Nosocomial, or hospital-acquired, infections are a key determinant of patient health in healthcare facilities, leading to longer stays and increased mortality. In addition to the direct effects on infected patients, the burden imposed by nosocomial infections impacts both staff and other patients by increasing the load on the healthcare system. The appropriate infection control response may differ depending on whether the infection was acquired in the hospital or the community. For example, nosocomial outbreaks may require ward closures to reduce the risk of onward transmission, whilst this may not be an appropriate response to repeated importations of infections from outside the facility. Unfortunately, it is often unclear whether an infection detected in a healthcare facility is nosocomial, as the time of infection is unobserved. Given this, there is a strong case for the development of models that can integrate multiple datasets available in hospitals to assess whether an infection detected in a hospital is nosocomial. When assessing nosocomiality, it is beneficial to take into account both whether the timing of infection is consistent with hospital acquisition and whether there are any likely candidates within the hospital who could have been the source of the infection. In this work, we developed a Bayesian model which jointly estimates whether a given infection detected in hospital is nosocomial and whether it came from a set of individuals identified as candidates by hospital staff. The model coherently integrates pathogen genetic information, the timings of epidemiological events, such as symptom onset, and location data on the infected patient and candidate infectors. We illustrated this model on a real hospital dataset showing both its output and how the impact of the different data sources on the assessed probabilities are contingent on what other data has been included in the model, and validated the calibration of the predictions against simulated data.

The nosocomial transmission of respiratory pathogens is an ongoing healthcare challenge, with consequences for the health of vulnerable individuals. Outbreaks in hospitals can require the closure of bays or entire wards, reducing hospital capacity and having a financial impact upon healthcare providers. Here we evaluate a novel strategy of pre-exposure prophylaxis as a means to reduce the nosocomial transmission of SARS-CoV-2. We model the effect of ursodeoxycholic acid (UDCA) upon levels of angiotensin-converting enzyme 2 (ACE2) expression, SARS-CoV-2 viral entry, and ultimately the probability of an infection. We then implement this model within simulations describing the spread of SARS-CoV-2 infections within a hospital context, simulating an intervention in which UDCA is given to patients on a ward for 10 days following the detection of a case of SARS-CoV-2 on that ward. Under default model parameters we infer a potential 17% reduction in the nosocomial transmission of SARS-CoV-2 to patients, with increased importation of cases into the hospital increasing the effectiveness of the intervention, and of the order 1000-2000 patient treatment days per nosocomial patient infection prevented. Our study provides preliminary evidence of the value of pre-exposure prophylaxis with UDCA as a strategy to reduce nosocomial SARS-CoV-2 transmission.

The mechanism by which hepatitis C virus (HCV) gains entry into cells is a complex one, involving a broad range of host proteins.Entry is a critical phase of the viral lifecycle, and a potential target for therapeutic or vaccine-mediated intervention.However, the mechanics of HCV entry remain poorly understood.Here we describe a novel computational model of viral entry, encompassing the relationship between HCV and the key host receptors CD81 and SR-B1.We conduct experiments to thoroughly quantify the influence of an increase or decrease in receptor availability upon the extent of viral entry.We use these data to build and parameterise a mathematical model, which we then validate by further experiments.Our results are consistent with sequential HCV-receptor interactions, whereby initial interaction between the HCV E2 glycoprotein and SR-B1 facilitates the accumulation CD81 receptors, leading to viral entry.However, we also demonstrate that a small minority of viruses can achieve entry in the absence of SR-B1.Our model estimates the impact of the different obstacles that viruses must surmount to achieve entry; among virus particles attaching to the cell surface, around one third of viruses accumulate sufficient CD81 receptors, of which 4-8% then complete the subsequent steps to achieve productive infection.Furthermore, we make estimates of receptor stoichiometry; in excess of 10 receptors are likely to be required to achieve viral entry.Our model provides a tool to investigate the entry characteristics of HCV variants and outlines a framework for future quantitative studies of the multi-receptor dynamics of HCV entry. Author summaryHepatitis C virus affects approximately 70 million people worldwide, resulting in a significant impact on human health.The virus initiates infection through a complex set of interactions with proteins on the surface of human cells.Here we combine experimental approaches with a new mathematical model to study the process of viral entry.Our model is successful in capturing the behaviour of experiments, which show how changes in the amount of the human proteins CD81 and SR-B1 expressed by a cell alter the probability of a virus getting into a cell.Our model suggests that more than 10 CD81 receptors are

UNLABELLED: An increasingly common method for studying evolution is the collection of time-resolved short-read sequence data. Such datasets allow for the direct observation of rapid evolutionary processes, as might occur in natural microbial populations and in evolutionary experiments. In many circumstances, evolutionary pressure acting upon single variants can cause genomic changes at multiple nearby loci. SAMFIRE is an open-access software package for processing and analyzing sequence reads from time-resolved data, calling important single- and multi-locus variants over time, identifying alleles potentially affected by selection, calculating linkage disequilibrium statistics, performing haplotype reconstruction and exploiting time-resolved information to estimate the extent of uncertainty in reported genomic data. AVAILABILITY AND IMPLEMENTATION: C ++ code may be found at https://github.com/cjri/samfire/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The within-host evolution of influenza is a vital component of its epidemiology. A question of particular interest is the role that selection plays in shaping the viral population over the course of a single infection. We here describe a method to measure selection acting upon the influenza virus within an individual host, based upon time-resolved genome sequence data from an infection. Analysing sequence data from a transmission study conducted in pigs, describing part of the haemagglutinin gene (HA1) of an influenza virus, we find signatures of non-neutrality in six of a total of sixteen infections. We find evidence for both positive and negative selection acting upon specific alleles, while in three cases, the data suggest the presence of time-dependent selection. In one infection we observe what is potentially a specific immune response against the virus; a non-synonymous mutation in an epitope region of the virus is found to be under initially positive, then strongly negative selection. Crucially, given the lack of homologous recombination in influenza, our method accounts for linkage disequilibrium between nucleotides at different positions in the haemagglutinin gene, allowing for the analysis of populations in which multiple mutations are present at any given time. Our approach offers a new insight into the dynamics of influenza infection, providing a detailed characterisation of the forces that underlie viral evolution.

Abstract Motivation: The observation of positive selection acting on a mutant indicates that the corresponding mutation has some form of functional relevance. Determining the fitness effects of mutations thus has relevance to many interesting biological questions. One means of identifying beneficial mutations in an asexual population is to observe changes in the frequency of marked subsets of the population. We here describe a method to estimate the establishment times and fitnesses of beneficial mutations from neutral marker frequency data. Results: The method accurately reproduces complex marker frequency trajectories. In simulations for which positive selection is close to 5% per generation, we obtain correlations upwards of 0.91 between correct and inferred haplotype establishment times. Where mutation selection coefficients are exponentially distributed, the inferred distribution of haplotype fitnesses is close to being correct. Applied to data from a bacterial evolution experiment, our method reproduces an observed correlation between evolvability and initial fitness defect. Availability: A C++ implementation of the inference tool is available under GNU GPL license (http://www.sanger.ac.uk/resources/software/optimist/). Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.