Detecting changes in the transmission rate of a stochastic epidemic model

Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation policies. Estimating such changes in transmission rates can help us better model and predict the dynamics of an epidemic, and provide insight into the efficacy of control and intervention strategies. We present a method for likelihood-based estimation of parameters in the stochastic susceptible-infected-removed model under a time-inhomogeneous transmission rate comprised of piecewise constant components. In doing so, our method simultaneously learns change points in the transmission rate via a Markov chain Monte Carlo algorithm. The method targets the exact model posterior in a difficult missing data setting given only partially observed case counts over time. We validate performance on simulated data before applying our approach to data from an Ebola outbreak in Western Africa and COVID-19 outbreak on a university campus.

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

When it comes to incidence data, most of the work on this field focuses on the modeling of nonextreme periods. Several attempts have been made and a variety of techniques are available to achieve so. In this work, in order to model not only the nonextreme periods but also capture the behavior of the whole time-series, we make use of a dataset on influenza-like illness rate for Greece, for the period 2014-2016. The identification of extreme periods is made possible via changepoint detection analysis and model selection techniques are developed in order to identify the optimal periodic-type auto-regressive moving average model with covariates that best describes the pattern of the time-series. In addition, in the context of incidence data modeling, an advanced algorithm was developed in order to improve the accuracy of the selected model. The derived results are satisfactory since the changepoint method seems to identify correctly the extreme periods, and the selected model: (1) estimates accurately the influenza-like illness syndrome morbidity burden in the case of Greece, and (2) captures satisfactorily enough the behavior of the whole time-series.

Proportional mixture of two rarefaction/extrapolation curves to forecast biodiversity changes under landscape transformation

Progressive habitat transformation causes global changes in landscape biodiversity patterns, but can be hard to quantify. Rarefaction/extrapolation approaches can quantify within-habitat biodiversity, but may not be useful for cases in which one habitat type is progressively transformed into another habitat type. To quantify biodiversity patterns in such transformed landscapes, we use Hill numbers to analyse individual-based species abundance data or replicated, sample-based incidence data. Given biodiversity data from two distinct habitat types, when a specified proportion of original habitat is transformed, our approach utilises a proportional mixture of two within-habitat rarefaction/extrapolation curves to analytically predict biodiversity changes, with bootstrap confidence intervals to assess sampling uncertainty. We also derive analytic formulas for assessing species composition (i.e. the numbers of shared and unique species) for any mixture of the two habitat types. Our analytical and numerical analyses revealed that species unique to each habitat type are the most important determinants of landscape biodiversity patterns.

Analysis of Reported Balloon Malfunctions and Proposed Rescue Strategy for Malfunction during Airway Dilation

Objective The rate of balloon dilator failure is unknown, and a rescue strategy for device malfunction has not been established. The purposes of this investigation were to determine the approximate number of balloon failures in the gastrointestinal tract and airway, evaluate the parameters required to rupture balloon dilators, and develop a rescue strategy to efficiently reestablish airway patency. Study Design Retrospective cohort and basic medical research. Setting Academic tertiary care medical center. Subjects and Methods The Manufacturer and User Facility Device Experience database was queried for adverse events associated with tracheal and esophageal dilators between January 1, 2014, and January 1, 2017. A bench-top model of airway stenosis was developed, and optimal conditions for the safe removal of a malfunctioning dilator were assessed (2, 4, 6 atm). Results There were 420 reported balloon malfunctions, including 104 cases with deflation/removal issues. The bench-top model determined that device rupture allowing for immediate removal occurs with needle puncture at balloon pressures ≥8 atm. Balloons inflated to 6 atm required a median of 17.5 seconds (range, 0-55.3) for removal, in comparison with 30.2 seconds (range, 7.1-87.5) at 2 atm ( P > .05). Conclusion Balloon dilator malfunction is a significant problem that practitioners must be prepared for. Pressure ≥8 atm (~33% overinflation) is required to consistently cause complete balloon dilator rupture via needle puncture. While counterintuitive, increasing the inflation pressure of a malfunctioning balloon (8-10 atm) may expedite rupture and safe removal. A rescue strategy for balloon malfunction is proposed.