A mechanistic modeling and estimation framework for environmental pathogen surveillance.

Environmental pathogen surveillance is a promising disease surveillance modality that has been widely adopted for SARS-CoV-2 monitoring. The highly variable nature of environmental pathogen data is a challenge for integrating these data into public health response. One source of this variability is heterogeneous infection both within an individual over the course of infection as well as between individuals in their pathogen shedding over time.

Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio.

The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery. Recently, the Dynamical Survival Analysis (DSA) method has been shown to be an effective tool in analyzing complex non-Markovian epidemic processes that are otherwise difficult to handle using standard methods. One of the advantages of Dynamical Survival Analysis (DSA) is its representation of typical epidemic data in a simple although not explicit form that involves solutions of certain differential equations.

Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification.

Repeated freeze-thaw cycles in freeze-tolerant treefrogs: novel interindividual variation of integrative biochemical, cellular, and organismal responses.

The freeze-tolerant anuran , Cope's gray treefrog, mobilizes a complex cryoprotectant system that includes glycerol, glucose, and urea to minimize damage induced by freezing and thawing of up to 65% of body water. In this species' eastern Northern American temperate habitat, oscillations of temperature above and below freezing are common; however, the effects of repeated freezing and thawing in this species are unstudied. The biochemical and physiological effects of repeated freeze-thaw cycles were therefore evaluated and compared with cold acclimation and single freeze-thaw episodes.

On the effects of selection and mutation on species tree inference.

The effect of selection acting on regions of the genome on the accuracy of species-level phylogenetic inference using methods that do not explicitly model selection is an open question that is relevant to most, if not all, phylogenomic studies. To address this, we derive a mathematical approximation to the Wright-Fisher model with mutation and selection in the limit as the population size becomes large. In contrast to previous approximations based on diffusion processes, our approximation can be used to study the distribution of coalescent times for an arbitrary number of lineages, allowing calculation of the probability distribution of gene genealogies under the coalescent model.

Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio.

Unlabelled: As the Coronavirus 2019 (COVID-19) disease started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at the Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: 1) A Dynamic Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification.

Rib Variation at Multiple Locations and Implications for Histological Age Estimation.

Existing histological age estimation methods using the rib were developed mainly from the midshaft; however, in forensic practice, uncertainty of sampling location often arises due to fragmented or previously sampled ribs. The potential for error increases when sampling location is uncertain and utilizing a section beyond the midshaft (either anterior or posterior) may result in erroneous age estimates. Additionally, there is debate within the field regarding the minimum number of sections needed for accurate age estimation.

Consistency of SVDQuartets and Maximum Likelihood for Coalescent-Based Species Tree Estimation.

Numerous methods for inferring species-level phylogenies under the coalescent model have been proposed within the last 20 years, and debates continue about the relative strengths and weaknesses of these methods. One desirable property of a phylogenetic estimator is that of statistical consistency, which means intuitively that as more data are collected, the probability that the estimated tree has the same topology as the true tree goes to 1. To date, consistency results for species tree inference under the multispecies coalescent (MSC) have been derived only for summary statistics methods, such as ASTRAL and MP-EST.