order to close this gap. [ABSTRACT FROM AUTHOR]

Universities are faced with decisions on how to resume campus activities while mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) risk. To provide guidance for these decisions, we developed an agent-based network model of SARS-CoV-2 transmission to assess the potential impact of strategies to reduce outbreaks. The model incorporates important features related to risk at the University of California San Diego. We found that structural interventions for housing (singles only) and instructional changes (from in-person to hybrid with class size caps) can substantially reduce the basic reproduction number, but masking and social distancing are required to reduce this to at or below 1. Within a risk mitigation scenario, increased frequency of asymptomatic testing from monthly to twice weekly has minimal impact on average outbreak size (1.1–1.9), but substantially reduces the maximum outbreak size and cumulative number of cases. We conclude that an interdependent approach incorporating risk mitigation, viral detection, and public health intervention is required to mitigate risk. [ABSTRACT FROM AUTHOR]

This article develops methods for estimating treatment effects in mixed-effects models using outcome data gathered from serial dilution assays. Our application allows us to estimate the viral burden of HIV infection before and after antiviral treatment from cell dilution assays. This assay is designed to determine the infectious units per patient peripheral blood mononuclear cell (PBMC). The infectious unit is the amount of virus required to produce detectable HIV infection in PBMC's from healthy, uninfected donors. At each dilution level of the patient cells, one observes whether or not it was possible for the virus from these cells to infect donor cells. Thus the assay result for each subject consists of a series of repeated binary outcomes. We propose an analytic approach in which patient-specific titers (measures of viral burden) are modeled as random effects from an unknown distribution, and treatment effects are modeled as fixed. This approach makes use of all assay results, even if many assays fail to reach endpoint (i.e.. they turn negative at the highest dilution level) and the assay design (dilution scheme) changes over time. KEY WORDS: HIV infection; Mixed-effects model; Nonparametric estimation; Random-effects model; Serial dilution assay; Viral burden., Approaches for determining treatment effects in mixed-effects models utilizing outcome data obtained from serial dilution assays were established. These methods enabled the computation of the viral load of HIV infection before and after antiviral treatment from cell dilution assays. The assay result for each patient was composed of a series of repeated binary results. An analytic method was suggested in which patient-specific titers were modeled as random effects from an unknown distribution while treatment effects were modeled as fixed., 1. INTRODUCTION This article presents a method for nonparametric maximum likelihood estimation applied to mixed-effects models for repeated binary outcomes. The problem is motivated by an analysis of results of [...]

Background Public health action combating human immunodeficiency virus (HIV) includes facilitating navigation through the HIV continuum of care: timely diagnosis followed by linkage to care and initiation of antiretroviral therapy to suppress viral replication. Molecular epidemiology can identify rapidly growing HIV genetic transmission clusters. How progression through the care continuum relates to transmission clusters has not been previously characterized. Methods We performed a retrospective study on HIV surveillance data from 5226 adult cases in Los Angeles County diagnosed from 2010 through 2014. Genetic transmission clusters were constructed using HIV-TRACE. Cox proportional hazard models were used to estimate the impact of transmission cluster growth on the time intervals between care continuum events. Gamma frailty models incorporated the effect of heterogeneity associated with genetic transmission clusters. Results In contrast to our expectations, there were no differences in time to the care continuum events among individuals in clusters with different growth dynamics. However, upon achieving viral suppression, individuals in high growth clusters were slower to experience viral rebound (hazard ratio 0.83, P  = .011) compared with individuals in low growth clusters. Heterogeneity associated with cluster membership in the timing to each event in the care continuum was highly significant (P  < .001), with and without adjustment for transmission risk and demographics. Conclusions Individuals within the same transmission cluster have more similar trajectories through the HIV care continuum than those across transmission clusters. These findings suggest molecular epidemiology can assist public health officials in identifying clusters of individuals who may benefit from assistance in navigating the HIV care continuum. [ABSTRACT FROM AUTHOR]

Estimation of causal effects from observational data is a primary goal of epidemiology. The use of multiple methods with different assumptions relating to exchangeability improves causal inference by demonstrating robustness across assumptions. We estimated the effect of antiretroviral therapy (ART) on mortality in rural KwaZulu-Natal, South Africa, from 2007 to 2011, using 2 methods with substantially different assumptions: the regression discontinuity design (RDD) and inverse-probability–weighted (IPW) marginal structural models (MSMs). The RDD analysis took advantage of a CD4-cell-count–based threshold for ART initiation (200 cells/μL). The 2 methods yielded consistent but nonidentical results for the effect of immediate initiation of ART (RDD intention-to-treat hazard ratio (HR) = 0.66, 95% confidence interval (CI): 0.35, 1.26; RDD complier average causal effect HR = 0.56, 95% CI: 0.41, 0.77; IPW MSM HR = 0.49, 95% CI: 0.42, 0.58). Although RDD and IPW MSM estimates have distinct identifying assumptions, strengths, and limitations in terms of internal and external validity, results in this application were similar. The differences in modeling approaches and the external validity of each method may explain the minor differences in effect estimates. The overall consistency of the results lends support for causal inference about the effect of ART on mortality from these data. [ABSTRACT FROM AUTHOR]

Influence analysis and leverage analysis are important and well-established adjuncts to ordinary least squares (OLS) regression, but analogous regression diagnostics are not generally available for multivariate regression problems. This article describes measures of influence and leverage for a generalized least squares (GLS) estimator of the regression coefficients in a class of multivariate linear models for repeated measurements. When the covariance matrix of the observation vectors is known, the GLS estimator can be expressed as an OLS estimator calculated from transformed variables. In this situation, the multivariate regression diagnostics are directly related to the univariate measures. When the covariance matrix is unknown, however, multistage estimation procedures are required. We describe measures of influence and leverage for a three-step estimator that uses the residuals from an initial OLS regression to estimate the covariance matrix of the response vectors. These diagnostics account for the influence of individual data values on the estimated covariance matrix. The article reports on two different measures of influence and the relation between them: (a) derivative influence, the differential change in an estimated parameter or predicted value resulting from a slight perturbation in the weight assigned to a given observation or vector of observations, and (b) deletion influence, the change in a parameter estimate resulting from the deletion of the vector of observations. Both of these measures can be represented as the sum of components that correspond to the stages of the GLS estimation. Examination of the relative asymptotic sizes of these components and of the differences between derivative and deletion influence leads to modified versions of these... [ABSTRACT FROM AUTHOR]

The article discusses the pregnancy conception on antiretroviral therapy (ART) which need data for maternal safety. It cites on the association of ART initiation in pregnancy and adverse pregnancy outcomes which was being evaluated from research. The article also discusses the outcomes of pregnancy which include preterm delivery and low birth weight,