Dose-response models incorporating aerosol size dependency for Francisella tularensis

Home quarantine: A numerical evaluation of SARS-CoV-2 spread in a single-family house

The worldwide pandemic of SARS-CoV-2 which causes coronavirus disease 2019 (COVID-19) has resulted in first-time responses in recent history as most of affected countries are confining residents to their homes. When sick people are detected, home quarantine is usually recommended because of the lack of available hospital rooms for first-stage symptoms that do not require constant medical monitoring. In this situation, one can thus wonder about the transmission of the disease to other household members. In this paper, we numerically investigate the transport of the aerosol generated by an infected person quarantined to his bedroom to the other rooms in a typical French detached house by performing TRNSYS-CONTAM simulations. The intent here is to assess the exposure concentration to the virus of the other household members when simple strategies are employed to reduce the risk of airborne transmission. Due to the uncertainty regarding the evaluation of different parameters such as the emission of contaminated droplets and the infectivity of the occupants, critical cases have been considered in this study. The main results show that, even if remediation actions lower the exposure of other occupants, the risk of contamination remains high even if the contagious person is quarantined to its bedroom.

Seroconversion and fever are dose-dependent in a nonhuman primate model of inhalational COVID-19

While evidence exists supporting the potential for aerosol transmission of SARS-CoV-2, the infectious dose by inhalation remains unknown. In the present study, the probability of infection following inhalation of SARS-CoV-2 was dose-dependent in a nonhuman primate model of inhalational COVID-19. The median infectious dose, assessed by seroconversion, was 52 TCID50 (95% CI: 23-363 TCID50), and was significantly lower than the median dose for fever (256 TCID50, 95% CI: 102-603 TCID50), resulting in a group of animals that developed an immune response post-exposure but did not develop fever or other clinical signs of infection. In a subset of these animals, virus was detected in nasopharyngeal and/or oropharyngeal swabs, suggesting that infected animals without signs of disease are able to shed virus and may be infectious, which is consistent with reports of asymptomatic spread in human cases of COVID-19. These results suggest that differences in exposure dose may be a factor influencing disease presentation in humans, and reinforce the importance of public health measures that limit exposure dose, such as social distancing, masking, and increased ventilation. The dose-response data provided by this study are important to inform disease transmission and hazard modeling, and, ultimately, mitigation strategies. Additionally, these data will be useful to inform dose selection in future studies examining the efficacy of therapeutics and vaccines against inhalational COVID-19, and as a baseline in healthy, young adult animals for assessment of the importance of other factors, such as age, comorbidities, and viral variant, on the infectious dose and disease presentation.

A human time dose response model for Q fever

The causative agent of Q fever, Coxiella burnetii, has the potential to be developed for use in biological warfare and it is classified as a bioterrorism threat agent by the Centers for Disease Control and Prevention (CDC) and as a category B select agent by the National Institute of Allergy and Infectious Diseases (NIAID). In this paper we focus on the in-host properties that arise when an individual inhales a dose of C. burnetii and establish a human time-dose response model. We also propagate uncertainty throughout the model allowing us to robustly estimate key properties including the infectious dose and incubation period. Using human study data conducted in the 1950's we conclude that the dose required for a 50% probability of infection is about 15 organisms, and that one inhaled organism of C. burnetti can cause infection in 5% of the exposed population. In addition, we derive a low dose incubation period of 17.6 days and an extracellular doubling time of half a day. In conclusion this paper provides a framework for detailing the parameters and approaches that would be required for risk assessments associated with exposures to C. burnetii that might cause human infection.

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.

Evaluation of Inhaled Versus Deposited Dose Using the Exponential Dose-Response Model for Inhalational Anthrax in Nonhuman Primate, Rabbit, and Guinea Pig

The application of the exponential model is extended by the inclusion of new nonhuman primate (NHP), rabbit, and guinea pig dose-lethality data for inhalation anthrax. Because deposition is a critical step in the initiation of inhalation anthrax, inhaled doses may not provide the most accurate cross-species comparison. For this reason, species-specific deposition factors were derived to translate inhaled dose to deposited dose. Four NHP, three rabbit, and two guinea pig data sets were utilized. Results from species-specific pooling analysis suggested all four NHP data sets could be pooled into a single NHP data set, which was also true for the rabbit and guinea pig data sets. The three species-specific pooled data sets could not be combined into a single generic mammalian data set. For inhaled dose, NHPs were the most sensitive (relative lowest LD50) species and rabbits the least. Improved inhaled LD50 s proposed for use in risk assessment are 50,600, 102,600, and 70,800 inhaled spores for NHP, rabbit, and guinea pig, respectively. Lung deposition factors were estimated for each species using published deposition data from Bacillus spore exposures, particle deposition studies, and computer modeling. Deposition was estimated at 22%, 9%, and 30% of the inhaled dose for NHP, rabbit, and guinea pig, respectively. When the inhaled dose was adjusted to reflect deposited dose, the rabbit animal model appears the most sensitive with the guinea pig the least sensitive species.

Microbial dose response modeling: past, present, and future

The understanding of the risk to humans from exposure to pathogens has been firmly put into a risk assessment framework. A key element of applying this approach is the understanding of the relationship between dose and response for particular pathogens. This understanding has progressed from early use of threshold concepts ("minimal infectious dose") thru multiple generations of models. Generation 1 models describe probability of response to exposed dose. Generation 2 models incorporate host factors (e.g., age) and/or pathogen factors (e.g., particle size of inhaled agents). Generation 3 models describe the rate at which effects develop, i.e. the epidemic curve. These (generation 1 through three models) have been developed and used in multiple contexts. Beyond Generation 3 lies an opportunity for the deep incorporation of in vivo physiological responses and the coupling of the individual host dynamics to the dynamics of spread of contagious diseases in the population. This would enable more direct extrapolation from controlled dosing studies to estimate population level effects. There remain also needs to understand broader categories of infectious agents, including pathogenic amoebae and fungi. More advanced models need to be validated against well-characterized human outbreak data.