211 research outputs found

    Genetic Assignment Methods for Gaining Insight into the Management of Infectious Disease by Understanding Pathogen, Vector, and Host Movement

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    For many pathogens with environmental stages, or those carried by vectors or intermediate hosts, disease transmission is strongly influenced by pathogen, host, and vector movements across complex landscapes, and thus quantitative measures of movement rate and direction can reveal new opportunities for disease management and intervention. Genetic assignment methods are a set of powerful statistical approaches useful for establishing population membership of individuals. Recent theoretical improvements allow these techniques to be used to cost-effectively estimate the magnitude and direction of key movements in infectious disease systems, revealing important ecological and environmental features that facilitate or limit transmission. Here, we review the theory, statistical framework, and molecular markers that underlie assignment methods, and we critically examine recent applications of assignment tests in infectious disease epidemiology. Research directions that capitalize on use of the techniques are discussed, focusing on key parameters needing study for improved understanding of patterns of disease

    Impact of the Infection Period Distribution on the Epidemic Spread in a Metapopulation Model

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    Epidemic models usually rely on the assumption of exponentially distributed sojourn times in infectious states. This is sometimes an acceptable approximation, but it is generally not realistic and it may influence the epidemic dynamics as it has already been shown in one population. Here, we explore the consequences of choosing constant or gamma-distributed infectious periods in a metapopulation context. For two coupled populations, we show that the probability of generating no secondary infections is the largest for most parameter values if the infectious period follows an exponential distribution, and we identify special cases where, inversely, the infection is more prone to extinction in early phases for constant infection durations. The impact of the infection duration distribution on the epidemic dynamics of many connected populations is studied by simulation and sensitivity analysis, taking into account the potential interactions with other factors. The analysis based on the average nonextinct epidemic trajectories shows that their sensitivity to the assumption on the infectious period distribution mostly depends on , the mean infection duration and the network structure. This study shows that the effect of assuming exponential distribution for infection periods instead of more realistic distributions varies with respect to the output of interest and to other factors. Ultimately it highlights the risk of misleading recommendations based on modelling results when models including exponential infection durations are used for practical purposes

    Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life

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    Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an ‘epidemiological landscape’ connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a ‘movement-pathogen pace of life’ heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur

    Effect of landscape fragmentation on bat population dynamics and disease persistence in Uruguay

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    La transmisión de patógenos a nuevas especies hospedadoras, un proceso conocido como spillover, requiere que una serie de condiciones coincidan en tiempo y espacio. Una serie de barreras imperfectas impiden el salto de patógenos de una especie a otra. Éstas pueden incluir la distribución y abundancia del hospedador primario, la supervivencia del patógeno en el ambiente y la susceptibilidad del hospedador receptor al patógeno. Solo cuando las condiciones adecuadas se coinciden puede ocurrir el salto de especie. Estos spillovers son eventos relativamente raros y por lo tanto la comprensión de la dinámica de las barreras se ve limitada por la capacidad de detectar y analizar tales eventos. Los sistemas en los que, a pesar de la aparente presencia de todas las condiciones necesarias, no se produce un salto de especie brindan la oportunidad de comprender las barreras que impiden la transmisión entre especies. La rabia transmitida por murciélagos vampiros en Uruguay brinda esa oportunidad. A pesar de que presenta una densidad de ganado alta y estable, abundancia de vampiros y circulación del virus en los países limítrofes, el país no experimentó brotes de rabia en el ganado hasta 2007. En este trabajo combinamos una revisión histórica, muestreos de campo y modelos estadísticos y matemáticos para comprender los factores que determinaron la ausencia de brotes hasta ese momento, y su aparición en 2007. Nuestros resultados sugieren que los brotes de rabia en el país están asociados espacial y temporalmente con la fragmentación de las áreas de pastoreo. Demostramos que el aumento propuesto de la conectividad entre las colonias, en respuesta a la fragmentación, es suficiente para explicar la persistencia más prolongada del virus en las colonias de murciélagos, lo que permitiría más oportunidades de transmisión del virus al ganado. Nuestro trabajo también prueba que la estacionalidad reproductiva y la tasa de recambio de la población tienen efectos marginales en comparación con la conectividad. Como la conectividad entre colonias, producida por compartir áreas de alimentación podría no ser detectable mediante análisis genéticos de los murciélagos, proponemos el uso de un virus con alta prevalencia en vampiros en Uruguay como marcador para estimar la conectividad entre colonias. Combinados, los resultados presentados en este trabajo proporcionan herramientas que se pueden aplicar para intervenir y diseñar medidas de mitigación y para prevenir la transmisión del virus desde el vampiro a nuevas especies.Agencia Nacional de Investigación e InnovaciónMontana State UniversityAmerican Society of MammalogistsBat Conservation Internationa

    Natural, persistent oscillations in a spatial multi-strain disease system with application to dengue.

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    This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Many infectious diseases are not maintained in a state of equilibrium but exhibit significant fluctuations in prevalence over time. For pathogens that consist of multiple antigenic types or strains, such as influenza, malaria or dengue, these fluctuations often take on the form of regular or irregular epidemic outbreaks in addition to oscillatory prevalence levels of the constituent strains. To explain the observed temporal dynamics and structuring in pathogen populations, epidemiological multi-strain models have commonly evoked strong immune interactions between strains as the predominant driver. Here, with specific reference to dengue, we show how spatially explicit, multi-strain systems can exhibit all of the described epidemiological dynamics even in the absence of immune competition. Instead, amplification of natural stochastic differences in disease transmission, can give rise to persistent oscillations comprising semi-regular epidemic outbreaks and sequential dominance of dengue's four serotypes. Not only can this mechanism explain observed differences in serotype and disease distributions between neighbouring geographical areas, it also has important implications for inferring the nature and epidemiological consequences of immune mediated competition in multi-strain pathogen systems.Fundacao para a Ciencia e TecnologiaSiemens PortugalRoyal Societ

    Correlations between stochastic endemic infection in multiple interacting subpopulations.

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    Heterogeneity plays an important role in the emergence, persistence and control of infectious diseases. Metapopulation models are often used to describe spatial heterogeneity, and the transition from random- to heterogeneous-mixing is made by incorporating the interaction, or coupling, within and between subpopulations. However, such couplings are difficult to measure explicitly; instead, their action through the correlations between subpopulations is often all that can be observed. We use moment-closure methods to investigate how the coupling and resulting correlation are related, considering systems of multiple identical interacting populations on highly symmetric complex networks: the complete network, the k-regular tree network, and the star network. We show that the correlation between the prevalence of infection takes a relatively simple form and can be written in terms of the coupling, network parameters and epidemiological parameters only. These results provide insight into the effect of metapopulation network structure on endemic disease dynamics, and suggest that detailed case-reporting data alone may be sufficient to infer the strength of between population interaction and hence lead to more accurate mathematical descriptions of infectious disease behaviour

    Population and Metapopulation Ecology of Childhood Diseases

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    Researchers have long used mathematical models and empirical data to explore the population ecology of childhood diseases such as measles and whooping cough. These diseases have proven ideal model systems for studying population dynamics over space and time. Here we present a novel dataset of weekly measles and whooping cough case reports in pre-vaccine era U.S. cities and states, along with a previously- studied dataset of measles in England & Wales. We first estimate per-population disease reporting probabilities. We find that disease reporting is highly variable over space and between diseases, and correlated with socioeconomic covariates including ethnic composition and school attendance. Using these reporting estimates, we infer the long-term, marginal distribution of disease incidence for each population. This describes a probabilistic measure of disease persistence that compares favorably with a classic threshold persistence measure, critical community size (CCS). The U.S. and England & Wales exhibit similar patterns of measles incidence distributions: larger populations show higher mean viincidence and lower variance. The per-time probability of local extinction (conditioned on population size) is higher in the U.S. than in England & Wales, likely due to larger distances between U.S. cities. Finally, we use observed persistence and inferred incidence distributions to estimate the per-time probability of true persistence. Estimated persistence of whooping cough is much higher than persistence of measles (conditioned on population size). We find that cryptic persistence (the difference between observed and estimated persistence) of whooping cough is most common in small populations, while for measles cryptic persistence is most common in medium-sized populations that hover at the edge of extinction. Our results show that variation in disease reporting can significantly affect meta- population estimates of disease persistence, such as CCS. The distributional estimates of incidence presented here explicitly account for incomplete reporting, providing summaries of long-term ecological patterns that are comparable between metapopulations. These measures can provide disease control programs with valuable information on where disease incidence is expected to be higher or lower than expected based on population size alone
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