Biology subjects

Shweta Bansal

Publications and source records attributed to Shweta Bansal.

8 recordsLinked to original sources

bioRxiv2016

Contact, travel, and transmission: The impact of winter holidays on influenza dynamics in the United States

BackgroundThe seasonality of influenza is thought to vary according to environmental factors and human behavior. During winter holidays, potential disease-causing contact and travel deviate from typical patterns, and we aim to understand these changes on age-specific and spatial flu transmission.\n\nMethodsWe characterized the changes to transmission and epidemic trajectories among children and adults in a spatial context before, during, and after the winter holidays among aggregated physician medical claims in the United States from 2001 to 2009 and among synthetic data simulated from a deterministic, age-specific spatial metapopulation model.\n\nResultsWinter holidays reduced flu transmission and delayed the trajectory of flu season epidemics. The holiday period itself observed a shift in relative risk of disease from children towards adults. Model results indicated that holidays delay epidemic peaks and synchronize incidence across locations, and contact reductions from school closures rather than age-specific mixing and travel produce these observed holiday dynamics.\n\nConclusionsWinter holidays delay seasonal influenza epidemic peaks due to changes in contact patterns. These findings may improve the future design of influenza intervention strategies, such as the proper timing and duration of school closures, and the spatial and demographic allocation of vaccines.

Epidemiology↗

bioRxiv2015

Inferring social structure and its drivers from refuge use in the desert tortoise, a relatively solitary species

For several species, refuges (such as burrows, dens, roosts, nests) are an essential resource for protection from predators and extreme environmental conditions. Refuges also serve as focal sites for social interactions including mating, courtship and aggression. Knowledge of refuge use patterns can therefore provide information about social structure, mating and foraging success, as well as the robustness and health of wildlife populations, especially for species considered to be relatively solitary. In this study, we construct networks of burrow use to infer social associations in a threatened wildlife species typically considered solitary - the desert tortoise. We show that tortoise social networks are significantly different than null networks of random associations, and have moderate spatial constraints. We next use statistical models to identify major mechanisms behind individual-level variation in tortoise burrow use, popularity of burrows in desert tortoise habitat and test for stressor-driven changes in refuge use patterns. We show that seasonal variation has a strong impact on tortoise burrow switching behavior. On the other hand, burrow age and topographical condition influence the number of tortoises visiting a burrow in desert tortoise habitat. Of three major population stressors affecting this species (translocation, drought, disease), translocation alters tortoise burrow switching behavior, with translocated animals visiting fewer unique burrows than residents. In a species that is not social, our study highlights the importance of leveraging refuge use behavior to study the presence of and mechanisms behind non-random social structure and individual-level variation. Our analysis of the impact of stressors on refuge-based social structure further emphasizes the potential of this method to detect environmental or anthropogenic disturbances.\n\nSignificance statement: Adaptive and social behavior that affects fitness is now being increasingly incorporated in the conservation and management of wildlife species. However, direct observations of social interactions in species considered to be solitary are difficult, and therefore integration of behavior in conservation and management decisions in such species has been infrequent. For such species, we propose quantifying refuge use behavior as it can provide insights towards their (hidden) social structure, establish relevant contact patterns of infectious disease spread, and provide early warning signals of population stressors. Our study highlights this approach in a long-lived and threatened species, the desert tortoise. We provide evidence towards the presence of and identify mechanisms behind the social structure in desert tortoises formed by their burrow use preferences. We also show how individuals burrow use behavior responds to the presence of population stressors.

Ecology↗

bioRxiv2015

Livestock market data for modeling disease spread among US cattle

Transportation of livestock carries the risk of spreading foreign animal diseases, leading to costly public and private sector expenditures on disease containment and eradication. Livestock movement tracing systems in Europe, Australia and Japan have allowed epidemiologists to model the risks engendered by transportation of live animals and prepare responses designed to protect the livestock industry. Within the US, data on livestock movement is not sufficient for direct parameterization of models for disease spread, but network models that assimilate limited data provide a path forward in model development to inform preparedness for disease outbreaks in the US. Here, we develop a novel data stream, the information publicly reported by US livestock markets on the origin of cattle consigned at live auctions, and demonstrate the potential for estimating a national-scale network model of cattle movement. By aggregating auction reports generated weekly at markets in several states, including some archived reports spanning several years, we obtain a market-oriented sample of edges from the dynamic cattle transportation network in the US. We first propose a sampling framework that allows inference about shipments originating from operations not explicitly sampled and consigned at non-reporting livestock markets in the US, and we report key predictors that are influential in extrapolating beyond our opportunistic sample. As a demonstration of the utility gained from the data and fitted parameters, we model the critical role of market biosecurity procedures in the context of a spatially homogeneous but temporally dynamic representation of cattle movements following an introduction of a foreign animal disease. We conclude that auction market data fills critical gaps in our ability to model intrastate cattle movement for infectious disease dynamics, particularly with an ability to addresses the capacity of markets to amplify or control a livestock disease outbreak.\n\nAuthor SummaryWe have automated the collection of previously unavailable cattle movement data, allowing us to aggregate details on the origins of cattle sold at live-auction markets in the US. Using our novel dataset, we demonstrate potential to infer a complete dynamic transportation network that would drive disease transmission in models of potential US livestock epidemics.

Bioinformatics↗

bioRxiv2015

Using heterogeneity in the population structure of U.S. swine farms to compare transmission models for porcine epidemic diarrhoea

In 2013, U.S. swine producers were confronted with the disruptive emergence of porcine epidemic diarrhoea (PED). Movement of animals among farms is hypothesised to have played a role in the spread of PED among farms. Via this or other mechanisms, the rate of spread may also depend on the geographic density of farms and climate. To evaluate such effects on a large scale, we analyse state-level counts of outbreaks with variables describing the distribution of farm sizes and types, aggregate flows of animals among farms, and an index of climate. Our first main finding is that it is possible for a correlation analysis to be sensitive to transmission model parameters. This finding is based on a global sensitivity analysis of correlations on simulated data that included a biased and noisy observation model based on the available PED data. Our second main finding is that flows are significantly associated with the reports of PED outbreaks. This finding is based on correlations of pairwise relationships and regression modeling of total and weekly outbreak counts. These findings illustrate how variation in population structure may be employed along with observational data to improve understanding of disease spread.

Ecology↗

bioRxiv2015

Detecting Signals of Seasonal Influenza Severity through Age Dynamics

Background: Measures of population-level influenza severity are important for public health planning, but estimates are often based on case-fatality and case-hospitalization risks, which require multiple data sources, are prone to surveillance biases, and are typically unavailable in the early stages of an outbreak. In this study, we develop a severity index based on influenza age dynamics estimated from routine surveillance data that can be used in retrospective and early warning contexts.\n\nMethods and Findings: Our method relies on the observation that age-specific attack rates vary between seasons, so that key features of the age distribution of cases may be used as a marker of severity early in an epidemic. We illustrate our method using weekly outpatient medical claims of influenza-like illness (ILI) in the United States from the 2001 to 2009 and develop a novel population-level influenza severity index based on the relative risk of ILI among working-age adults to that among school-aged children. We validate our ILI index against a benchmark that comprises traditional influenza severity indicators such as viral activity, hospitalizations and deaths using publicly available surveillance data. We find that severe influenza seasons have higher relative rates of ILI among adults than mild seasons. In reference to the benchmark, the ILI index is a robust indicator of severity during the period of peak epidemic growth (87.5% accuracy in retrospective classification), and may have predictive power during the period between Thanksgiving and the winter holidays (57.1% accuracy in early warning). We further apply our approach at the state-level to characterize regional severity patterns across seasons. We hypothesize that our index is a proxy for severity because working-age adults have both pre-existing immunity to influenza and a high number of contacts, infecting them preferentially in severe seasons associated with antigenic changes in circulating influenza viruses. Our analysis is limited by its application to seasonal influenza epidemics and a relatively short study period.\n\nConclusions: Our severity index and research on the link between age dynamics and seasonal influenza severity will enable decision makers to better target public health strategies in severe seasons and improve our knowledge of influenza epidemiology and population impact. These findings demonstrate that routine surveillance data can be translated into operational information for policymakers. Our study also highlights the need for further research on the putative age-related mechanisms of severity in seasonal and pandemic influenza seasons.

Ecology↗

bioRxiv2014

Social, spatial and temporal segregation in an ant society

Introduction Introduction Results Discussion Methods References Sociality can be risky. A chief cost of social living is increased transmission of infectious diseases, due to higher population densities combined with greater contact between susceptible and infected individuals (1,2,3,4). This greater encounter rate has led to a growing interest in the role of social contact structure in infectious disease transmission (5,6,7,8,9,10,11) To capture the dynamics of disease spread within dense groups, epidemiological models are shifting from the principle of mass action, in which infected and susceptible individuals are assumed to mix randoml ...

Ecology↗

bioRxiv2013

Assessing the Use of Antiviral Treatment to Control Influenza

Vaccines are the cornerstone of influenza control policy, but can suffer from several drawbacks. Seasonal influenza vaccines are prone to production problems and low efficacies, while pandemic vaccines are unlikely to be available in time to slow a rapidly spreading global outbreak. Antiviral therapy was found to be beneficial during the influenza A(H1N1)pdm09 pandemic even with limited use; however, antiviral use has decreased further since then. We seek to determine the role antiviral therapy can play in pandemic and seasonal influenza control on the population level, and to find optimized strategies for more efficient use of treatment. Using an age-structured contact network model for an urban population, we find that while a conservative antiviral therapy strategy cannot replace a robust influenza vaccine, it can play a role in reducing attack rates and eliminating outbreaks.

Ecology↗

bioRxiv2013

Exploring community structure in biological networks with random graphs

BackgroundCommunity structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a systems functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge.\n\nResultsHere, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks.\n\nConclusionOur model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

Bioinformatics↗