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PURPOSE: Inherited cataract, opacification of the lens, is the most common worldwide cause of blindness in children. We aimed to identify the genetic cause of isolated autosomal-dominant lamellar cataract in a five-generation British family. METHODS: Whole exome sequencing (WES) was performed on two affected individuals of the family and further validated by direct sequencing in family members. RESULTS: A novel missense mutation NM_001040667.2:c.190A>G;p.K64E was identified in the DNA-binding-domain of heat-shock transcription factor 4 (HSF4) and found to co-segregate with disease. CONCLUSION: We have identified a novel mutation in HSF4 in a large British pedigree causing dominant congenital lamellar cataract. This is the second mutation in this gene found in the British population. This mutation is likely to be dominant negative and affect the DNA-binding affinity of HSF4.

Highlights • Silent circulation (SC) of wild polio viruses (WPV) when oral polio vaccine (OPV) use is stopped, could threaten eradication. • We analyzed a model designed to develop theory about mechanisms and factors that lead to SC and how SC risks can be assessed using surveillance data. • Prolonged low-level SC emerges as a threshold phenomenon through a mechanism related to balancing contributions of different populations to the effective reproduction number. • Factors that promote this mechanism are many years of inadequate vaccination efforts, ongoing waning of immunity against transmission years after last OPV or WPV infection, low transmissibility of OPV, and high transmission conditions. • Analyzing acute flaccid paralysis surveillance or environmental surveillance data by themselves cannot assess the risk that an SC threshold has been passed, but new methods to analyze them jointly could do so., Waning immunity could allow transmission of polioviruses without causing poliomyelitis by promoting silent circulation (SC). Undetected SC when oral polio vaccine (OPV) use is stopped could cause difficult to control epidemics. Little is known about waning. To develop theory about what generates SC, we modeled a range of waning patterns. We varied both OPV and wild polio virus (WPV) transmissibility, the time from beginning vaccination to reaching low polio levels, and the infection to paralysis ratio (IPR). There was longer SC when waning continued over time rather than stopping after a few years, when WPV transmissibility was higher or OPV transmissibility was lower, and when the IPR was higher. These interacted in a way that makes recent emergence of prolonged SC a possibility. As the time to reach low infection levels increased, vaccine rates needed to eliminate polio increased and a threshold was passed where prolonged low-level SC emerged. These phenomena were caused by increased contributions to the force of infection from reinfections. The resulting SC occurs at low levels that would be difficult to detect using environmental surveillance. For all waning patterns, modest levels of vaccination of adults shortened SC. Previous modeling studies may have missed these phenomena because (1) they used models with no or very short duration waning and (2) they fit models to paralytic polio case counts. Our analyses show that polio case counts cannot predict SC because nearly identical polio case count patterns can be generated by a range of waning patterns that generate different patterns of SC. We conclude that the possibility of prolonged SC is real but unquantified, that vaccinating modest fractions of adults could reduce SC risk, and that joint analysis of acute flaccid paralysis and environmental surveillance data can help assess SC risks and ensure low risks before stopping OPV.

Genetic sequences from pathogens can provide information about infectious disease dynamics that may supplement or replace information from other epidemiological observations. Currently available methods first estimate phylogenetic trees from sequence data, then estimate a transmission model conditional on these phylogenies. Outside limited classes of models, existing methods are unable to enforce logical consistency between the model of transmission and that underlying the phylogenetic reconstruction. Such conflicts in assumptions can lead to bias in the resulting inferences. Here, we develop a general, statistically efficient, plug-and-play method to jointly estimate both disease transmission and phylogeny using genetic data and, if desired, other epidemiological observations. This method explicitly connects the model of transmission and the model of phylogeny so as to avoid the aforementioned inconsistency. We demonstrate the feasibility of our approach through simulation and apply it to estimate stage-specific infectiousness in a subepidemic of HIV in Detroit, Michigan. In a supplement, we prove that our approach is a valid sequential Monte Carlo algorithm. While we focus on how these methods may be applied to population-level models of infectious disease, their scope is more general. These methods may be applied in other biological systems where one seeks to infer population dynamics from genetic sequences, and they may also find application for evolutionary models with phenotypic rather than genotypic data.

Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time-series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor can they provide parameters for predictive models. State-space models (SSMs) applied to time-series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in an SSM framework to seasonal time series (1994-2012) using a recently developed, maximum-likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g., nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the observed B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore provides evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios, such as climate change.

This research summarized the effect of cell phone restrictions on the prevalence of cell phone use and motor vehicle crashes involving young drivers. Multiple databases were searched with the use of terms related to cell phone restrictions, the prevalence of cell phone use, crashes, and young drivers. Fifty-three abstracts were reviewed. Eleven studies met the inclusion criteria. Six studies examined changes in the prevalence of cell phone use before and after a restriction was imposed; five studies used changes in crash rates as the outcome. The majority of prevalence studies found no change in young drivers’ cell phone use following the introduction of a restriction. Crash studies using multistate samples reported significant reductions in fatal crashes; single-state studies reported no change or small increases in crashes. Restrictions appeared to have no long-term effect on the prevalence of cell phone use among novice drivers. Conflicting findings from crash studies reflected differences in analytical approaches, limitations of existing data, and the challenges of quantifying the effect of young-driver restrictions in the presence of all-driver restrictions. Overall, evidence of the effectiveness of young drivers’ cell phone restrictions is inconclusive. There are relatively few studies, and the methodological limitations of the existing studies revealed no clear pattern of effects. Additional studies are needed to distinguish novice drivers (16- to 17-year olds) from experienced young drivers and to use distraction-related crashes as an outcome.

BackgroundWhile live attenuated monovalent human rotavirus vaccine (Rotarix) efficacy has been characterized through randomized studies, its effectiveness, especially in non-clinical settings, is unclear. In this study, we estimate direct, indirect, and overall effectiveness of Rotarix vaccination.MethodsWe analyze 29 months of all-cause diarrhea surveillance from a child cohort (n=376) and ten years of serial population-based case-control lab-confirmed rotavirus data (n=2489) from rural Ecuador during which Rotarix vaccination was introduced. We estimate: 1) the direct effect of vaccination from a cohort of children born from 2008-2013 using Cox regression to compare time to first all-cause diarrhea case by vaccine status; and 2) the overall effect on all-cause diarrheal and symptomatic and asymptomatic rotavirus infection for all age groups, including indirect effects on adults, from the case-control data using weighted logistic regression.ResultsRotarix vaccination provided direct protection against all-cause diarrhea among children 0.5 - 2 years (All-cause diarrhea reduction for receipt of 2 doses of Rotarix=57.1%, 95% CI: 16.6, 77.9%). Overall effectiveness against rotavirus infection was strong (Exposure to 100% coverage of Rotarix vaccination was associated with an 85.5% reduction, 95% CI: 61.1-94.6%) compared to 0% coverage. Indirect effects were observed among older, vaccine-ineligible children and adults (84.5% reduction, 95% CI: 48.2-95.4%). Vaccine effectiveness was high against both symptomatic (48.3% reduction,95% CI: 0.03-73.1%) and asymptomatic infection (90.1% reduction, 95% CI: 56.9-97.7%).ConclusionsRotarix vaccination suppresses overall transmission. It is highly effective among children in a rural community setting and provides population-level benefits through indirect protection among adults.

Human immunodeficiency virus (HIV) transmission models that include variability in sexual behavior over time have shown increased incidence, prevalence, and acute-state transmission rates for a given population risk profile. This raises the question of whether dynamic variation in individual sexual behavior is a real phenomenon that can be observed and measured. To study this dynamic variation, we developed a model incorporating heterogeneity in both between-person and within-person sexual contact patterns. Using novel methodology that we call iterated filtering for longitudinal data, we fitted this model by maximum likelihood to longitudinal survey data from the Centers for Disease Control and Prevention's Collaborative HIV Seroincidence Study (1992-1995). We found evidence for individual heterogeneity in sexual behavior over time. We simulated an epidemic process and found that inclusion of empirically measured levels of dynamic variation in individual-level sexual behavior brought the theoretical predictions of HIV incidence into closer alignment with reality given the measured per-act probabilities of transmission. The methods developed here provide a framework for quantifying variation in sexual behaviors that helps in understanding the HIV epidemic among gay men.

Geomagnetic storms start with activity on the Sun that causes propagation of magnetized plasma structures in the solar wind. The type of solar activity is used to classify the plasma structures as being either interplanetary coronal mass ejection (ICME) or corotating interaction region (CIR) driven. The ICME-driven events are further classified as either magnetic cloud (MC) driven or sheath (SH) driven by the geoeffective structure responsible for the peak of the storm. The geoeffective solar wind flow then interacts with the magnetosphere producing a disturbance in near-Earth space. It is commonly believed that a SH-driven event behaves more like a CIR-driven event than a MC-driven event; however, in our analysis this is not the case. In this study, geomagnetic storms are investigated statistically with respect to the solar wind driver and the intensity of the events. We use the Hot Electron and Ion Drift Integrator (HEIDI) model to simulate the inner magnetospheric hot ion population during all of the storms classified as intense (Dstmin ≤ −100 nT) within solar cycle 23 (1996–2005). HEIDI is configured four different ways using either the Volland-Stern or self-consistent electric field and either event-based Los Alamos National Laboratory (LANL) magnetospheric plasma analyzer (MPA) data or a reanalyzed lower resolution version of the data that provides spatial resolution. Presenting the simulation results, geomagnetic data, and solar wind data along a normalized epoch timeline shows the average behavior throughout a typical storm of each classification. The error along the epoch timeline for each HEIDI configuration is used to rate the model's performance. We also subgrouped the storms based on the magnitude of the minimum Dst. We found that typically the LANL MPA data provide the best outer boundary condition. Additionally, the self-consistent electric field better reproduces SH- and MC-driven events throughout most of the storm timeline, but the Volland-Stern electric field better reproduces CIR-driven events. Contrary to what we expect, examination of the HEIDI model results and solar wind data shows that SH-driven events behave more like MC-driven events than CIR-driven storms.

Monte Carlo methods to evaluate and maximize the likelihood function enable the construction of confidence intervals and hypothesis tests, facilitating scientific investigation using models for which the likelihood function is intractable. When Monte Carlo error can be made small, by sufficiently exhaustive computation, then the standard theory and practice of likelihood-based inference applies. As datasets become larger, and models more complex, situations arise where no reasonable amount of computation can render Monte Carlo error negligible. We develop profile likelihood methodology to provide frequentist inferences that take into account Monte Carlo uncertainty. We investigate the role of this methodology in facilitating inference for computationally challenging dynamic latent variable models. We present examples arising in the study of infectious disease transmission, demonstrating our methodology for inference on nonlinear dynamic models using genetic sequence data and panel time-series data. We also discuss applicability to nonlinear time-series and spatio-temporal data.

(S1) Inference for nonlinear partially observed spatiotemporal systems; (S2) Comparing MCAP with Fisher information and a bootstrap; (S3) Implementation of the MCAP algorithm in R.

We propose a method for inference on moderately high-dimensional, nonlinear, non-Gaussian, partially observed Markov process models for which the transition density is not analytically tractable. Markov processes with intractable transition densities arise in models defined implicitly by simulation algorithms. Widely used particle filter methods are applicable to nonlinear, non-Gaussian models but suffer from the curse of dimensionality. Improved scalability is provided by ensemble Kalman filter methods, but these are inappropriate for highly nonlinear and non-Gaussian models. We propose a particle filter method having improved practical and theoretical scalability with respect to the model dimension. This method is applicable to implicitly defined models having analytically intractable transition densities. Our method is developed based on the assumption that the latent process is defined in continuous time and that a simulator of this latent process is available. In this method, particles are propagated at intermediate time intervals between observations and are resampled based on a forecast likelihood of future observations. We combine this particle filter with parameter estimation methodology to enable likelihood-based inference for highly nonlinear spatiotemporal systems. We demonstrate our methodology on a stochastic Lorenz 96 model and a model for the population dynamics of infectious diseases in a network of linked regions.