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. In this work we describe how a complex non-Markovian Dynamical Survival Analysis (DSA) model may be applied to a specific data set with the help of appropriate numerical and statistical schemes. The ideas are illustrated with a data example of the COVID-19 epidemic in Ohio.

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. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

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

The freeze-tolerant anuran Dryophytes chrysoscelis, 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. Glycerol was elevated in plasma, liver, and skeletal muscle of both singly and repeatedly frozen and thawed animals compared with cold-acclimated frogs. In contrast, urea was unchanged by freezing and thawing, whereas glucose was elevated in singly frozen and thawed animals but was reduced toward cold acclimation levels after repeated bouts of freezing. Overall, the cryoprotectant system was maintained, but not further elevated, in all tissues assayed in repeatedly frozen and thawed animals. For repeated freeze-thaw only, hepatic glycogen was depleted and plasma hemoglobin, indicative of erythrocyte hemolysis, increased. Postfreeze recovery of locomotor function, including limb and whole body movement, was delayed with repeated freeze-thaw and was associated with glycerol accumulation and glycogen depletion. Individuals that resumed locomotor function more quickly also accumulated greater cryoinjury. Integrated analyses of cryoprotectant and cryoinjury accumulation suggest that winter survival of D. chrysoscelis may be vulnerable to climate change, limited by carbohydrate stores, cellular repair mechanisms, and plasticity of the cryoprotectant system.

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. We use these calculations to show that direct selection at strengths typically encountered in practice has only a small effect on the distribution of coalescent times, and hence on the distribution of gene trees. This implies that many coalescent-based methods for estimating the species tree topology will be robust to the presence of selection in a subset of the underlying genes. Selection will, however, bias the estimation of speciation times, causing them to underestimate the true speciation times. Our model captures the effects of selection on the genealogies that generate the observed sequence data, but does not model selective pressures that act only on the subsequent sequences or that negatively impact gene tree estimation.

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

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. 2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology has been made available publicly. Highlights: We present a novel statistical approach called Dynamic Survival Analysis (DSA) to model an epidemic curve with incomplete data. The DSA approach is advantageous over standard statistical methods primarily because it does not require prior knowledge of the size of the susceptible population, the overall prevalence of the disease, and also the shape of the epidemic curve.The principal motivation behind the study was to obtain predictions of case counts of COVID-19 and the resulting hospital burden in the state of Ohio during the early phase of the pandemic.The proposed methodology was applied to the COVID-19 incidence data in the state of Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA) with predictions of hospital burden in each of the Hospital Catchment Areas (HCAs) of the state.

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. These methods have been found to be consistent given true gene trees but may be inconsistent when gene trees are estimated from data for loci of finite length. Here, we consider the question of statistical consistency for four taxa for SVDQuartets for general data types, as well as for the maximum likelihood (ML) method in the case in which the data are a collection of sites generated under the MSC model such that the sites are conditionally independent given the species tree (we call these data coalescent independent sites [CIS] data). We show that SVDQuartets is statistically consistent for all data types (i.e., for both CIS data and for multilocus data), and we derive its rate of convergence. We additionally show that ML is consistent for CIS data under the JC69 model and discuss why a proof for the more general multilocus case is difficult. Finally, we compare the performance of ML and SDVQuartets using simulation for both data types. [Consistency; gene tree; maximum likelihood; multilocus data; hylogenetic inference; species tree; SVDQuartets.].

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. The aim of this research is to determine the importance of the midshaft distinction for age-at-death assessment and the necessity of analyzing serial sections by evaluating histological variables at sampling locations along the length of the rib. Three seriated histological sections at three sampling locations (anterior, midshaft, and posterior) were obtained from sixth ribs of ten postmortem human subjects. Cortical area (Ct.Ar) and osteon population density (OPD) were collected from each section (n = 90). Significant differences were determined in Ct.Ar between sampling locations, demonstrating the variation present along the length of the rib. A comparison of OPD at sampling locations revealed significant differences, suggesting that sampling site is critical to accurate age estimates. When sampling location is uncertain, a more anterior section should be taken. Analysis of serial sections within locations revealed no significant differences in OPD or Ct.Ar, supporting the practice of collecting data from one section for age estimation. While an age estimate can be achieved through the analysis of one section, best practice suggests reading two sections to capture intraindividual variation.