40,605 research outputs found
Persistent Monitoring of Events with Stochastic Arrivals at Multiple Stations
This paper introduces a new mobile sensor scheduling problem, involving a
single robot tasked with monitoring several events of interest that occur at
different locations. Of particular interest is the monitoring of transient
events that can not be easily forecast. Application areas range from natural
phenomena ({\em e.g.}, monitoring abnormal seismic activity around a volcano
using a ground robot) to urban activities ({\em e.g.}, monitoring early
formations of traffic congestion using an aerial robot). Motivated by those and
many other examples, this paper focuses on problems in which the precise
occurrence times of the events are unknown {\em a priori}, but statistics for
their inter-arrival times are available. The robot's task is to monitor the
events to optimize the following two objectives: {\em (i)} maximize the number
of events observed and {\em (ii)} minimize the delay between two consecutive
observations of events occurring at the same location. The paper considers the
case when a robot is tasked with optimizing the event observations in a
balanced manner, following a cyclic patrolling route. First, assuming the
cyclic ordering of stations is known, we prove the existence and uniqueness of
the optimal solution, and show that the optimal solution has desirable
convergence and robustness properties. Our constructive proof also produces an
efficient algorithm for computing the unique optimal solution with time
complexity, in which is the number of stations, with time
complexity for incrementally adding or removing stations. Except for the
algorithm, most of the analysis remains valid when the cyclic order is unknown.
We then provide a polynomial-time approximation scheme that gives a
-optimal solution for this more general, NP-hard problem
Statistical approaches to the surveillance of infectious diseases for veterinary public health
This technical report covers the aspect of using statistical methodology for the monitoring of routinely collected surveillance data in veterinary public health. An account of the Farrington algorithm and Poisson cumulative sum schemes for the detection of aberrations is given with special attention devoted to the occurrence of seasonality and spatial aggregation of the time series. Modelling approaches for retrospective analysis of surveillance counts are described.
To illustrate the applicability of the methodology in veterinary public health, data from the surveillance of rabies among fox in Hesse, Germany, are analysed
Reducing respiratory syncytial virus (RSV) hospitalization in a lower-income country by vaccinating mothers-to-be and their households
Respiratory syncytial virus is the leading cause of lower respiratory tract infection among infants. RSV is a priority for vaccine development. In this study, we investigate the potential effectiveness of a two-vaccine strategy aimed at mothers-to-be, thereby boosting maternally acquired antibodies of infants, and their household cohabitants, further cocooning infants against infection. We use a dynamic RSV transmission model which captures transmission both within households and communities, adapted to the changing demographics and RSV seasonality of a low-income country. Model parameters were inferred from past RSV hospitalisations, and forecasts made over a 10-year horizon. We find that a 50% reduction in RSV hospitalisations is possible if the maternal vaccine effectiveness can achieve 75 days of additional protection for newborns combined with a 75% coverage of their birth household co-inhabitants (∼7.5% population coverage)
Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model
Landscape epidemiology and landscape genetics combine advances in molecular techniques, spatial analyses and epidemiological models to generate a more real-world understanding of infectious disease dynamics and provide powerful new tools for the study of RNA viruses. Using dog rabies as a model we have identified how key questions regarding viral spread and persistence can be addressed using a combination of these techniques. In contrast to wildlife rabies, investigations into the landscape epidemiology of domestic dog rabies requires more detailed assessment of the role of humans in disease spread, including the incorporation of anthropogenic landscape features, human movements and socio-cultural factors into spatial models. In particular, identifying and quantifying the influence of anthropogenic features on pathogen spread and measuring the permeability of dispersal barriers are important considerations for planning control strategies, and may differ according to cultural, social and geographical variation across countries or continents. Challenges for dog rabies research include the development of metapopulation models and transmission networks using genetic information to uncover potential source/sink dynamics and identify the main routes of viral dissemination. Information generated from a landscape genetics approach will facilitate spatially strategic control programmes that accommodate for heterogeneities in the landscape and therefore utilise resources in the most cost-effective way. This can include the efficient placement of vaccine barriers, surveillance points and adaptive management for large-scale control programmes
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
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