Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut using the Cellular Device Network

Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of the exposure assignment bias for an air pollutant (ozone) when subjects are assumed to be static as compared to accounting for individual mobility. To achieve this goal, we use cell-phone mobility data on approximately 400,000 users in the state of Connecticut during a week in July, 2016, in conjunction with an ozone pollution model, and compare individual ozone exposure assuming static versus mobile scenarios. Our results show that exposure models not taking mobility into account often provide poor estimates of individuals commuting into and out of urban areas: the average 8-hour maximum difference between these estimates can exceed 80 parts per billion (ppb). However, for most of the population, the difference in exposure assignment between the two models is small, thereby validating many current epidemiologic studies focusing on exposure to ozone

The Need to Incorporate Communities in Compartmental Models

Tian et al. provide a framework for assessing population- level interventions of disease outbreaks through the construction of counterfactuals in a large-scale, natural experiment assessing the efficacy of mild, but early interventions compared to delayed interventions. The technique is applied to the recent SARS-CoV-2 outbreak with the population of Shenzhen, China acting as the mild-but-early treatment group and a combination of several US counties resembling Shenzhen but enacting a delayed intervention acting as the control. To help further the development of this framework and identify an avenue for further enhancement, we focus on the use and potential limitations of compartmental mod- els. In particular, compartmental models make assumptions about the communicability of a disease that may not per- form well when they are used for large areas with multiple communities where movement is restricted. To illustrate this phenomena, we provide a simulation of a directed percolation (outbreak) process on a simple stochastic block model with two blocks. The simulations show that when transmissibility between two communities is severely restricted an outbreak in two communities resembles a primary and secondary outbreak potentially causing policy and decision makers to mistake effective intervention strategies with non- compliance or inefficacy of an intervention

Climate Modeling of a Potential ExoVenus

The planetary mass and radius sensitivity of exoplanet discovery capabilities has reached into the terrestrial regime. The focus of such investigations is to search within the Habitable Zone where a modern Earth-like atmosphere may be a viable comparison. However, the detection bias of the transit and radial velocity methods lies close to the host star where the received flux at the planet may push the atmosphere into a runaway greenhouse state. One such exoplanet discovery, Kepler-1649b, receives a similar flux from its star as modern Venus does from the Sun, and so was categorized as a possible exoVenus. Here we discuss the planetary parameters of Kepler-1649b with relation to Venus to establish its potential as a Venus analog. We utilize the general circulation model ROCKE-3D to simulate the evolution of the surface temperature of Kepler-1649b under various assumptions, including relative atmospheric abundances. We show that in all our simulations the atmospheric model rapidly diverges from temperate surface conditions towards a runaway greenhouse with rapidly escalating surface temperatures. We calculate transmission spectra for the evolved atmosphere and discuss these spectra within the context of the James Webb Space Telescope (JWST) Near-Infrared Spectrograph (NIRSpec) capabilities. We thus demonstrate the detectability of the key atmospheric signatures of possible runaway greenhouse transition states and outline the future prospects of characterizing potential Venus analogs.Comment: 11 pages, 4 figures, 1 table, accepted for publication in the Astrophysical Journal. The data from this paper are open source and are available from the following data portals: https://portal.nccs.nasa.gov/GISS_modelE/ROCKE-3D/Climate_Modeling_of_a_Potential_ExoVenus https://archive.org/details/Climate_Modeling_of_a_Potential_ExoVenu

Climate Modeling of a Potential Exovenus

The planetary mass and radius sensitivity of exoplanet discovery capabilities has reached into the terrestrial regime. The focus of such investigations is to search within the Habitable Zone where a modern Earth-like atmosphere may be a viable comparison. However, the detection bias of the transit and radial velocity methods lies close to the host star where the received flux at the planet may push the atmosphere into a runaway greenhouse state. One such exoplanet discovery, Kepler-1649b, receives a similar flux from its star as modern Venus does from the Sun, and so was categorized as a possible exoVenus. Here we discuss the planetary parameters of Kepler-1649b in relation to Venus to establish its potential as a Venus analog. We utilize the general circulation model ROCKE-3D to simulate the evolution of the surface temperature of Kepler-1649b under various assumptions, including relative atmospheric abundances. We show that in all our simulations the atmospheric model rapidly diverges from temperate surface conditions toward a runaway greenhouse with rapidly escalating surface temperatures. We calculate transmission spectra for the evolved atmosphere and discuss these spectra within the context of the James Webb Space Telescope Near-Infrared Spectrograph capabilities. We thus demonstrate the detectability of the key atmospheric signatures of possible runaway greenhouse transition states and outline the future prospects of characterizing potential Venus analogs

A Comparative Study of the Angoff and Nedelsky Methods: Implications for Validity

The Angoff and Nedelsky methods are two well-known procedures for setting passing scores on tests. Previous comparative studies indicate that the Nedelsky method tends to consistently set the lowest passing score relative to the Angoff and other methods. However, it cannot be concluded that the lower Nedelsky estimates are less accurate, because previous studies have not included a criterion of the correct passing score against which Nedelsky and other passing scores could be validated. The present paper describes an experiment in which criterion measures of the correct passing scores were generated and were compared for accuracy to Angoff and Nedelsky estimates

preference: An R Package for Two-Stage Clinical Trial Design Accounting for Patient Preference

The consideration of a patient's treatment preference may be essential in determining how a patient will respond to a particular treatment. While traditional clinical trials are unable to capture these effects, the two-stage randomized preference design provides an important tool for researchers seeking to understand the role of patient preferences. In addition to the treatment effect, these designs seek to estimate the role of preferences through testing of selection and preference effects. The R package preference facilitates the use of two-stage clinical trials by providing the necessary tools to design and analyze these studies. To aid in the design, functions are provided to estimate the required sample size and to estimate the study power when a sample size is fixed. In addition, analysis functions are provided to determine the significance of each effect using either raw data or summary statistics. The package is able to incorporate either an unstratified or stratified preference design. The functionality of the package is demonstrated using data from a study evaluating two management methods in women found to have an atypical Pap smear

Distribution of human exposure to ozone during commuting hours in Connecticut using the cellular device network

Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school, or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of the exposure assignment bias for an air pollutant (ozone) when subjects are assumed to be static as compared to accounting for individual mobility. To achieve this goal, we use cell-phone mobility data on approximately 400,000 users in the state of Connecticut, USA during a week in July 2016, in conjunction with an ozone pollution model, and compare individual ozone exposure assuming static versus mobile scenarios. Our results show that exposure models not taking mobility into account often provide poor estimates of individuals commuting into and out of urban areas: the average 8-h maximum difference between these estimates can exceed 80 parts per billion (ppb). However, for most of the population, the difference in exposure assignment between the two models is small, thereby validating many current epidemiologic studies focusing on exposure to ozone