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Everything, everywhere, all at once - Surveillance and molecular epidemiology reveal Melissococcus plutonius is endemic among Michigan, US beekeeping operations of all sizes and present in some honey bee colonies year-round

European foulbrood (EFB) is a severe bacterial disease of honey bee brood often leading to significant declines in colony health and honey production. The dearth of data on this disease in the United States (US) complicates response efforts. In this study, we combine surveillance and molecular epidemiology to investigate prevalence, diversity, and transmission dynamics of Melissococcus plutonius, the causative agent of EFB, in US honey bee colonies. Rates of infection and disease were found to be seasonal, with prevalence peaking in June when over half the colonies screened were infected. Whole genome, single nucleotide polymorphism analysis revealed wide genetic diversity even within a single hive. Operations often had multiple genotypes present which varied from year to year, consistent with high rates of transmission and reinfection. Prevalence and whole genome data provided here will be critical in tracking the efficacy of mitigation efforts and underscore the necessity of additional epidemiological investigations.

genomics↗

Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes

Inferences from population genomic data provide valuable insights into the demographic history of a population. Likewise, in genomic epidemiology, pathogen genomic data provide key insights into epidemic dynamics and potential sources of transmission. Yet predicting what information will be gained from genomic data about variables of interest and how different sampling strategies will impact the quality of downstream inferences remains challenging. As a result, population genomics largely lacks theory to guide decisions on how best to sample individuals for genomic sequencing. By adopting a sequential decision making framework, we show how Markov decision processes (MDPs) can be applied to jointly model a populations dynamics along with the sampling process. Critically, these MDPs allow us to compute the expected long-term value of sampling in terms of information gained about estimated variables. This in turn allows us to very efficiently explore and identify optimal sampling strategies. To illustrate our framework, we develop MDPs for three common demographic and epidemiological inference problems: estimating population growth rates, minimizing the transmission distance between sampled individuals and estimating migration rates between subpopulations. In each case, the MDP allows us to identify optimal sampling strategies that maximize the information gained from genomic data while minimizing costs.

genomics↗

Clustering of plasmid genomes for genomic epidemiology by using rearrangement distances, with pling

Integration of plasmids into genomic epidemiology is challenging, because there are no clearly defined evolving-units (equivalent to species), and because plasmids appear to evolve as much by structural change (rearrangements, insertions and deletions) as by mutation (1). Further, plasmids transfer horizontally between bacterial hosts (2), and thus a model beyond just a phylogeny is needed to integrate their genetic information with that of their hosts. Pling (3) is a tool designed to measure a genetic distance between plasmids that is related to how they empirically appear to evolve, by measuring the distance between two plasmids as the minimum number of structural changes needed to change one plasmid into the other (ignoring SNP differences). Having done this, it constructs a relatedness network of the plasmids under study, and then clusters them into groups that are credibly recently related. We give here a protocol for running pling, and how we integrate its information with plasmid typing and SNP information. Together, these provide a system for deciding which plasmids are worth treating as "the same plasmid" for the purposes of epidemiology, quantifying their relatedness in terms of rearrangements and SNPs, and then seeing how they are distributed across the host phylogeny.

bioinformatics↗

Unmasking Vibrio paracholerae: Genomic Reclassification and Epidemiology

The genus Vibrio encompasses globally relevant pathogens, of which Vibrio cholerae is the best known due to its role in cholera. Closely related species within the Cholerae clade--Vibrio paracholerae, Vibrio metoecus, and Vibrio tarriae--were long misclassified as non-O1/O139 V. cholerae. The objective of this study was to analyze all 13,000+ available V. cholerae genomes in GenBank to determine the presence of species from the Cholerae clade. Genome-wide analyses using Mash, Whole-genome based Average Nucleotide Identity (gANI), and digital DNA-DNA hybridization (dDDH) reclassified 193 unique genomes as V. paracholerae, while V. metoecus and V. tarriae were not identified. Phylogenomic analyses revealed that V. paracholerae forms distinct but relatively homogeneous lineages, spanning clinical, environmental, and animal sources over a period of more than a century. Virulence profiling revealed the absence of CTX and TCP; however, most genomes exhibited other virulence factors, including hemolysins, RTX toxins, colix toxin, and a conserved type VI secretion system. Resistome analysis revealed several antibiotic resistance genes, many of which were incorporated into superintegrons, highlighting the role of V. paracholerae as a reservoir of resistance determinants. Importantly, two genomic markers exhibited high discriminatory power for species identification, providing robust tools for epidemiological surveillance. These findings underscore the relevance of V. paracholerae for clinical and epidemiological monitoring.

genomics↗

Observational epidemiological studies can mitigate genetic confounding with the genetic relatedness matrix

Observational studies are commonly used in psychology and epidemiology to identify risk factors correlated with health outcomes. However, these studies are vulnerable to confounding when shared genetic variation influences both the putative risk factor and outcome. Researchers have historically controlled for this type of genetic confounding using polygenic scores, but these scores are often noisy and biased estimators of a traits genetic component. Here, we develop a method that leverages a genetic relatedness matrix (GRM) to control genetic confounding when testing for non-genetic risk factors. In simulations, we find that our method outperforms existing approaches, particularly at sample sizes that are large by the standards of much human research but smaller than datasets often used in human genetics. We also demonstrate that existing methods are susceptible to poor GWAS portability, whereas our method is inherently robust to such concerns. Finally, we apply our method to the UK Biobank to re-analyze social risk factors for health outcomes in previously understudied cohorts. Significance statementLarge observational datasets are often used in epidemiology or social sciences to identify risk factors associated with health outcomes. However, these studies can be misleading when the putative risk factor and outcome are both complex genetic traits: a correlated genetic basis can create spurious associations between a putative risk factor and outcome. Existing approaches to address this problem typically rely on GWAS summary statistics, which require biobank-scale datasets. In this work, we introduce an alternative method that uses a genetic relatedness matrix (GRM) to control for genetic confounding directly. We show that our approach is closely related to existing methods but outperforms them in smaller datasets, making it an especially valuable tool for researchers who work on understudied traits and populations.

genetics↗

Simulation based-inference of epidemiological and phylodynamic models via Neural Posterior Estimation

Mathematical models play a central role in understanding and forecasting infectious disease dynamics, but parameter inference is often difficult when likelihoods are intractable. Simulation-based inference (SBI) circumvents this limitation by relying on model simulations. Traditional SBI methods, such as Approximate Bayesian Computation, typically depend on handcrafted summary statistics and scale poorly in high-dimensional settings. Recent advances address these challenges by using flexible statistical and machine-learning surrogates to approximate likelihoods or posterior distributions. Neural Posterior Estimation (NPE) directly learns the posterior from simulated data using neural density estimators, automatically extracting informative representations without ad-hoc summaries. Despite its potential, NPE has been rarely applied in infectious disease epidemiology and phylodynamics. Here, we evaluate NPE for parameter inference in mechanistic models using data from the 2014 Ebola outbreak in Sierra Leone. We consider two case studies: a compartmental transmission model fitted to case and death time series, and a birth-death phylodynamic model fitted to an early Ebola phylogeny. In both settings, NPE yields accurate and reliable posterior estimates. Its amortized nature further enables efficient model calibration and criticism. We conclude that NPE is a flexible and scalable tool for epidemiological and phylodynamic inference, and provide detailed online tutorials illustrating the workflow.

systems biology↗

An method for systematically surveying data visualizations in infectiousdisease genomic epidemiology

MotivationData visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options.\n\nResultsWe have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations, and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools. Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas.\n\nAvailabilityOur browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease

bioinformatics↗

The Molecular Epidemiology and Mechanisms of Antibiotic Resistance in Gram-positive Bacteria in Africa: A Systematic Review and Meta-Analysis from a One Health Perspective

A systematic review and meta-analysis of antibiotic-resistant Gram-positive bacteria in Africa, showing the molecular epidemiology of resistant species from animal, human and environmental sources, is lacking. Thus, the current burden, type, and sources of Gram-positive bacterial resistance and their dissemination routes from farm to fork is absent. To fill this One Health information gap, we systematically searched PubMed, Web of Science and African Journals Online for English research articles reporting on the resistance mechanisms and clonality of resistant Gram-positive bacteria in Africa within 2007 to 2018. The review and all statistical analysis were undertaken with 130 included articles.\n\nFrom our analyses, the same resistant Gram-positive bacterial clones, resistance genes, and mobile genetic elements (MGEs) are circulating in humans, animals and the environment. The resistance genes, mecA, erm(B), erm(C), tet(M), tet(K), tet(L), vanB, vanA, vanC, and tet(O), were found in isolates from humans, animals and the environment. Commonest clones and mobile genetic elements identified from all three sample sources included Staphylococcus aureus ST5 (n=208 isolates), ST 8 (n=116 isolates), ST 80 (n=123 isolates) and ST 88 (n=105 isolates), and IS16 (n=18 isolates), Tn916 (n=60 isolates) and SCCmec (n=202 isolates). Resistance to penicillin (n=4 224 isolates, 76.2%), erythromycin (n=3 552 isolates, 62.6%), ampicillin (n=1 507 isolates, 54.0%), sulfamethoxazole/trimethoprim (n=2 261 isolates, 46.0%), tetracycline (n=3 054 isolates, 42.1%), vancomycin (n=1 281 isolates, 41.2%), streptomycin (n=1 198 isolates, 37.0%), rifampicin (n=2 645 isolates, 33.1%), ciprofloxacin (n=1 394 isolates, 30.5%), clindamycin (n=1 256 isolates, 29.9%) and gentamicin (n=1 502 isolates, 27.3%) (p-value <0.0001) were commonest.\n\nMean resistance rates of 14.2% to 98.5% were recorded in 20 countries within the study period, which were mediated by clonal, polyclonal and horizontal transmission of resistance genes. A One Health approach to research, surveillance, molecular epidemiology, and antibiotic stewardship to contain ABR should be prioritized.

microbiology↗

Characterization of emetic and diarrheal Bacillus cereus strains from a 2016 foodborne outbreak using whole-genome sequencing: addressing the microbiological, epidemiological, and bioinformatic challenges

The Bacillus cereus group comprises multiple species capable of causing emetic or diarrheal foodborne illness. Despite being responsible for tens of thousands of illnesses each year in the U.S. alone, whole-genome sequencing (WGS) has not been routinely employed to characterize B. cereus group isolates from foodborne outbreaks. Here, we describe the first WGS-based characterization of isolates linked to an outbreak caused by members of the B. cereus group. In conjunction with a 2016 outbreak traced to a supplier of refried beans served by a fast food restaurant chain in upstate New York, a total of 33 B. cereus group strains were obtained from human cases (n =7) and food samples (n = 26). Emetic (n = 30) and diarrheal (n = 3) isolates were most closely related to B. paranthracis (clade III) and B. cereus sensu stricto (clade IV), respectively. WGS indicated that the 30 emetic isolates (24 and 6 from food and humans, respectively) were closely-related and formed a well-supported clade relative to publicly-available emetic clade III genomes with an identical sequence type (ST 26). When compared to publicly-available emetic clade III ST 26 B. cereus group genomes, the 30 emetic clade III isolates from this outbreak differed from each other by a mean of 8.3 to 11.9 core single nucleotide polymorphisms (SNPs), while differing from publicly-available genomes by a mean of 301.7 to 528.0 core SNPs, depending on the SNP calling methodology used. Using a WST-1 cell proliferation assay, the strains isolated from this outbreak had only mild detrimental effects on HeLa cell metabolic activity compared to reference diarrheal strain B. cereus ATCC 14579. Based on both WGS and epidemiological data, we hypothesize that the outbreak was a single source outbreak caused by emetic clade III B. cereus belonging to the B. paranthracis species. In addition to showcasing how WGS can be used to characterize B. cereus group strains linked to a foodborne outbreak, we also discuss potential microbiological and epidemiological challenges presented by B. cereus group outbreaks, and we offer recommendations for analyzing WGS data from the isolates associated with them.

microbiology↗

Characterisation of the HIV-1 Molecular Epidemiology in Nigeria: Origin, Diversity, Demography and Geographic Spread

Nigeria has been reported to have the highest number of AIDS-related deaths in the world. In this study, we aimed to determine the HIV-1 genetic diversity and phylodynamics in Nigeria. We analysed 1442 HIV-1 pol sequences collected 1999-2014 from four geopolitical zones in Nigeria. Phylogenetic analysis showed that the main circulating strains was the circulating recombinant strain (CRF) 02_AG (44% of the analysed sequences), subtype G (8%), and CRF43_02G (16%); and that these were introduced in Nigeria in the 1960s, 1970s and 1980s, respectively. The number of effective infections decreased in Nigeria after the introduction of free antiretroviral treatment in 2006. We also found a significant number of unique recombinant forms (22.7%). The majority of those were recombinants between two or three of the main circulating strains. Seven of those recombinants may represent novel CRFs. Finally, phylogeographic analysis suggested multiple occasions of HIV-1 transmissions between Lagos and Abuja (two of the main cities in Nigeria), that HIV-1 epidemic started in these cities, and then dispersed into rural areas.\n\nIMPORTANCENigeria has the second largest HIV-1 epidemic in the world with the highest number of AIDS-related deaths. The few previous reports have focused on local HIV-1 subtype/CRF distributions in different Nigerian regions, and the molecular epidemiology of HIV-1 in Nigeria as a whole is less well characterized. In this study, we describe the HIV-1 spatiotemporal dynamics of the five dominating transmission clusters representing the main characteristics of the epidemiology. Our results may contribute to inform prevention strategies against further spread of HIV-1 in Nigeria.

microbiology↗

SKA: Split Kmer Analysis Toolkit for Bacterial Genomic Epidemiology

Genome sequencing is revolutionising infectious disease epidemiology, providing a huge step forward in sensitivity and specificity over more traditional molecular typing techniques. However, the complexity of genome data often means that its analysis and interpretation requires high-performance compute infrastructure and dedicated bioinformatics support. Furthermore, current methods have limitations that can differ between analyses and are often opaque to the user, and their reliance on multiple external dependencies makes reproducibility difficult. Here I introduce SKA, a toolkit for analysis of genome sequence data from closely-related, small, haploid genomes. SKA uses split kmers to rapidly identify variation between genome sequences, making it possible to analyse hundreds of genomes on a standard home computer. Tests on publicly available simulated and real-life data show that SKA is both faster and more efficient than the gold standard methods used today while retaining similar levels of accuracy for epidemiological purposes. SKA can take raw read data or genome assemblies as input and calculate pairwise distances, create single linkage clusters and align genomes to a reference genome or using a reference-free approach. SKA requires few decisions to be made by the user, which, along with its computational efficiency, allows genome analysis to become accessible to those with only basic bioinformatics training. The limitations of SKA are also far more transparent than for current approaches, and future improvements to mitigate these limitations are possible. Overall, SKA is a powerful addition to the armoury of the genomic epidemiologist. SKA source code is available from Github (https://github.com/simonrharris/SKA).

genomics↗

Genomic epidemiology supports multiple introductions and cryptic transmission of Zika virus in Colombia

BackgroundColombia was the second most affected country during the American Zika virus (ZIKV) epidemic, with over 109,000 reported cases. Despite the scale of the outbreak, limited genomic sequence data were available from Colombia. We sought to sequence additional samples and use genomic epidemiology to describe ZIKV dynamics in Colombia.\n\nMethodsWe sequenced ZIKV genomes directly from clinical diagnostic specimens and infected Aedes ae-gypti samples selected to cover the temporal and geographic breadth of the Colombian outbreak. We performed phylogeographic analysis of these genomes, along with other publicly-available ZIKV genomes from the Americas, to estimate the frequency and timing of ZIKV introductions to Colombia.\n\nResultsWe attempted PCR amplification on 184 samples; 19 samples amplified sufficiently to perform sequencing. Of these, 8 samples yielded sequences with at least 50% coverage. Our phylogeo-graphic reconstruction indicates two separate introductions of ZIKV to Colombia, one of which was previously unrecognized. We find that ZIKV was first introduced to Colombia in February 2015 (95%CI: Jan 2015 - Apr 2015), corresponding to 5 to 8 months of cryptic ZIKV transmission prior to confirmation in September 2015. Despite the presence of multiple introductions, we find that the majority of Colombian ZIKV diversity descends from a single introduction. We find evidence for movement of ZIKV from Colombia into bordering countries, including Peru, Ecuador, Panama, and Venezuela.\n\nConclusionsSimilarly to genomic epidemiologic studies of ZIKV dynamics in other countries, we find that ZIKV circulated cryptically in Colombia. This more accurate dating of when ZIKV was circulating refines our definition of the population at risk. Additionally, our finding that the majority of ZIKV transmission within Colombia was attributable to transmission between individuals, rather than repeated travel-related importations, indicates that improved detection and control might have succeeded in limiting the scale of the outbreak within Colombia.

genomics↗

Epidemiology of Staphylococcus aureus in Neonates on Admission to a Chinese Neonatal Intensive Care Unit

PurposeLittle is known about the molecular epidemiology of Staphylococcus aureus in Chinese neonatal intensive care units (NICUs). We describe the molecular epidemiology of S. aureus isolated from neonates on admission to Beijing Childrens Hospital. MethodsFrom May 2015-March 2016, nasal swabs were obtained on admission from 536 neonates. Cultures were also obtained from body sites with suspected infections. S. aureus isolates were characterized by staphylococcal chromosomal cassette (SCCmec) type, staphylococcal protein A (spa) type, multilocus sequence type (MLST), sasX gene, antimicrobial susceptibility and cytotoxicity. Logistic regression assessed risk factors for colonization. ResultsOverall, 92 (18%) infants were colonized with S. aureus and 23 (4%) were diagnosed with culture-positive S. aureus infection. Of the colonized infants, 72% harbored MSSA, while 74% of infected infants were culture-positive for MRSA. Risk factors for colonization included female sex, age 7-28 days, birthweight and vaginal delivery. The most common MRSA and MSSA clones were community-associated ST59-SCCmecIVa-t437 (60%) and ST188-t189 (15%), respectively. The sasX gene was not detected. Some MSSA isolates (16%) were penicillin-susceptible and some MRSA isolates (18%) were oxacillin-susceptible. MRSA and MSSA had similar cytotoxicity, but colonizing strains were less cytotoxic than strains associated with infections. ConclusionsS. aureus colonization was common in infants admitted to our NICU and two community-associated clones predominated. Several non-modifiable risk factors for S. aureus colonization were identified. These results suggest that screening infants for S. aureus upon admission and targeting decolonization of high-risk infants and/or those colonized with high-risk clones could be useful to prevent transmission.

microbiology↗

Epidemiology, ecology and human perceptions of snakebites in a savanna community of northern Ghana

Background Worldwide, snakebite envenomations total ~2.7 million reported cases annually with ~100,000 fatalities. Since 2009, snakebite envenomation has been classified as a very important neglected tropical disease by the World Health Organisation. Despite this emerging awareness, limited efforts have been geared towards addressing the serious public health implications of snakebites, particularly in sub-Saharan Africa, where baseline epidemiological data remain incomplete. Due to poverty as well as limited infrastructure and public health facilities, people in rural Africa, including Ghana, often have no other choice than to seek treatment from traditional medical practitioners (TMP). The African snakebite crisis is highlighted here using extensive complementary data from a community-based epidemiological study conducted by snake ecologists in the savanna zone of northern Ghana. Methodology and findings Our cross-sectional study included 1,000 residents and 24 TMPs in the Savelugu-Nanton District in northern Ghana between December 2008 and May 2009, and a 10-year (1999-2008) retrospective snakebite data from the district hospital. Variables tested included demography, human activity patterns, seasonality, snake ecology and clinical reports. Complementary data showed higher snakebite prevalence during the rainy season, and a hump-shaped correlation between rainfall intensity and snakebite incidences. Almost 6% of respondents had experienced a personal snakebite, whereas ~60% of respondents had witnessed a total of 799 snakebite cases. Out of a total of 857 reported snakebite cases, 24 (~2.8%) died. Highest snakebite prevalence was recorded for males in the age group 15-44 years during farming activities, with most bites occurring in the leg/foot region. Highest snakebite rate was within farmlands, most frequently caused by the Carpet viper (Echis ocellatus). Conclusion The relatively high community-based prevalence rate of ~6%, and fatality rate of ~3%, indicate that snakebites represent an important public health risk in northern Ghana. Based on the high number of respondents and long recording period, we believe these data truly reflect the general situation in rural Ghana and West Africa at large. We recommend increased efforts from both local and international health authorities to address the current snakebite health crisis generally compromising livelihoods and productivity of rural farming communities in West Africa.

ecology↗

Re-introduction of dengue virus serotype 2 in the State of Rio de Janeiro after almost a decade of epidemiological silence

The Asian/American genotype of dengue virus serotype 2 (DENV-2) has been introduced in Brazil through the state of Rio de Janeiro around 1990, and since then it has been spreading and evolving, leading to several waves of dengue epidemics throughout the country that cause a major public health problem. Of particular interest has been the epidemic of 2008, whose highest impact was evidenced in the state of Rio de Janeiro, with a higher number of severe cases and mortality rate, compared to previous outbreaks. Interestingly, no circulation of DENV-2 was witnessed in this region during the preceding 9-year period. By early 2010, phylogenetic analysis of the 2008 epidemic strain revealed that the outbreak was caused by a new viral lineage of the Asian/American genotype, which was pointed as responsible for the outbreak severity as well. The same scenario is repeating in 2019 in this state; however, only a few cases have been detected yet. To provide information that helps to the understanding of DENV-2 dynamics in the state of Rio de Janeiro, and thereafter contribute to public health control and prevention actions, we employed phylogenetic studies combined with temporal and dynamics geographical features to determine the origin of the current viral strain. To this effect, we analyzed a region of 1626 nucleotides entailing the Envelope/NS1 viral genes. Our study reveals that the current strain belongs to the same lineage that caused the 2008 outbreak, however, it is phylogenetically distant from any Brazilian strain identified so far. Indeed, it seemed to be originated in Puerto Rico around 2002 and has been introduced into the state in late 2018. Taking into account that no DENV-2 case was reported over the last decade in the state (representing a whole susceptible children generation), and the fact that a new viral strain may be causing current dengue infections, these results will be influential in strengthening dengue surveillance and disease control, mitigating the potential epidemiological consequences of virus spread.\n\nAuthor SummaryBy the time dengue virus serotype 2 (DENV-2) was introduced into Brazil through the state of Rio de Janeiro in 1990, the first dengue hemorrhagic cases started to evidence as well. Years of seasonal outbreaks were followed by almost ten years oy epidemiological silence in the state. However, in 2007 this serotype was re-introduced into the state causing one of the worst dengue epidemics ever described in the country. The same viral genotype was involved, however, a different viral lineage was detected and pointed as responsible for the outbreak severity. This same scenario could repeat nowadays in the state of Rio de Janeiro. Since new DENV-2 cases are being detected in this region, we analyzed the identity and origin of the viral strain obtained from two infected patients. Phylogeny combined with temporal and geographical analyses of viral sequences demonstrated that the strain causing 2019s dengue cases belonged to the same lineage as the one causing the outbreak in 2008, but to a different subgroup, and might have originated in Puerto Rico and entered the state in recent times. These results may represent a crucial starting point for strengthening Brazilian surveillance systems and disease control, helping to reduce the impact of a potential epidemic of great magnitude.

microbiology↗

Serosolver: an open source tool to infer epidemiological and immunological dynamics from serological data

We present a flexible, open source R package designed to obtain additional biological and epidemiological insights from commonly available serological datasets. Analysis of serological responses against pathogens with multiple strains such as influenza pose a specific statistical challenge because observed antibody responses measured in serological assays depend both on unobserved prior infections and the resulting cross-reactive antibody dynamics that these infections generate. We provide a general modelling framework to jointly infer these two typically confounded biological processes using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody dynamics that generates expected antibody titres over time. This makes it possible to use observations of antibodies in serological assays to infer an individuals infection history as well as the parameters of the antibody process model. Our aim is to provide a flexible inference package that can be applied to a range of datasets studying different viruses over different timescales. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, and well as latent epidemiological processes such as attack rates and age-stratified infection risk.

immunology↗

Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing

Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has been responsible for substantial impacts on public, veterinary, and wildlife health. WNV isolates have been sampled at various times and locations across North America since its introduction to New York twenty years ago. We exploit this genetic data repository to demonstrate that factors hypothesised to affect viral dispersal and demography can be formally tested. Specifically, we detail and apply an analytical workflow consisting of state-of-the art methods that we further improve to test the impact of environmental factors on the dispersal locations, velocity, and frequency of viral lineages, as well as on the genetic diversity of the viral population through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. Using a simulation procedure, we find no evidence that viral lineages preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient. Finally, we also separately apply our testing approaches on the three WNV genotypes that circulated in North America in order to understand and compare their dispersal ability. Our study demonstrates that the development and application of statistical approaches, coupled with comprehensive pathogen genomic data, can address epidemiological questions that might otherwise be difficult or impractically expensive to answer.

evolutionary biology↗

Epidemiology of bacterial contamination of inert hospital surfaces and equipment in critical and non-critical care units: a Brazilian multicenter study

The hospital environment is an important reservoir of microorganisms, including multidrug-resistant pathogens, which can cause in-patient contamination and healthcare-related infections. The objective of this study was to analyze the epidemiology of bacterial contamination (contaminated sites, pathogen species and their antimicrobial susceptibility, and tracking of multidrug-resistant microorganisms - MDR) of inert hospital surfaces and medical equipment in two public hospitals in Northern Brazil. This was a cross-sectional study with 243 samples (n = 208, from Hospital A; and n = 35, from Hospital B) collected by friction with swabs moistened in Brain Heart Infusion from inert surfaces and equipment. The samples were cultivated and bacterial species were identified by the classical approach and tested for their susceptibility through agar diffusion assay according to the Clinical and Laboratory Standards Institute (CLSI). Most inert surfaces and equipment analyzed presented bacterial contamination (95.5%). Staphylococcus aureus was the main pathogen of clinical significance detected both in Hospital A (61.8%) and B (68.6%). Hospital A showed higher rates of isolated MDR bacteria than Hospital B, especially in the Adult Intensive Care Unit, which included methicillin-resistant Staphylococcus aureus (MRSA) (52.7%), Enterobacteria resistant to 4th generation cephalosporins (19.4%), and multidrug-resistant Pseudomonas aeruginosa (2.78%). The failures in the prevention and control of infections in the two hospitals analyzed reinforce the need for a revised protocol for cleaning and disinfection of inert surfaces and medical equipment, and for regulation of antibiotic dispensing, mainly in the AICU of Hospital A, which was found to be a reservoir of MDR pathogens. This study is innovative because it is the pioneer in Western Bahia that describes the epidemiology of contamination of hospital surfaces, opportuning futures studies in this field.

microbiology↗