J Biomed Inform

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising.P01-HK000086/HK/PHITPO CDC HHS/United StatesR01 LM009132/LM/NLM NIH HHS/United StatesR01 LM011370/LM/NLM NIH HHS/United StatesR01-LM009132/LM/NLM NIH HHS/United StatesR01-LM011370/LM/NLM NIH HHS/United States2016-02-01T00:00:00Z25181466PMC444133

Betacoronavirus genomes: How genomic information has been used to deal with past outbreaks and the COVID-19 pandemic

In the 21st century, three highly pathogenic betacoronaviruses have emerged, with an alarming rate of human morbidity and case fatality. Genomic information has been widely used to understand the pathogenesis, animal origin and mode of transmission of betacoronaviruses in the aftermath of the 2002-03 severe acute respiratory syndrome (SARS) and 2012 Middle East respiratory syndrome (MERS) outbreaks. Furthermore, genome sequencing and bioinformatic analysis have had an unprecedented relevance in the battle against the 2019-20 coronavirus disease 2019 (COVID-19) pandemic, the newest and most devastating outbreak caused by a coronavirus in the history of mankind, allowing the follow up of disease spread and transmission dynamics in near real time. Here, we review how genomic information has been used to tackle outbreaks caused by emerging, highly pathogenic, betacoronavirus strains, emphasizing on SARS-CoV, MERS-CoV and SARS-CoV-2.In the 21st century, three highly pathogenic betacoronaviruses have emerged, with an alarming rate of human morbidity and case fatality. Genomic information has been widely used to understand the pathogenesis, animal origin and mode of transmission of betacoronaviruses in the aftermath of the 2002-03 severe acute respiratory syndrome (SARS) and 2012 Middle East respiratory syndrome (MERS) outbreaks. Furthermore, genome sequencing and bioinformatic analysis have had an unprecedented relevance in the battle against the 2019-20 coronavirus disease 2019 (COVID-19) pandemic, the newest and most devastating outbreak caused by a coronavirus in the history of mankind, allowing the follow up of disease spread and transmission dynamics in near real time. Here, we review how genomic information has been used to tackle outbreaks caused by emerging, highly pathogenic, betacoronavirus strains, emphasizing on SARS-CoV, MERS-CoV and SARS-CoV-2

Repatriation of an old fish host as an opportunity for myxozoan parasite diversity: The example of the allis shad, Alosa alosa (Clupeidae), in the Rhine

Background: Wildlife repatriation represents an opportunity for parasites. Reintroduced hosts are expected to accumulate generalist parasites via spillover from reservoir hosts, whereas colonization with specialist parasites is unlikely. We address the question of how myxozoan parasites, which are characterized by a complex life-cycle alternating between annelids and fish, can invade a reintroduced fish species and determine the impact of a de novo invasion on parasite diversity. We investigated the case of the anadromous allis shad, Alosa alosa (L.), which was reintroduced into the Rhine approximately 70 years after its extinction in this river system. Methods: We studied parasites belonging to the Myxozoa (Cnidaria) in 196 allis shad from (i) established populations in the French rivers Garonne and Dordogne and (ii) repatriated populations in the Rhine, by screening the first adults returning to spawn in 2014. Following microscopical detection of myxozoan infections general myxozoan primers were used for SSU rDNA amplification and sequencing. Phylogenetic analyses were performed and cloned sequences were analyzed from individuals of different water sources to better understand the diversity and population structure of myxozoan isolates in long-term coexisting vs recently established host-parasite systems. Results: We describe Hoferellus alosae n. sp. from the renal tubules of allis shad by use of morphological and molecular methods. A species-specific PCR assay determined that the prevalence of H. alosae n. sp. is 100 % in sexually mature fish in the Garonne/Dordogne river systems and 22 % in the first mature shad returning to spawn in the Rhine. The diversity of SSU rDNA clones of the parasite was up to four times higher in the Rhine and lacked a site-specific signature of SNPs such as in the French rivers. A second myxozoan, Ortholinea sp., was detected exclusively in allis shad from the Rhine. Conclusions: Our data demonstrate that the de novo establishment of myxozoan infections in rivers is slow but of great genetic diversity, which can only be explained by the introduction of spores from genetically diverse sources, predominantly via straying fish or by migratory piscivorous birds. Long-term studies will show if and how the high diversity of a de novo introduction of host-specific myxozoans succeeds into the establishment of a local successful strain in vertebrate and invertebrate hosts

Comparative Assessment of Epidemiological Models for Analyzing and Forecasting Infectious Disease Outbreaks

Mathematical modeling offers a quantitative framework for analyzing mechanisms underlying infectious disease transmission and explaining patterns in epidemiological data. Models are also commonly applied in outbreak investigations for assessing intervention and control strategies and generating epidemic forecasts in real time. However, successful application of mathematical models depends on the ability to reliably estimate key transmission and severity parameters, which are critical for guiding public health interventions. Overall, the three studies presented provide a thorough guide for assessing and utilizing mathematical models for describing infectious disease outbreak trends. In the first study, we describe the process for analyzing identifiability of parameters of interest in mechanistic disease transmission models. In the second study, we expand this idea to simple phenomenological models and explore the idea of overdispersion in the data and how to determine an appropriate error structure within the analyses. In the third study, we use previously validated phenomenological models to generate short-term forecasts of the ongoing COVID-19 pandemic. During infectious disease epidemics, public health authorities rely on modeling results to inform intervention decisions and resource allocation. Therefore, we highlight the importance of interpreting modeling results with caution, particularly regarding theoretical aspects of mathematical models and parameter estimation methods. Further, results from modeling studies should be presented with quantified uncertainty and interpreted in terms of the assumptions and limitations of the model, methods, and data used. The methodology presented in this dissertation provides a thorough guide for conducting model-based inferences and presenting the uncertainty associated with parameter estimation results

Stable Isotope Analysis Reveals Differences in Domoic Acid Accumulation and Feeding Strategies of Key Vector Species in Central California

Given the effects of harmful algal blooms (HABs) on human and wildlife health, understanding how domoic acid (DA) is accumulated and transferred through food webs is critical for recognizing the most affected marine communities and predicting ecosystem effects. This study combines stable isotopes of carbon (δ13C) and nitrogen (δ15N) from bulk muscle tissue with DA measurements from viscera to identify the foraging strategies of important DA vectors and predators in Monterey Bay, CA. Tissue samples were collected from 23 species across three habitats in the summer of 2018 and 2019 (time periods without prominent HABs), with a focus on California sea lions, as the primary predator affected by DA, their prey (anchovies, sardines, squid, krill, juvenile rockfish), and other key sentinel species (e.g., mussels). My results highlight 13C enrichment in krill and elevated DA concentrations ([DA]; ppm) in anchovies collected inside Monterey Bay, indicating inshore-offshore differences in coastal productivity and DA accumulation. The narrow, overlapping isotopic niches between anchovies and sardines and striking differences in [DA], suggests these common prey species exhibit dietary specialization and resource partitioning, potentially based on prey size. In contrast, krill, market squid, and juvenile rockfish accumulated minimal DA during 2018/19 and thus have a lower capacity to serve as DA vectors during years of low HAB activity. Low [DA] in the livers of stranded sea lions along with their large isotopic niche may indicate that individuals have different diets or feed in isotopically distinct locations limiting the ability to use sea lions as sentinels for DA outbreaks in a specific geographic area. Collectively, my results show that DA was produced a few kilometers from the coastline and that anchovies were the most powerful DA vector in coastal-pelagic zones (potentially associated with their feeding specialization and high mobility), while mussels did not contain detectable DA in the years of sampling (despite their status as the key indicator of DA in coastal systems) and only reflect in situ DA, δ13C, and δ15N values. In comparison, anchovy DA loads in this study consistently exceeded FDA regulatory limits for human consumption. The findings demonstrate the efficacy of combining multiple biogeochemical tracers to improve HAB monitoring efforts and identifying routes of DA transfer across habitats and trophic levels

Evolution of Highly Pathogenic H5N1 Avian Influenza Viruses in Vietnam between 2001 and 2007

Highly pathogenic avian influenza (HPAI) H5N1 viruses have caused dramatic economic losses to the poultry industry of Vietnam and continue to pose a serious threat to public health. As of June 2008, Vietnam had reported nearly one third of worldwide laboratory confirmed human H5N1 infections. To better understand the emergence, spread and evolution of H5N1 in Vietnam we studied over 300 H5N1 avian influenza viruses isolated from Vietnam since their first detection in 2001. Our phylogenetic analyses indicated that six genetically distinct H5N1 viruses were introduced into Vietnam during the past seven years. The H5N1 lineage that evolved following the introduction in 2003 of the A/duck/Hong Kong/821/2002-like viruses, with clade 1 hemagglutinin (HA), continued to predominate in southern Vietnam as of May 2007. A virus with a clade 2.3.4 HA newly introduced into northern Vietnam in 2007, reassorted with pre-existing clade 1 viruses, resulting in the emergence of novel genotypes with neuraminidase (NA) and/or internal gene segments from clade 1 viruses. A total of nine distinct genotypes have been present in Vietnam since 2001, including five that were circulating in 2007. At least four of these genotypes appear to have originated in Vietnam and represent novel H5N1 viruses not reported elsewhere. Geographic and temporal analyses of H5N1 infection dynamics in poultry suggest that the majority of viruses containing new genes were first detected in northern Vietnam and subsequently spread to southern Vietnam after reassorting with pre-existing local viruses in northern Vietnam. Although the routes of entry and spread of H5N1 in Vietnam remain speculative, enhanced poultry import controls and virologic surveillance efforts may help curb the entry and spread of new HPAI viral genes

Genetic and genomic variability of Legionella pneumophila: applications to molecular epidemiology and public health

Tesis por compendio[EN] Legionella pneumophila is a strictly environmental and opportunistic pathogen that can cause severe pneumonia after inhalation of aerosols with enough bacterial load. Outbreaks and sporadic cases are usually localized in temperate environments, and the reservoirs are often water-related sources where biofilms are created. The existence of non-cultivable forms of the bacteria increases the risk for public health, as culture-based methods may miss them, thus complicating the environmental investigations of the sources. Genetic classification through the Sequence-Based Typing (SBT) technique allowed an increased discrimination among L. pneumophila strains compared to previous methods. SBT data can also be used for genetic variability and population structure studies, but a more exhaustive analysis can be performed using high-throughput genome sequencing strategies. This thesis describes the use of both SBT and genomic sequencing to evaluate and provide solutions to different public health needs in L. pneumophila epidemiology. We have focused in the Comunidad Valenciana (CV), the second region in Spain with the highest incidence of Legionellosis, with special interest in the city of Alcoy, where recurrent outbreaks have occurred since 1998. Firstly, SBT data were used to gain a deeper insight into the genetic variability and distribution of the most abundant Sequence Types (ST) in the CV area. We have shown that the level of variability in this region is comparable to that from other countries, revealing the existence of both locally and broadly extended profiles. Approximately half of the observed genetic diversity was found to result from geographical and temporal structure. Secondly, L. pneumophila detection from environmental sources remains a challenge for public health. A comparison between water and biofilm samples using a sensitive touchdown PCR (TD-PCR) strategy revealed that the use of biofilms increased by ten-fold the detection rate. This method allowed evaluating the hidden uncultivable L. pneumophila diversity in the locality of Alcoy and the real-time investigation of a Legionellosis outbreak affecting a hotel in Calpe (Southeast of Spain) in 2012. Thirdly, genomic sequencing was applied to a set of 69 strains isolated during 13 outbreaks occurred in Alcoy in the period 1999-2010, mainly the recurrent ST578. Higher intra-outbreak variability than expected was observed, pointing to the potential existence of multiple sources in this endemic area or high environmental diversity. Interestingly, above 98% of the genomic variability in this ST was found as being incorporated through recombination processes rather than through point mutations. Finally, a metagenomic analysis of environmental biofilms from Alcoy revealed a microbial community dominated by Proteobacteria, Cyanobacteria, Actinobacteria and Bacteroidetes. Despite the known endemism of Legionella in this area, the genus was only found in a relative abundance ranging 0.01-0.07%, which explains the low recovery from environmental sources. In summary, the results from this thesis can benefit public health efforts to control this pathogen in the environment, as we provide new insight into its molecular epidemiology, with immediate applications to surveillance and outbreak investigations.[ES] Legionella pneumophila es un patógeno oportunista estrictamente ambiental capaz de causar neumonía debido a la inhalación de aerosoles con suficiente carga bacteriana. Los brotes y casos esporádicos suelen producirse en ambientes templados y los reservorios encontrarse en zonas con agua donde pueden crearse biopelículas microbianas. La existencia de formas no cultivables de la bacteria aumenta el riesgo para la salud pública, ya que los métodos estándar basados en cultivo microbiológico no pueden detectarlas, complicando las investigaciones ambientales. La clasificación genética basada en el método Sequence-Based Typing (SBT) permite un mayor poder de discriminación entre cepas de L. pneumophila en comparación con métodos previos. Los datos derivados del SBT pueden utilizarse para estudios de variabilidad genética y estructura poblacional. Sin embargo, puede llevarse a cabo un análisis más exhaustivo mediante técnicas de secuenciación genómica de alto rendimiento. Esta tesis describe la utilización tanto de SBT como de secuenciación genómica para evaluar e incluso proponer soluciones a diferentes necesidades en salud pública relacionadas con la epidemiología de L. pneumophila. Nos centramos en la Comunidad Valenciana (CV), la segunda región en España con mayor incidencia de Legionelosis, con especial interés en la localidad de Alcoy, donde ocurren brotes de forma recurrente. En primer lugar, utilizamos datos derivados de SBT para conocer mejor la variabilidad y la distribución de los perfiles genéticos (Sequence Types, ST) en el área de la CV. Mostramos que el nivel de variabilidad en sólo esta región es comparable a la de otros países, con perfiles extendidos local y globalmente. Aproximadamente la mitad de la diversidad genética observada se estima que procede de estructuración geográfica y temporal. En segundo lugar, la detección de L. pneumophila a partir de fuentes ambientales sigue suponiendo un reto para la salud pública. En esta tesis realizamos una comparación entre la detección mediante touchdown PCR (TD-PCR) a partir de muestras de agua y biopelículas microbianas y mostramos que estas últimas proporcionan un aumento de 10 veces en la tasa de detección de la bacteria. Este método permitió evaluar la diversidad no cultivable de L. pneumophila en la localidad de Alcoy y la investigación a tiempo real de un brote en un hotel en Calpe (Sudeste de España) en 2012. A continuación, aplicamos la secuenciación genómica a 69 cepas aisladas durante 13 brotes ocurridos en Alcoy en el período 1999-2010, principalmente el recurrente ST578. Se observó mayor variabilidad entre cepas de un mismo brote que la esperada, lo cual apunta a la existencia potencial de múltiples fuentes en este área, o alta diversidad ambiental. Además, se observó que más del 98% de la variabilidad genómica fue introducida por procesos de recombinación y no de mutación puntual. Finalmente, se realizó un análisis metagenómico de biopelículas ambientales recogidas en Alcoy. Se encontró que la comunidad está dominada por Proteobacteria, Cyanobacteria, Actinobacteria y Bacteroidetes. A pesar del conocido endemismo de Legionella en el área, este género sólo se encontró en una abundancia relativa entre 0.01-0.07%, lo cual explica su baja tasa de recuperación a partir de muestras ambientales. En resumen, los resultados de esta tesis pueden ser de utilidad para los programas de control de este patógeno llevados a cabo por las autoridades de salud pública, ya que proporcionan una nueva percepción de su epidemiología molecular, con aplicación inmediata a la vigilancia e investigación de brotes.[CA] Legionella pneumophila és un patogen oportunista estrictament ambiental capaç d'ocasionar pneumònia degut a la inhalació d'aerosols amb la suficient carga bacteriana. Els brots i casos esporàdics solen ocórrer en ambients temperats, i els reservoris solen trobar-se en zones amb aigua on poden crear-se biopel·lícules microbianes. La existència de formes no cultivables del bacteri augmenten el risc per a la salut pública, ja que els mètodes estàndard basats en el cultiu microbiològic no poden detectar-les, complicant les investigacions ambientals. La classificació genètica basada en el mètode Sequence-Based Typing (SBT) permet un major poder de discriminació entre soques de L. pneumophila en comparació amb previs mètodes. Les dades derivades del SBT poden utilitzar-se per a estudis de variabilitat genètica i estructura poblacional, però un anàlisis més exhaustiu pot dur-se a terme a través de tècniques de seqüenciació genòmica d'alt rendiment. Esta tesis descriu la utilització tant del SBT com de la seqüenciació genòmica per a avaluar i proposar solucions a diferents necessitats en salut pública relacionades amb l'epidemiologia de L. pneumophila. Ens centrem en la Comunitat Valenciana (CV), la segona regió d'Espanya amb la major incidència de Legionel·losi, amb especial interès en la localitat d'Alcoi, on els brots ocorren de forma recurrent des de 1998. Primer, hem utilitzat dades derivades del SBT per a conèixer millor la variabilitat i la distribució dels perfils genètics (Sequence Types, ST) en l'àrea de la CV. Mostrem que el nivell de variabilitat en només aquesta regió és comparable a la d'altres països, amb perfils estesos tant de forma local com més amplia. Aproximadament la meitat de la diversitat genètica observada s'estima que procedeix d'estructuració geogràfica i temporal. Segon, la detecció de L. pneumophila a partir de fonts ambientals continua suposant un repte per a la salut pública. En aquesta tesis realitzem una comparació entre la detecció mitjançant touchdown PCR (TD-PCR) a partir de mostres d'aigua i biopel·lícules microbianes i mostrem que aquestes últimes proporcionen un augment de deu vegades en la tassa de detecció. A més, aquest mètode ens va permetre avaluar la diversitat no cultivable de L. pneumophila a la localitat d'Alcoi i la investigació a temps real d'un brot de Legionelosis que va afectar a un hotel en Calp (Sud-est d'Espanya) a l'any 2012. Tercer, vam aplicar la seqüenciació genòmica a 69 soques aïllades durant 13 brots ocorreguts a Alcoi en el període 1999-2010, principalment el recurrent ST578. Es va observar una major variabilitat entre soques d'un mateix brot de l'esperada, apuntant a l'existència potencial de múltiples fonts en aquesta àrea, considerada endèmica, o alta diversitat ambiental. A més, es va observar que més del 98% de la variabilitat genòmica havia sigut introduïda a partir de processos de recombinació i no de mutació puntual. Finalment, es va realitzar una anàlisi metagenòmica de biopel·lícules ambientals recollides a Alcoi. Varem trobar que la comunitat està dominada per Proteobacteria, Cyanobacteria, Actinobacteria i Bacteroidetes. A pesar del conegut endemisme de Legionella en l'àrea, aquest gènere només es va trobar en una abundància relativa entre 0.01-0.07%, el qual explica la seua baixa tassa de recuperació a partir de mostres ambientals. En resum, els resultats d'aquesta tesi poden ser d'utilitat per als programes de control d'aquest patogen duts a terme per les autoritats de salut pública, ja que proporcionen una nova percepció de la seua epidemiologia molecular, amb aplicació immediata a la vigilància i la investigació de brots.Sánchez Busó, L. (2015). Genetic and genomic variability of Legionella pneumophila: applications to molecular epidemiology and public health [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/52854TESISPremios Extraordinarios de tesis doctoralesCompendi

Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

Tail biting is a damaging behaviour that impacts the welfare and health of pigs. Early detection of precursor signs of tail biting provides the opportunity to take preventive measures, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real-time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (Generalized Linear Model with Stepwise Feature Selection, random forest, Support Vector Machines with Radial Basis Function Kernel, Bayesian Generalized Linear Model, Neural network, K-nearest neighbour, and Partial Least Squares Discriminant Analysis) were evaluated from daily feeding data collected from 65 pens originating from two herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data in two different ways, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. In the first data splitting, the model is regularly updated with previous data from the pen, whereas in the second data splitting, the model tries to predict for a pen that it has never seen before. The K-nearest neighbour algorithm was able to predict 78% of the upcoming events with an accuracy of 96%, when predicting events in pens for which it had previous data. Our results indicate that machine learning models can be considered for implementation into automatic feeder systems for real-time prediction of tail biting events