Epidemic Alphaviruses: Ecology, Emergence and Outbreaks.
Ontology highlight
ABSTRACT:
SUBMITTER: Azar SR
PROVIDER: S-EPMC7464724 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Epidemic Alphaviruses: Ecology, Emergence and Outbreaks.
Azar Sasha R SR Campos Rafael K RK Bergren Nicholas A NA Camargos Vidyleison N VN Rossi Shannan L SL
Microorganisms 20200801 8
Over the past century, the emergence/reemergence of arthropod-borne zoonotic agents has been a growing public health concern. In particular, agents from the genus <i>Alphavirus</i> pose a significant risk to both animal and human health. Human alphaviral disease presents with either arthritogenic or encephalitic manifestations and is associated with significant morbidity and/or mortality. Unfortunately, there are presently no vaccines or antiviral measures approved for human use. The present rev ...[more]
Similar Datasets
Project description:Assessing and managing the impact of large-scale epidemics considering only the individual risk and severity of the disease is exceedingly difficult and could be extremely expensive. Economic consequences, infrastructure and service disruption, as well as the recovery speed, are just a few of the many dimensions along which to quantify the effect of an epidemic on society's fabric. Here, we extend the concept of resilience to characterize epidemics in structured populations, by defining the system-wide critical functionality that combines an individual's risk of getting the disease (disease attack rate) and the disruption to the system's functionality (human mobility deterioration). By studying both conceptual and data-driven models, we show that the integrated consideration of individual risks and societal disruptions under resilience assessment framework provides an insightful picture of how an epidemic might impact society. In particular, containment interventions intended for a straightforward reduction of the risk may have net negative impact on the system by slowing down the recovery of basic societal functions. The presented study operationalizes the resilience framework, providing a more nuanced and comprehensive approach for optimizing containment schemes and mitigation policies in the case of epidemic outbreaks.
| S-EPMC5789872 | biostudies-other
Project description:To investigate the molecular basis of the emergence of Aeromonas hydrophila responsible for an epidemic outbreak of motile aeromonad septicemia of catfish in the Southeastern United States, we sequenced 11 A. hydrophila isolates that includes five reference and six recent epidemic isolates. Comparative genomics revealed that recent epidemic A. hydrophila isolates are highly clonal, whereas reference isolates are greatly diverse. We identified 55 epidemic-associated genetic regions with 313 predicted genes that are present in epidemic isolates but absent from reference isolates and 35% of these regions are located within genomic islands, suggesting their acquisition through lateral gene transfer. The epidemic-associated regions encode predicted prophage elements, pathogenicity islands, metabolic islands, fitness islands and genes of unknown functions, and 34 of the genes encoded in these regions were predicted as virulence factors. We found two pilus biogenesis gene clusters encoded within predicted pathogenicity islands. A functional metabolic island that encodes a complete pathway for myo-inositol catabolism was evident by the ability of epidemic A. hydrophila isolates to use myo-inositol as a sole carbon source. Testing of A. hydrophila field isolates found a consistent correlation between myo-inositol utilization as a sole carbon source and the presence of an epidemic-specific genetic marker. All epidemic isolates and one reference isolate shared a novel O-antigen cluster. Altogether we identified four different O-antigen biosynthesis gene clusters within the 11 sequenced A. hydrophila genomes. Our study reveals new insights into the evolutionary changes that have resulted in the emergence of recent epidemic A. hydrophila strains.
| S-EPMC3835674 | biostudies-literature
Project description:When a disease breaks out in a human population, changes in behavior in response to the outbreak can alter the progression of the infectious agent. In particular, people aware of a disease in their proximity can take measures to reduce their susceptibility. Even if no centralized information is provided about the presence of a disease, such awareness can arise through first-hand observation and word of mouth. To understand the effects this can have on the spread of a disease, we formulate and analyze a mathematical model for the spread of awareness in a host population, and then link this to an epidemiological model by having more informed hosts reduce their susceptibility. We find that, in a well-mixed population, this can result in a lower size of the outbreak, but does not affect the epidemic threshold. If, however, the behavioral response is treated as a local effect arising in the proximity of an outbreak, it can completely stop a disease from spreading, although only if the infection rate is below a threshold. We show that the impact of locally spreading awareness is amplified if the social network of potential infection events and the network over which individuals communicate overlap, especially so if the networks have a high level of clustering. These findings suggest that care needs to be taken both in the interpretation of disease parameters, as well as in the prediction of the fate of future outbreaks.
| S-EPMC2672559 | biostudies-other
Project description:Noroviruses (NoVs) are the leading cause of gastroenteritis outbreaks in humans worldwide. Since late 2012, a new GII.4 variant Sydney 2012 has caused a significant increase in NoV epidemics in several countries. From November of 2012 to January of 2013, three gastroenteritis outbreaks occurred in two social welfare homes (Outbreaks A and B) and a factory (Outbreak C) in Shenzhen city of China. Feces and swabs were collected for laboratory tests for causative agents. While no bacterial pathogen was identified, all three outbreaks were caused by NoVs with detection rates of 26.2% (16/61) at Outbreak A, 35.2% (38/108) at Outbreak B), and 59.3% (16/27) at Outbreaks C. For Outbreak B, 25 of the 29 symptomatic individuals (86.2%) and 13 of the 79 asymptomatic individuals (16.5%) were found NoV-positive. For Outbreak C, an asymptomatic food handler was NoV-positive. All thirteen NoV sequences from the three outbreaks were classified into genogroup II and genotype 4 (GII.4), which we identified to be the GII.4 Sydney 2012 variant. The genome of two isolates from Outbreaks A and B were recombinant with the opening reading frame (ORF) 1 of GII.4 Osaka 2007 and ORF2 and 3 of the GII.4 New Orleans. Our study indicated that the GII.4 Sydney 2012 variant emerged and caused the outbreaks in China.
| S-EPMC5102426 | biostudies-literature
Project description:Addressing current global challenges such as biodiversity loss, global change, and increasing demands for ecosystem services requires improved ecological prediction. Recent increases in data availability, process understanding, and computing power are fostering quantitative approaches in ecology. However, flexible methodological frameworks are needed to utilize these developments towards improved ecological prediction. Deep learning is a rapidly evolving branch of machine learning, yet has received only little attention in ecology to date. It refers to the training of deep neural networks (DNNs), i.e. artificial neural networks consisting of many layers and a large number of neurons. We here provide a reproducible example (including code and data) of designing, training, and applying DNNs for ecological prediction. Using bark beetle outbreaks in conifer-dominated forests as an example, we show that DNNs are well able to predict both short-term infestation risk at the local scale and long-term outbreak dynamics at the landscape level. We furthermore highlight that DNNs have better overall performance than more conventional approaches to predicting bark beetle outbreak dynamics. We conclude that DNNs have high potential to form the backbone of a comprehensive disturbance forecasting system. More broadly, we argue for an increased utilization of the predictive power of DNNs for a wide range of ecological problems.
| S-EPMC6827389 | biostudies-literature
Project description:Porcine epidemic diarrhea virus (PEDV) belongs to the Coronaviridae family and causes malabsorptive watery diarrhea, vomiting, dehydration and imbalanced blood electrolytes in pigs. Since the 1970s, PED outbreaks have become a source of problems in pig producing countries all over the world, causing large economic losses for pig producers. Although the infection in adults is not fatal, in naïve suckling piglets mortality is close to 100%. In this study, we investigated genome-wide differences between dead and recovered suckling piglets from commercial farms after PED outbreaks. Samples from 262 animals (156 dead and 106 recovered) belonging to several commercial lines were collected from five different farms in three different countries (USA, Canada and Germany) and genotyped with the porcine 80K SNP chip. Mean Fst value was calculated in 1-Mb non-overlapping windows between dead and recovered individuals, and the results were normalized to find differences within the comparison. Seven windows with high divergence between dead and recovered were detected-five on chromosome 2, one on chromosome 4 and one on chromosome 15-in total encompassing 152 genes. Several of these genes are either under- or overexpressed in many virus infections, including Coronaviridae (such as SARS-CoV). A total of 32 genes are included in one or more Gene Ontology terms that can be related to PED development, such as Golgi apparatus, as well as mechanisms generally linked to resilience or diarrhea development (cell proliferation, ion transport, ATPase activity). Taken together this information provides a first genomic picture of PEDV resilience in suckling piglets.
| S-EPMC7159462 | biostudies-literature
Project description:This paper presents a new dataset of infectious disease outbreaks collected from the Disease Outbreak News and the Coronavirus Dashboard produced by the World Health Organization. The dataset contains information on 70 infectious diseases and 2227 public health events that occurred over the period from January 1996 to March 2022 in 233 countries and territories around the world. We illustrate the potential use of this dataset to the research community by analysing the spatial distribution of disease outbreaks. We find evidence of spatial clusters of high incidences ("hot spots") in Africa, America, and Asia. This spatial analysis enables policymakers to identify the regions with the greatest likelihood of suffering from disease outbreaks and, taking into account their degree of preparedness and vulnerability, to develop policies that may help contain the spreading of future outbreaks. Further applications could focus on combining our data with other information sources to study, for instance, the link between environmental, globalization, and/or socioeconomic factors with disease outbreaks.
| S-EPMC9648436 | biostudies-literature
Project description:Pseudomonas aeruginosa (P. aeruginosa) is one of the most common nosocomial pathogens worldwide. Although the emergence of multidrug-resistant (MDR) P. aeruginosa is a critical problem in medical practice, the key features involved in the emergence and spread of MDR P. aeruginosa remain unknown. This study utilized whole genome sequence (WGS) analyses to define the population structure of 185 P. aeruginosa clinical isolates from several countries. Of these 185 isolates, 136 were categorized into sequence type (ST) 235, one of the most common types worldwide. Phylogenetic analysis showed that these isolates fell within seven subclades. Each subclade harbors characteristic drug resistance genes and a characteristic genetic background confined to a geographic location, suggesting that clonal expansion following antibiotic exposure is the driving force in generating the population structure of MDR P. aeruginosa. WGS analyses also showed that the substitution rate was markedly higher in ST235 MDR P. aeruginosa than in other strains. Notably, almost all ST235 isolates harbor the specific type IV secretion system and very few or none harbor the CRISPR/CAS system. These findings may help explain the mechanism underlying the emergence and spread of ST235 P. aeruginosa as the predominant MDR lineage.
| S-EPMC5726472 | biostudies-literature
Project description:To understand the contact patterns of a population--who is in contact with whom, and when the contacts happen--is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can meet any with equal probability. A more modern approach, network epidemiology, assumes people are connected into a static network over which the disease spreads. Newer yet, temporal network epidemiology, includes the time in the contact representations. In this paper, we investigate the effect of these successive inclusions of more information. Using empirical proximity data, we study both outbreak sizes from unknown sources, and from known states of ongoing outbreaks. In the first case, there are large differences going from a fully mixed simulation to a network, and from a network to a temporal network. In the second case, differences are smaller. We interpret these observations in terms of the temporal network structure of the data sets. For example, a fast overturn of nodes and links seem to make the temporal information more important.
| S-EPMC4585889 | biostudies-literature
Project description:Epidemic C. difficile (027/BI/NAP1) has rapidly emerged in the past decade as the leading cause of antibiotic-associated diarrhea worldwide. However, the key events in evolutionary history leading to its emergence and the subsequent patterns of global spread remain unknown. Here, we define the global population structure of C. difficile 027/BI/NAP1 using whole-genome sequencing and phylogenetic analysis. We show that two distinct epidemic lineages, FQR1 and FQR2, not one as previously thought, emerged in North America within a relatively short period after acquiring the same fluoroquinolone resistance-conferring mutation and a highly related conjugative transposon. The two epidemic lineages showed distinct patterns of global spread, and the FQR2 lineage spread more widely, leading to healthcare-associated outbreaks in the UK, continental Europe and Australia. Our analysis identifies key genetic changes linked to the rapid transcontinental dissemination of epidemic C. difficile 027/BI/NAP1 and highlights the routes by which it spreads through the global healthcare system.
| S-EPMC3605770 | biostudies-literature