A human time dose response model for Q fever

The Burr distribution as a model for the delay between key events in an individual's infection history

Understanding the temporal relationship between key events in an individual's infection history is crucial for disease control. Delay data between events, such as infection and symptom onset times, is doubly censored because the exact time at which these key events occur is generally unknown. Current mathematical models for delay distributions are derived from heuristic justifications. Here, we derive a new model for delay distributions, specifically for incubation periods, motivated by bacterial-growth dynamics that lead to the Burr family of distributions being a valid modelling choice. We also incorporate methods within these models to account for the doubly censored data. Our approach provides biological justification in the derivation of our delay distribution model, the results of fitting to data highlighting the superiority of the Burr model compared to currently used models when the mode of the distribution is clearly defined or when the distribution tapers off. Under these conditions, our results indicate that the derived Burr distribution is a better-performing model for incubation-period data than currently used methods, with the derived Burr distribution being 13 times more likely to be a better-performing model than the gamma distribution for Legionnaires' disease based on data from a known outbreak.

Which Factors Influence the Consumption of Antibiotics in Q-Fever-Positive Dairy Farms in Northern Germany?

It was the aim of this study to examine whether the usage of the vaccine COXEVAC® (Ceva Santé Animale) could reduce the consumption of antibiotics in Q-fever-positive dairy farms. Additionally, the effects of other herd-level factors on the consumption of antibiotics were investigated. A total of 36 farms with vaccination and 13 farms without vaccination participated in this longitudinal cohort study. In all herds, Coxiella burnetii had been directly or indirectly diagnosed. To compare the treatment frequency of antibiotics between the vaccinated group and the non-vaccinated group, the consumption of antibiotics for each farm was collected using the veterinary documents about the application and delivery of antibiotics. To gather detailed information about herd data, nutrition, milking management, housing, and animal health, the farmers were interviewed with the help of a questionnaire. The results thereof suggest that there might be an association between the vaccination against Q fever and a reduced consumption of antibiotics. Neither herd size nor milk yield level influenced the consumption of antibiotics in the study herds. Type of flooring and udder-cluster disinfection while milking were associated with a lower and higher therapy frequency, respectively. Further studies are necessary to elucidate the cause-effect relationship between vaccination and the consumption of antibiotics.

Using mixture density networks to emulate a stochastic within-host model of Francisella tularensis infection

For stochastic models with large numbers of states, analytical techniques are often impractical, and simulations time-consuming and computationally demanding. This limitation can hinder the practical implementation of such models. In this study, we demonstrate how neural networks can be used to develop emulators for two outputs of a stochastic within-host model of Francisella tularensis infection: the dose-dependent probability of illness and the incubation period. Once the emulators are constructed, we employ Markov Chain Monte Carlo sampling methods to parameterize the within-host model using records of human infection. This inference is only possible through the use of a mixture density network to emulate the incubation period, providing accurate approximations of the corresponding probability distribution. Notably, these estimates improve upon previous approaches that relied on bacterial counts from the lungs of macaques. Our findings reveal a 50% infectious dose of approximately 10 colony-forming units and we estimate that the incubation period can last for up to 11 days following low dose exposure.

Tracking the Source of Human Q Fever from a Southern French Village: Sentinel Animals and Environmental Reservoir

Coxiella burnetii, also known as the causal agent of Q fever, is a zoonotic pathogen infecting humans and several animal species. Here, we investigated the epidemiological context of C. burnetii from an area in the Hérault department in southern France, using the One Health paradigm. In total, 13 human cases of Q fever were diagnosed over the last three years in an area comprising four villages. Serological and molecular investigations conducted on the representative animal population, as well as wind data, indicated that some of the recent cases are likely to have originated from a sheepfold, which revealed bacterial contamination and a seroprevalence of 47.6%. However, the clear-cut origin of human cases cannot be ruled out in the absence of molecular data from the patients. Multi-spacer typing based on dual barcoding nanopore sequencing highlighted the occurrence of a new genotype of C. burnetii. In addition, the environmental contamination appeared to be widespread across a perimeter of 6 km due to local wind activity, according to the seroprevalence detected in dogs (12.6%) and horses (8.49%) in the surrounding populations. These findings were helpful in describing the extent of the exposed area and thus supporting the use of dogs and horses as valuable sentinel indicators for monitoring Q fever. The present data clearly highlighted that the epidemiological surveillance of Q fever should be reinforced and improved.

Coxiella burnetii (Q-fever) outbreak associated with non-occupational exposure in a semi-urban area of western Croatia in 2022

In March 2022, an outbreak of Q fever (Coxiella burnetii) with non-occupational exposure was confirmed in a semi-urban area in Čavle, Croatia. Veterinary and human epidemiological investigations were conducted to identify the source of the outbreak and to implement appropriate control measures. Three farms were settled next to each other near the homes of the first human cases at the end of the street. The closest farm was less than 500 meters away. These farms contained 161 adult sheep and goats. Among the animal samples analysed, all 16 goats (100%) and 24/50 sheep (48%) tested positive for C. burnetii IgM/IgG antibodies, phase I and II. One out of five sheeps' vaginal swabs were C. burnetti DNA positive. Human testing revealed 20 confirmed and three probable cases (9/23 pneumonia, 2/23 hepatitis, 21/23 fever), with three hospitalizations, and one death. Twenty-seven cases were discarded following negative laboratory results. The epidemiological investigation revealed airborne transmission as the most likely route of transmission. Multiple logistic regression analyses were used to evaluate risk factors for Q fever infection. Persons who were near the farms (≤750 m) (OR 4.5; 95% CI = 1.1-18.3) and lived in the nearest street to the farms had the highest risk of contracting Q fever (OR 3.7; 95% CI = 1.1-13.6). Decreased rainfall compared to monthly averages was recorded in the months prior to the outbreak with several days of strong wind in January preceding the outbreak. This was the largest Q fever outbreak in the county in the last 16 years, which was unexpected due to its location and non-occupational exposure. To stop the outbreak, numerous intensive biosecurity measures were implemented. The outbreak highlights the importance of urban development strategies to limit the number of animal housing near residential areas while providing regular biosecurity measures to prevent infections in livestock.

Serological Prevalence of and Risk Factors for Coxiella burnetti Infection in Women of Punjab Province, Pakistan

Background: Coxiella burnetii, the etiological agent of Q (query) fever, provokes abortions in ruminants and is suspected to cause adverse pregnancy outcomes in women. Infection of pregnant women is linked with high mortality and morbidity of the fetus and the mother is at high risk to acquire chronic Q fever. This research was conducted to evaluate the prevalence of Q fever in women and to detect associated risk factors in four districts of Punjab Province, Pakistan. Methods: A total of 297 blood samples were obtained from 147 pregnant and 150 non-pregnant women of the districts Okara, Jhang, Chiniot and Faisalabad of Punjab, Pakistan. Data related to risk factors and demographic parameters were collected using a questionnaire. Serum samples were screened for phase I and phase II specific IgG antibodies for antigens of phase I and phase II using ELISA tests. Univariate and binary regression were used to analyze important risk factors of Q fever. Results: Twenty-five serum samples (8.4%) were found seropositive for Q fever. Seventeen women were positive for Phase-I and twenty-one were positive for phase-II antibodies. Highest and statistically significant (p < 0.05) seroprevalence of 17.1% was observed in Faisalabad. Age, urbanicity, living status, pregnancy status, abortion history, occupation, and consumption of tap water were positively correlated (p < 0.05) with Q fever, while being aged, urbanity, low income, contact with animals and consumption of tap water was identified as potential risk factors. Conclusions: Q fever is prevalent in women of Pakistan. There is a need for an awareness program about the importance of C. burnetii infections and prevention strategies in women during pregnancy to minimize adverse pregnancy outcomes.

Contemporary diagnostics for medically relevant fastidious microorganisms belonging to the genera Anaplasma, Bartonella, Coxiella, Orientia, and Rickettsia

Many of the human infectious pathogens—especially the zoonotic or vector-borne bacteria—are fastidious organisms that are difficult to cultivate because of their strong adaption to the infected host culminating in their near-complete physiological dependence on this environment. These bacterial species exhibit reduced multiplication rates once they are removed from their optimal ecological niche. This fact complicates the laboratory diagnosis of the disease and hinders the detection and further characterization of the underlying organisms, e.g. at the level of their resistance to antibiotics due to their slow growth. Here, we describe the current state of microbiological diagnostics for five genera of human pathogens with a fastidious laboratory lifestyle. For Anaplasma spp., Bartonella spp., Coxiella burnetii, Orientia spp. and Rickettsia spp., we will summarize the existing diagnostic protocols, the specific limitations for implementation of novel diagnostic approaches and the need for further optimization or expansion of the diagnostic armamentarium. We will reflect upon the diagnostic opportunities provided by new technologies including mass spectrometry and next-generation nucleic acid sequencing. Finally, we will review the (im)possibilities of rapidly developing new in vitro diagnostic tools for diseases of which the causative agents are fastidiously growing and therefore hard to detect.

A Stochastic Intracellular Model of Anthrax Infection With Spore Germination Heterogeneity

We present a stochastic mathematical model of the intracellular infection dynamics of Bacillus anthracis in macrophages. Following inhalation of B. anthracis spores, these are ingested by alveolar phagocytes. Ingested spores then begin to germinate and divide intracellularly. This can lead to the eventual death of the host cell and the extracellular release of bacterial progeny. Some macrophages successfully eliminate the intracellular bacteria and will recover. Here, a stochastic birth-and-death process with catastrophe is proposed, which includes the mechanism of spore germination and maturation of B. anthracis. The resulting model is used to explore the potential for heterogeneity in the spore germination rate, with the consideration of two extreme cases for the rate distribution: continuous Gaussian and discrete Bernoulli. We make use of approximate Bayesian computation to calibrate our model using experimental measurements from in vitro infection of murine peritoneal macrophages with spores of the Sterne 34F2 strain of B. anthracis. The calibrated stochastic model allows us to compute the probability of rupture, mean time to rupture, and rupture size distribution, of a macrophage that has been infected with one spore. We also obtain the mean spore and bacterial loads over time for a population of cells, each assumed to be initially infected with a single spore. Our results support the existence of significant heterogeneity in the germination rate, with a subset of spores expected to germinate much later than the majority. Furthermore, in agreement with experimental evidence, our results suggest that most of the spores taken up by macrophages are likely to be eliminated by the host cell, but a few germinated spores may survive phagocytosis and lead to the death of the infected cell. Finally, we discuss how this stochastic modelling approach, together with dose-response data, allows us to quantify and predict individual infection risk following exposure.

A Novel Marmoset (Callithrix jacchus) Model of Human Inhalational Q Fever

Common marmosets (Callithrix jacchus) were shown to be susceptible to inhalational infection with Coxiella burnetii, in a dose-dependent manner, producing a disease similar to human Q fever, characterized by a resolving febrile response. Illness was also associated with weight loss, liver enzyme dysfunction, characteristic cellular activation, circulating INF-γ and bacteraemia. Viable C. burnetii was recovered from various tissues during disease and from 75% of the animal's lungs on 28 days post challenge, when there were no overt clinical features of disease but there was histological evidence of macrophage and lymphocyte infiltration into the lung resulting in granulomatous alveolitis. Taken together, these features of disease progression, physiology and bacterial spread appear to be consistent with human disease and therefore the common marmoset can be considered as a suitable model for studies on the pathogenesis or the development of medical counter measures of inhalational Q fever.

Dose-Response Modeling: Extrapolating From Experimental Data to Real-World Populations

Dose-response modeling of biological agents has traditionally focused on describing laboratory-derived experimental data. Limited consideration has been given to understanding those factors that are controlled in a laboratory, but are likely to occur in real-world scenarios. In this study, a probabilistic framework is developed that extends Brookmeyer's competing-risks dose-response model to allow for variation in factors such as dose-dispersion, dose-deposition, and other within-host parameters. With data sets drawn from dose-response experiments of inhalational anthrax, plague, and tularemia, we illustrate how for certain cases, there is the potential for overestimation of infection numbers arising from models that consider only the experimental data in isolation.

Theoretical investigation of stochastic clearance of bacteria: first-passage analysis

Understanding mechanisms of bacterial eradication is critically important for overcoming failures of antibiotic treatments. Current studies suggest that the clearance of large bacterial populations proceeds deterministically, while for smaller populations, the stochastic effects become more relevant. Here, we develop a theoretical approach to investigate the bacterial population dynamics under the effect of antibiotic drugs using a method of first-passage processes. It allows us to explicitly evaluate the most important characteristics of bacterial clearance dynamics such as extinction probabilities and extinction times. The new meaning of minimal inhibitory concentrations for stochastic clearance of bacterial populations is also discussed. In addition, we investigate the effect of fluctuations in population growth rates on the dynamics of bacterial eradication. It is found that extinction probabilities and extinction times generally do not correlate with each other when random fluctuations in the growth rates are taking place. Unexpectedly, for a significant range of parameters, the extinction times increase due to these fluctuations, indicating a slowing in the bacterial clearance dynamics. It is argued that this might be one of the initial steps in the pathway for the development of antibiotic resistance. Furthermore, it is suggested that extinction times is a convenient measure of bacterial tolerance.