A Novel Framework for Modeling Person-to-Person Transmission of Respiratory Diseases.

From the beginning of the COVID-19 pandemic, researchers assessed the impact of the disease in terms of loss of life, medical load, economic damage, and other key metrics of resiliency and consequence mitigation; these studies sought to parametrize the critical components of a disease transmission model and the resulting analyses were informative but often lacked critical parameters or a discussion of parameter sensitivities. Using SARS-CoV-2 as a case study, we present a robust modeling framework that considers disease transmissibility from the source through transport and dispersion and infectivity. The framework is designed to work across a range of particle sizes and estimate the generation rate, environmental fate, deposited dose, and infection, allowing for end-to-end analysis that can be transitioned to individual and population health models. In this paper, we perform sensitivity analysis on the model framework to demonstrate how it can be used to advance and prioritize research efforts by highlighting critical parameters for further analyses.

Human Dose-Response Data for Francisella tularensis and a Dose- and Time-Dependent Mathematical Model of Early-Phase Fever Associated with Tularemia After Inhalation Exposure.

Military health risk assessors, medical planners, operational planners, and defense system developers require knowledge of human responses to doses of biothreat agents to support force health protection and chemical, biological, radiological, nuclear (CBRN) defense missions. This article reviews extensive data from 118 human volunteers administered aerosols of the bacterial agent Francisella tularensis, strain Schu S4, which causes tularemia. The data set includes incidence of early-phase febrile illness following administration of well-characterized inhaled doses of F. tularensis. Supplemental data on human body temperature profiles over time available from de-identified case reports is also presented. A unified, logically consistent model of early-phase febrile illness is described as a lognormal dose-response function for febrile illness linked with a stochastic time profile of fever. Three parameters are estimated from the human data to describe the time profile: incubation period or onset time for fever; rise time of fever; and near-maximum body temperature. Inhaled dose-dependence and variability are characterized for each of the three parameters. These parameters enable a stochastic model for the response of an exposed population through incorporation of individual-by-individual variability by drawing random samples from the statistical distributions of these three parameters for each individual. This model provides risk assessors and medical decisionmakers reliable representations of the predicted health impacts of early-phase febrile illness for as long as one week after aerosol exposures of human populations to F. tularensis.

Computational fluid dynamics simulations of inhaled nano- and microparticle deposition in the rhesus monkey nasal passages.

Anatomically accurate computational fluid dynamics (CFD) models of the nasal passages of an infant (6 months old, 1.3 kg) and adult (7 years old, 11.9 kg) rhesus monkey were used to predict nasal deposition of inhaled nano- and microparticles. Steady-state, inspiratory airflow simulations were conducted at flow rates equal to 100, 200 and 300% of the estimated minute volume for resting breathing in each model. Particle transport and deposition simulations were conducted using the Lagrangian method to track the motion of inhaled particles. Nasal deposition fractions were higher in the infant model than the adult model at equivalent physiologic flow rates. Deposition curves collapsed when differences in nasal geometry were accounted for by plotting microparticle deposition versus the Stokes number and nanoparticle deposition as a function of the Schmidt number and diffusion parameter. Particle deposition was also quantified on major nasal epithelial types. Maximum olfactory deposition ranged from 5 to 14% for 1-2 nm particles in the adult and infant models, depending on flow rate. For these particle sizes, deposition on respiratory/transitional epithelia ranged from 40 to 50%. Increased deposition was also predicted for olfactory and respiratory/transitional epithelia for particle sizes >5 µm in the infant model and >8 µm in the adult model. Semi-empirical curves were developed based on the CFD simulation results to allow for simplified calculations of age-based deposition in the rhesus monkey nasal passages that can be implemented into lung dosimetry models.

Development of a rhesus monkey lung geometry model and application to particle deposition in comparison to humans.

The exposure-dose-response characterization of an inhalation hazard established in an animal species needs to be translated to an equivalent characterization in humans relative to comparable doses or exposure scenarios. Here, the first geometry model of the conducting airways for rhesus monkeys is developed based upon CT images of the conducting airways of a 6-month-old male, rhesus monkey. An algorithm was developed for adding the alveolar region airways using published rhesus morphometric data. The resultant lung geometry model can be used in mechanistic particle or gaseous dosimetry models. Such dosimetry models require estimates of the upper respiratory tract volume of the animal and the functional residual capacity, as well as of the tidal volume and breathing frequency of the animal. The relationship of these variables to rhesus monkeys of differing body weights was established by synthesizing and modeling published data as well as modeling pulmonary function measurements on 121 rhesus control animals. Deposition patterns of particles up to 10 µm in size were examined for endotracheal and and up to 5 µm for spontaneous breathing in infant and young adult monkeys and compared to those for humans. Deposition fraction of respirable size particles was found to be higher in the conducting airways of infant and young adult rhesus monkeys compared to humans. Due to the filtering effect of the conducting airways, pulmonary deposition in rhesus monkeys was lower than that in humans. Future research areas are identified that would either allow replacing assumptions or improving the newly developed lung model.

A review of inhalability fraction models: discussion and recommendations.

The first step in mathematically modeling the mechanics of respiratory deposition of particles is to estimate the ability of a particle to enter the head, either through the mouth or nose. Models of the biological effects from inhaled particles are commonly, albeit incorrectly, simplified by making an assumption that the only particles of concern are those that can readily penetrate to the pulmonary region of the lung: typically particles less than 5microm in aerodynamic diameter. Inhalability for particles of this size is effectively 100%, so there is little need to develop a mathematical representation of the phenomenon. However, chemical irritants, biological agents, or radioactive material, in the form of large particles or droplets, can cause adverse biological responses by simply being taken into the head and depositing in the extrathoracic area. As a result, it is important to understand the inhalability of both small and large particles. The concept of particle inhalability received little consideration until the 1970s; since then it has been the subject of many experiments with a fairly wide disparity of results, in part due to the variety of dependent variables and the difficulty in adequate measurement methods. This article describes the currently utilized models of inhalability, recommends specific methods for implementing inhalability into mathematical models of respiratory deposition, and identifies outstanding issues and limitations. In this article, we describe inhalability as it applies to particulate matter and liquid droplets; modeling the inhalability of fibers is a work in progress and is not addressed.

Modeling the acute health effects of astronauts from exposure to large solar particle events.

Radiation exposure from Solar Particle Events (SPE) presents a significant health concern for astronauts for exploration missions outside the protection of the Earth's magnetic field, which could impair their performance and result in the possibility of failure of the mission. Assessing the potential for early radiation effects under such adverse conditions is of prime importance. Here we apply a biologically based mathematical model that describes the dose- and time-dependent early human responses that constitute the prodromal syndromes to consider acute risks from SPEs. We examine the possible early effects on crews from exposure to some historically large solar events on lunar and/or Mars missions. The doses and dose rates of specific organs were calculated using the Baryon radiation transport (BRYNTRN) code and a computerized anatomical man model, while the hazard of the early radiation effects and performance reduction were calculated using the Radiation-Induced Performance Decrement (RIPD) code. Based on model assumptions we show that exposure to these historical events would cause moderate early health effects to crew members inside a typical spacecraft or during extra-vehicular activities, if effective shielding and medical countermeasure tactics were not provided. We also calculate possible even worse cases (double intensity, multiple occurrences in a short period of time, etc.) to estimate the severity, onset and duration of various types of early illness. Uncertainties in the calculation due to limited data on relative biological effectiveness and dose-rate modifying factors for protons and secondary radiation, and the identification of sensitive sites in critical organs are discussed.

Placing long-term and acute radiation effects in a common framework.

No method presently exists for placing long-term risks from low-level radiation doses and acute radiation sickness (ARS) from high-level doses within a common framework useful for the military commander. This article proposes such a method. The Defense Threat Reduction Agency and its predecessor agencies pioneered the quantitative description of the impact of ARS on human performance capability and military unit effectiveness. One product of these efforts is the Radiation-Induced Performance Decrement (RIPD) software. RIPD software includes physiologically based models of ARS valid for both chronic and one-time radiation exposure. One key element of placing ARS and low-level radiation effects in a common framework is finding independent and dependent variables that have a common meaning for both. This paper examines the use of cumulative probability of effect vs. dose and time after exposure for establishing a common framework. The physiologically based models of upper gastrointestinal distress and fatigability and weakness from the RIPD software support this approach.