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".

Wastewater surveillance of SARS-CoV-2 in dormitories as a part of comprehensive university campus COVID-19 monitoring.

Wastewater-based epidemiology is an effective tool for monitoring infectious disease spread or illicit drug use within communities. At the Ohio State University, we conducted a SARS-CoV-2 wastewater surveillance program in the 2020-2021 academic year and compared results with the university-required weekly COVID-19 saliva testing to monitor COVID-19 infection prevalence in the on-campus residential communities. The objectives of the study were to rapidly track trends in the wastewater SARS-CoV-2 gene concentrations, analyze the relationship between case numbers and wastewater signals when adjusted using human fecal viral indicator concentrations (PMMoV, crAssphage) in wastewater, and investigate the relationship of the SARS-CoV-2 gene concentrations with wastewater parameters. SARS-CoV-2 nucleocapsid and envelope (N1, N2, and E) gene concentrations, determined with reverse transcription droplet digital PCR, were used to track SARS-CoV-2 viral loads in dormitory wastewater once a week at 6 sampling sites across the campus during the fall semester in 2020. During the following spring semester, research was focused on SARS-CoV2 N2 gene concentrations at 5 sites sampled twice a week. Spearman correlations both with and without adjusting using human fecal viral indicators showed a significant correlation (p < 0.05) between human COVID-19 positive case counts and wastewater SARS-CoV-2 gene concentrations. Spearman correlations showed significant relationships between N1 gene concentrations and both TSS and turbidity, and between E gene concentrations and both pH and turbidity. These results suggest that wastewater signal increases with the census of infected individuals, in which the majority are asymptomatic, with a statistically significant (p-value <0.05) temporal correlation. The study design can be utilized as a platform for rapid trend tracking of SARS-CoV-2 variants and other diseases circulating in various communities.

Modeling fomite-mediated SARS-CoV-2 exposure through personal protective equipment doffing in a hospital environment.

Self-contamination during doffing of personal protective equipment (PPE) is a concern for healthcare workers (HCW) following SARS-CoV-2-positive patient care. Staff may subconsciously become contaminated through improper glove removal; so, quantifying this exposure is critical for safe working procedures. HCW surface contact sequences on a respiratory ward were modeled using a discrete-time Markov chain for: IV-drip care, blood pressure monitoring, and doctors' rounds. Accretion of viral RNA on gloves during care was modeled using a stochastic recurrence relation. In the simulation, the HCW then doffed PPE and contaminated themselves in a fraction of cases based on increasing caseload. A parametric study was conducted to analyze the effect of: (1a) increasing patient numbers on the ward, (1b) the proportion of COVID-19 cases, (2) the length of a shift, and (3) the probability of touching contaminated PPE. The driving factors for the exposure were surface contamination and the number of surface contacts. The results simulate generally low viral exposures in most of the scenarios considered including on 100% COVID-19 positive wards, although this is where the highest self-inoculated dose is likely to occur with median 0.0305 viruses (95% CI =0-0.6 viruses). Dose correlates highly with surface contamination showing that this can be a determining factor for the exposure. The infection risk resulting from the exposure is challenging to estimate, as it will be influenced by the factors such as virus variant and vaccination rates.

Bystander chemical exposures and injuries associated with nearby plastic sewer pipe manufacture: public health practice and lessons.

Cured-in-place pipes (CIPPs) are plastic liners manufactured inside existing damaged sanitary sewer, storm sewer, and water pipes that extend the service life of host pipes. This process often is conducted in neighborhoods and near roadways. Before, during, and after plastic manufacture, waste materials that include volatile materials are released into the air. Emissions from this manufacturing process can affect outdoor air quality and indoor air quality for buildings connected to the sewer system. We identified key issues and solicited stakeholder feedback to estimate and manage public health risks of CIPP-generated chemical air pollution. A work group representing 13 U.S. agencies and public health associations provided feedback and prioritized public health issues for action. To mitigate potential public and occupational health risks, additional testing and public health educational efforts were recommended. An improved understanding of CIPP chemical exposure pathways, as well as stakeholder needs and interests, is essential.

Respirators, face masks, and their risk reductions via multiple transmission routes for first responders within an ambulance.

First responders may have high SARS-CoV-2 infection risks due to working with potentially infected patients in enclosed spaces. The study objective was to estimate infection risks per transport for first responders and quantify how first responder use of N95 respirators and patient use of cloth masks can reduce these risks. A model was developed for two Scenarios: an ambulance transport with a patient actively emitting a virus in small aerosols that could lead to airborne transmission (Scenario 1) and a subsequent transport with the same respirator or mask use conditions, an uninfected patient; and remaining airborne SARS-CoV-2 and contaminated surfaces due to aerosol deposition from the previous transport (Scenario 2). A compartmental Monte Carlo simulation model was used to estimate the dispersion and deposition of SARS-CoV-2 and subsequent infection risks for first responders, accounting for variability and uncertainty in input parameters (i.e., transport duration, transfer efficiencies, SARS-CoV-2 emission rates from infected patients, etc.). Infection risk distributions and changes in concentration on hands and surfaces over time were estimated across sub-Scenarios of first responder respirator use and patient cloth mask use. For Scenario 1, predicted mean infection risks were reduced by 69%, 48%, and 85% from a baseline risk (no respirators or face masks used) of 2.9 × 10-2 ± 3.4 × 10-2 when simulated first responders wore respirators, the patient wore a cloth mask, and when first responders and the patient wore respirators or a cloth mask, respectively. For Scenario 2, infection risk reductions for these same Scenarios were 69%, 50%, and 85%, respectively (baseline risk of 7.2 × 10-3 ± 1.0 × 10-2). While aerosol transmission routes contributed more to viral dose in Scenario 1, our simulations demonstrate the ability of face masks worn by patients to additionally reduce surface transmission by reducing viral deposition on surfaces. Based on these simulations, we recommend the patient wear a face mask and first responders wear respirators, when possible, and disinfection should prioritize high use equipment.

Development of a dose-response model for Naegleria fowleri.

This study develops novel dose-response models for Naegleria fowleri from selected peer-reviewed experiments on the virulence based on the intranasal exposure pathway. One data set measured the response of mice intranasally inoculated with the amebae and the other study addressed the response of mice swimming in N. fowleri infected water. The measured response for both studies was death. All experimental data were best fit by the beta-Poisson dose-response model. The three swimming experiments could be pooled, and this is the final recommended model with an LD50 of 13,257 amebae. The results of this study provide a better estimate of the probability of the risk to N. fowleri exposure than the previous models developed based on an intravenous exposure. An accurate dose-response model is the first step in quantifying the risk of free-living amebae like N. fowleri, which pose risks in recreational environments and have been detected in drinking water and premise plumbing systems. A better understanding of this risk will allow for risk management that limits the ability for pathogen growth, proliferation, and exposure.

Modeling the role of fomites in a norovirus outbreak.

Norovirus accounts for a large portion of the gastroenteritis disease burden, and outbreaks have occurred in a wide variety of environments. Understanding the role of fomites in norovirus transmission will inform behavioral interventions, such as hand washing and surface disinfection. The purpose of this study was to estimate the contribution of fomite-mediated exposures to infection and illness risks in outbreaks. A simulation model in discrete time that accounted for hand-to-porous surfaces, hand-to-nonporous surfaces, hand-to-mouth, -eyes, -nose, and hand washing events was used to predict 17 hr of simulated human behavior. Norovirus concentrations originated from monitoring contamination levels on surfaces during an outbreak on houseboats. To predict infection risk, two dose-response models (fractional Poisson and 2F1 hypergeometric) were used to capture a range of infection risks. A triangular distribution describing the conditional probability of illness given an infection was multiplied by modeled infection risks to estimate illness risks. Infection risks ranged from 70.22% to 72.20% and illness risks ranged from 21.29% to 70.36%. A sensitivity analysis revealed that the number of hand-to-mouth contacts and the number of hand washing events had strong relationships with model-predicted doses. Predicted illness risks overlapped with leisure setting and environmental attack rates reported in the literature. In the outbreak associated with the viral concentrations used in this study, attack rates ranged from 50% to 86%. This model suggests that fomites may have accounted for 25% to 82% of illnesses in this outbreak. Fomite-mediated exposures may contribute to a large portion of total attack rates in outbreaks involving multiple transmission modes. The findings of this study reinforce the importance of frequent fomite cleaning and hand washing, especially when ill persons are present.

Dose-response relationships for environmentally mediated infectious disease transmission models.

Environmentally mediated infectious disease transmission models provide a mechanistic approach to examining environmental interventions for outbreaks, such as water treatment or surface decontamination. The shift from the classical SIR framework to one incorporating the environment requires codifying the relationship between exposure to environmental pathogens and infection, i.e. the dose-response relationship. Much of the work characterizing the functional forms of dose-response relationships has used statistical fit to experimental data. However, there has been little research examining the consequences of the choice of functional form in the context of transmission dynamics. To this end, we identify four properties of dose-response functions that should be considered when selecting a functional form: low-dose linearity, scalability, concavity, and whether it is a single-hit model. We find that i) middle- and high-dose data do not constrain the low-dose response, and different dose-response forms that are equally plausible given the data can lead to significant differences in simulated outbreak dynamics; ii) the choice of how to aggregate continuous exposure into discrete doses can impact the modeled force of infection; iii) low-dose linear, concave functions allow the basic reproduction number to control global dynamics; and iv) identifiability analysis offers a way to manage multiple sources of uncertainty and leverage environmental monitoring to make inference about infectivity. By applying an environmentally mediated infectious disease model to the 1993 Milwaukee Cryptosporidium outbreak, we demonstrate that environmental monitoring allows for inference regarding the infectivity of the pathogen and thus improves our ability to identify outbreak characteristics such as pathogen strain.

Development of a microbial dose response visualization and modelling application for QMRA modelers and educators.

Microbial dose response modelling is vital to a well-characterized microbial risk estimate. Dose response modelling is an inherently multidisciplinary field, which collates knowledge and data from disparate scientific fields. This multidisciplinary nature presents a key challenge to the expansion of microbial dose response modelling into new groups of researchers and modelers. This research employs a dose response optimization R code used in 18 peer-reviewed research studies to develop a multi-functional dose response software. The underlying R code performs an optimization of the two primary dose response models using the MLE method and outputs statistical analyses of the fits and bootstrapped uncertainty information for the models. VizDR (Visual Dose Response) was developed to provide microbial dose response modelling capabilities to a larger audience. VizDR is programmed in JavaScript with underlying Python scripts for intercommunication with Rserve. VizDR allows for dose response model visualization and optimization of a user's own experimental data.

Effect of Surface Sampling and Recovery of Viruses and Non-Spore-Forming Bacteria on a Quantitative Microbial Risk Assessment Model for Fomites.

Quantitative microbial risk assessment (QMRA) is a powerful decision analytics tool, yet it faces challenges when modeling health risks for the indoor environment. One limitation is uncertainty in fomite recovery for evaluating the efficiency of decontamination. Addressing this data gap has become more important as a result of response and recovery from a potential malicious pathogen release. To develop more accurate QMRA models, recovery efficiency from non-porous fomites (aluminum, ceramic, glass, plastic, steel, and wood laminate) was investigated. Fomite material, surface area (10, 100, and 900 cm(2)), recovery tool (swabs and wipes), initial concentration on the fomites and eluent (polysorbate 80, trypticase soy broth, and beef extract) were evaluated in this research. Recovery was shown to be optimized using polysorbate 80, sampling with wipes, and sampling a surface area of 10-100 cm(2). The QMRA model demonstrated, through a relative risk comparison, the need for recovery efficiency to be used in these models to prevent underestimated risks.

Development of an Interspecies Nested Dose-Response Model for Mycobacterium avium subspecies paratuberculosis.

Mycobacterium avium subspecies paratuberculosis (MAP) causes chronic inflammation of the intestines in humans, ruminants, and other species. It is the causative agent of Johne's disease in cattle, and has been implicated as the causative agent of Crohn's disease in humans. To date, no quantitative microbial risk assessment (QMRA) for MAP utilizing a dose-response function exists. The objective of this study is to develop a nested dose-response model for infection from oral exposure to MAP utilizing data from the peer-reviewed literature. Four studies amenable to dose-response modeling were identified in the literature search and optimized to the one-parameter exponential or two-parameter beta-Poisson dose-response models. A nesting analysis was performed on all permutations of the candidate data sets to determine the acceptability of pooling data sets across host species. Three of four data sets exhibited goodness of fit to at least one model. All three data sets exhibited good fit to the beta-Poisson model, and one data set exhibited goodness of fit, and best fit, to the exponential model. Two data sets were successfully nested using the beta-Poisson model with parameters α = 0.0978 and N50 = 2.70 × 10(2) CFU. These data sets were derived from sheep and red deer host species, indicating successful interspecies nesting, and demonstrate the highly infective nature of MAP. The nested dose-response model described should be used for future QMRA research regarding oral exposure to MAP.

Dose-response models incorporating aerosol size dependency for Francisella tularensis.

The effect of bioaerosol size was incorporated into predictive dose-response models for the effects of inhaled aerosols of Francisella tularensis (the causative agent of tularemia) on rhesus monkeys and guinea pigs with bioaerosol diameters ranging between 1.0 and 24 µm. Aerosol-size-dependent models were formulated as modification of the exponential and ß-Poisson dose-response models and model parameters were estimated using maximum likelihood methods and multiple data sets of quantal dose-response data for which aerosol sizes of inhaled doses were known. Analysis of F. tularensis dose-response data was best fit by an exponential dose-response model with a power function including the particle diameter size substituting for the rate parameter k scaling the applied dose. There were differences in the pathogen's aerosol-size-dependence equation and models that better represent the observed dose-response results than the estimate derived from applying the model developed by the International Commission on Radiological Protection (ICRP, 1994) that relies on differential regional lung deposition for human particle exposure.

Development of a machine learning model to support low cost real-time Legionella monitoring in premise plumbing systems.

Legionella pneumophila (L. pneumophila) is a pathogenic bacterium primarily known for causing Legionnaires' Disease which is known for high mortality rates, particularly in the elderly. With caseloads continuing to increase, further research is needed to improve our understanding of optimized sampling schema and safe limits of L. pneumophila, in part to target improved treatment options and realistic population-level risk modeling. Particularly in healthcare and other high-risk locations these become crucial and time sensitive needs. Therefore, we conceptualized this research as a means of incorporating easily measured physiochemical water quality parameters and generalization of the unique ecology of building water systems to build a computational model that can allow for more rapid and accurate decision making. This research uses the specific machine learning (ML) method called statistical learning theory to incorporate concentration of host cells, such as native amoeba, and physiochemical water quality parameters to estimate the probability of observing ranges of Legionella gene copy concentrations. Using data from previously published research on Legionella prevalence in a large building, our ML method trains the model on the relative impacts of physiochemical parameters on likely amoeba host cell occurrences. The model is expanded to estimate host cell concentrations using correlations and regressions operated through LASSO algorithms. After categorization variables from these results are then used to inform a logistic regression to provide an estimate of the probability of Legionella gene copy concentration ranges. In summary, conventional results generated by logistic regression and multiple linear regression quantified the associations among ecological conditions in the water and ability to predict a likely range of Legionella concentration in a management focused way. Further, two ML methods, PCA and LASSO, demonstrated feasibility in accurate real-time monitoring of Legionella through physiochemical indicators as evidenced with good accuracy of predictions based for validation results. Furthermore results demonstrate the vital need to account for the impact of water quality on building on host cells, and via their quantified water microbial ecology, not just Legionella concentrations.

Development and evaluation of an intensive short course: the Quantitative Microbial Risk Assessment Interdisciplinary Instructional Institute (QMRA III).

Quantitative microbial risk assessment (QMRA) is a growing interdisciplinary field addressing exposures to microbial pathogens and infectious disease processes. Risk science is inherently interdisciplinary, but few of the contributing disciplinary programs offer courses and training specifically in QMRA. To develop multidisciplinary training in QMRA, an annual 10-day long intensive workshop was conducted from 2015 to 2019-the Quantitative Microbial Risk Assessment Interdisciplinary Instructional Institute (QMRA III). National leaders in the fields of public health, engineering, microbiology, epidemiology, communications, public policy, and QMRA served as instructors and mentors over the course of the program. To provide cross-training, multidisciplinary teams of 5-6 trainees were created from the approximately 30 trainees each year. A formal assessment of the program was performed based on observations and surveys containing Likert-type scales and open-ended prompts. In addition, a longitudinal alumni survey was also disseminated to facilitate the future redevelopment of QMRA institutes and determine the impact of the program. Across all years, trainees experienced statistically significant increases (P < 0.05) in their perceptions of their QMRA abilities (e.g., use of specific computer programs) and knowledge of QMRA constructs (e.g., risk management). In addition, 12 publications, three conference presentations, and two research grants were derived from the QMRA III institute projects or tangential research. The success of QMRA III indicates that a short course format can effectively address many multidisciplinary training needs. Key features of QMRA III, including the inter-disciplinary training approach, hands-on exercises, real-world institute projects, and interaction through a mentoring process, were vital for training multidisciplinary teams housing multiple forms of expertise. Future QMRA institutes are being redeveloped to leverage hybrid learning formats that can further the multidisciplinary training and mentoring objectives.

Impact of terminal cleaning in rooms previously occupied by patients with healthcare-associated infections.

Healthcare associated infections (HAIs) are costly but preventable. A limited understanding of the effects of environmental cleaning on the riskiest HAI associated pathogens is a current challenge in HAI prevention. This project aimed to quantify the effects of terminal hospital cleaning practices on HAI pathogens via environmental sampling in three hospitals located throughout the United States. Surfaces were swabbed from 36 occupied patient rooms with a laboratory-confirmed, hospital- or community-acquired infection of at least one of the four pathogens of interest (i.e., Acinetobacter baumannii (A. baumannii), methicillin resistant Staphylococcus aureus (MRSA), vancomycin resistant Enterococcus faecalis/faecium (VRE), and Clostridioides difficile (C. difficile)). Six nonporous, high touch surfaces (i.e., chair handrail, bed handrail, nurse call button, desk surface, bathroom counter near the sink, and a grab bar near the toilet) were sampled in each room for Adenosine Triphosphate (ATP) and the four pathogens of interest before and after terminal cleaning. The four pathogens of interest were detected on surfaces before and after terminal cleaning, but their levels were generally reduced. Overall, C. difficile was confirmed on the desk (n = 2), while MRSA (n = 24) and VRE (n = 25) were confirmed on all surface types before terminal cleaning. After cleaning, only MRSA (n = 6) on bed handrail, chair handrail, and nurse call button and VRE (n = 5) on bathroom sink, bed handrail, nurse call button, toilet grab bar, and C. difficile (n = 1) were confirmed. At 2 of the 3 hospitals, pathogens were generally reduced by >99% during terminal cleaning. One hospital showed that VRE increased after terminal cleaning, MRSA was reduced by 73% on the nurse call button, and VRE was reduced by only 50% on the bathroom sink. ATP detections did not correlate with any pathogen concentration. This study highlights the importance of terminal cleaning and indicates room for improvement in cleaning practices to reduce surface contamination throughout hospital rooms.

Fluence-based QMRA model for bacterial photorepair and regrowth in drinking water after decentralized UV disinfection.

Ultraviolet disinfection is a promising solution for decentralized drinking water systems such as communal water taps. A potential health risk is enzymatic photorepair of pathogens after UV disinfection, which can result in regrowth of pathogens. Even though photorepair is a known issue, no formal risk assessments have been conducted for photorepair after UV disinfection in drinking water. The main objective was to construct a quantitative microbial risk assessment (QMRA) of photorepair after UV disinfection of drinking water in a decentralized system. UV disinfection and photorepair kinetics for E. coli were modelled using reproducible fluence-based determinations. Impacts of water collection patterns, and wavelength-dependent water container material transmittance, sunlight intensity, and photorepair enzyme absorbance were quantified. After UV disinfection by 16 or 40 mJ/cm2 of < 5-log microorganisms per L, risk of infection did not exceed 1-in-10,000 under conditions permitting E. coli photorepair. Risk from photorepair was less than 1-in-10,000 for photorepair light exposure < 0.75 h throughout the day for UV fluence 16 mJ/cm2 or greater. UV disinfection followed by solar disinfection surpassing photoreactivation during storage reduced risk below 1-in-10,000 for photorepair light exposure > 2.5 h between modelled times of 9 AM - 3 PM. The model can be expanded to other pathogens as UV fluence and photorepair fluence response kinetics become available, and this QMRA can be used to inform the placement of community water access points to reduce risk of photorepair and ensure adequate shelf life of UV disinfected water under safe storage conditions.

Prevalence of Legionella in a Public Building Water Plumbing System During COVID-19 Lockdown.

Although water stagnation is widely accepted as an essential factor supporting Legionella growth in plumbing systems and "water flashing" has become a common action for water quality control, additional monitoring data in practical spaces are still needed to back up this recommendation. The lockdown of public buildings during the COVID-19 pandemic provided an ideal time window to collect such data on a large scale. This study investigated how the long-term lockdown of a public building and the subsequent water stagnation impact water quality and the population of Legionella in water. From June 2020 to May 2021, 192 water samples were collected from a public building during the lockdown and reopening due to the COVID-19 pandemic. Each water sample was assessed for common physicochemical characteristics. Concentrations of Legionella and three species of free-living amoeba (FLA) (Acanthamoeba spp., Naegleria fowleri, and Hartmannella vermiformis) were monitored by qPCR. The data suggest that long-term stagnation promotes the population of Legionella spp., Acanthamoeba spp., and N. fowleri. Notable associations were observed between Legionella and FLA. These relationships were impacted by stagnation. These results provide important evidence that can inform future water quality management actions to minimize the risk of Legionella outbreaks by avoiding the occurrence of water stagnation.

Knowledge to Predict Pathogens: Legionella pneumophila Lifecycle Systematic Review Part II Growth within and Egress from a Host Cell.

Legionella pneumophila (L. pneumophila) is a pathogenic bacterium of increasing concern, due to its ability to cause a severe pneumonia, Legionnaires' Disease (LD), and the challenges in controlling the bacteria within premise plumbing systems. L. pneumophila can thrive within the biofilm of premise plumbing systems, utilizing protozoan hosts for protection from environmental stressors and to increase its growth rate, which increases the bacteria's infectivity to human host cells. Typical disinfectant techniques have proven to be inadequate in controlling L. pneumophila in the premise plumbing system, exposing users to LD risks. As the bacteria have limited infectivity to human macrophages without replicating within a host protozoan cell, the replication within, and egress from, a protozoan host cell is an integral part of the bacteria's lifecycle. While there is a great deal of information regarding how L. pneumophila interacts with protozoa, the ability to use this data in a model to attempt to predict a concentration of L. pneumophila in a water system is not known. This systematic review summarizes the information in the literature regarding L. pneumophila's growth within and egress from the host cell, summarizes the genes which affect these processes, and calculates how oxidative stress can downregulate those genes.

A model for in-vivo delivered dose estimation for inhaled bacillus anthracis spores in humans with interspecies extrapolation.

The quantitative yardstick for quantitative microbial risk assessment (QMRA) is the dose response assessment phase. In this phase of the QMRA paradigm a mathematical model is used to describe the relationship between host response (infection, disease, etc.) and pathogen dose. There are, however, key uncertainties which if addressed can expand our understanding of the dose response relationship and improve its accuracy. The dose response models most frequently used in this phase of QMRA are based on the average exposed dose (i.e., inhaled, ingested, etc.). However once inhaled, spores are considered infectious after being transported to a specific region of the lungs (alveoli), therefore, average exposed dose does not account for this required spore transport through the respiratory system. It is the aim of this manuscript to develop a model for the in vivo delivered dose to the alveolated region of the lungs that accounts for losses of spores through the respiratory system. A stochastic system is used to account for the physics in the respiratory system that account for the various sinks during respiration. This stochastic system is then integrated into the exponential and beta Poisson dose response models. The stochastic model is also then expanded to the respiratory systems of guinea pigs and rhesus macaques as these are common animal models. This work develops a framework for a new class of dose response models accounting for host physiology, making progress to understanding dose response heterogeneity among hosts.

Animal and human dose-response models for Brucella species.

Human Brucellosis is one of the most common zoonotic diseases worldwide. Disease transmission often occurs through the handling of domestic livestock, as well as ingestion of unpasteurized milk and cheese, but can have enhanced infectivity if aerosolized. Because there is no human vaccine available, rising concerns about the threat of Brucellosis to human health and its inclusion in the Center for Disease Control's Category B Bioterrorism/Select Agent List make a better understanding of the dose-response relationship of this microbe necessary. Through an extensive peer-reviewed literature search, candidate dose-response data were appraised so as to surpass certain standards for quality. The statistical programming language, "R," was used to compute the maximum likelihood estimation to fit two models, the exponential and the approximate beta-Poisson (widely used for quantitative risk assessment) to dose-response data. Dose-response models were generated for prevalent species of Brucella: Br. suis, Br. melitensis, and Br. abortus. Dose-response models were created for aerosolized Br. suis exposure to guinea pigs from pooled studies. A parallel model for guinea pigs inoculated through both aerosol and subcutaneous routes with Br. melitensis showed that the median infectious dose corresponded to a 30 colony-forming units (CFU) dose of Br. suis, much less than the N(50) dose of about 94 CFU for Br. melitensis organisms. When Br. melitensis was tested subcutaneously on mice, the N(50) dose was higher, 1,840 CFU. A dose-response model was constructed from pooled data for mice, rhesus macaques, and humans inoculated through three routes (subcutaneously/aerosol/intradermally) with Br. melitensis.