244 research outputs found
Machine learning assists the classification of reports by citizens on disease-carrying mosquitoes
Mosquito Alert (www.mosquitoalert.com/en) is an expert-validated citizen science platform for tracking and controlling disease-carrying mosquitoes. Citizens download a free app and use their phones to send reports of presumed sightings of two world-wide disease vector
mosquito species (the Asian Tiger and the Yellow Fever mosquito). These reports are then supervised by a team of entomologists and, once validated, added to a database. As the platform prepares to scale to much larger geographical areas and user bases, the expert validation by entomologists becomes the main bottleneck. In this paper we describe the use of machine learning on the citizen reports to automatically validate a fraction of them, therefore allowing the entomologists either to deal with larger report streams or to concentrate on those that are more strategic, such as reports from new areas (so that early warning protocols are activated) or from areas with high epidemiological risks (so that control actions to reduce mosquito populations are activated). The current prototype flags a third of the reports as “almost certainly positive” with high confidence. It is currently being integrated into the main workflow of the Mosquito Alert platform.Postprint (published version
First-passage times in multi-scale random walks: the impact of movement scales on search efficiency
An efficient searcher needs to balance properly the tradeoff between the
exploration of new spatial areas and the exploitation of nearby resources, an
idea which is at the core of scale-free L\'evy search strategies. Here we study
multi-scale random walks as an approximation to the scale- free case and derive
the exact expressions for their mean-first passage times in a one-dimensional
finite domain. This allows us to provide a complete analytical description of
the dynamics driving the asymmetric regime, in which both nearby and faraway
targets are available to the searcher. For this regime, we prove that the
combination of only two movement scales can be enough to outperform both
balistic and L\'evy strategies. This two-scale strategy involves an optimal
discrimination between the nearby and faraway targets, which is only possible
by adjusting the range of values of the two movement scales to the typical
distances between encounters. So, this optimization necessarily requires some
prior information (albeit crude) about targets distances or distributions.
Furthermore, we found that the incorporation of additional (three, four, ...)
movement scales and its adjustment to target distances does not improve further
the search efficiency. This allows us to claim that optimal random search
strategies in the asymmetric regime actually arise through the informed
combination of only two walk scales (related to the exploitative and the
explorative scale, respectively), expanding on the well-known result that
optimal strategies in strictly uninformed scenarios are achieved through L\'evy
paths (or, equivalently, through a hierarchical combination of multiple
scales)
Expectation-Maximization Binary Clustering for Behavioural Annotation
We present a variant of the well sounded Expectation-Maximization Clustering
algorithm that is constrained to generate partitions of the input space into
high and low values. The motivation of splitting input variables into high and
low values is to favour the semantic interpretation of the final clustering.
The Expectation-Maximization binary Clustering is specially useful when a
bimodal conditional distribution of the variables is expected or at least when
a binary discretization of the input space is deemed meaningful. Furthermore,
the algorithm deals with the reliability of the input data such that the larger
their uncertainty the less their role in the final clustering. We show here its
suitability for behavioural annotation of movement trajectories. However, it
can be considered as a general purpose algorithm for the clustering or
segmentation of multivariate data or temporal series.Comment: 34 pages main text including 11 (full page) figure
Optimal intermittence in search strategies under speed-selective target detection
Random search theory has been previously explored for both continuous and intermittent scanning modes with full target detection capacity. Here we present a new class of random search problems in which a single searcher performs flights of random velocities, the detection probability when it passes over a target location being conditioned to the searcher speed. As a result, target detection involves an N-passage process for which the mean search time is here analytically obtained through a renewal approximation. We apply the idea of speed-selective detection to random animal foraging since a fast movement is known to significantly degrade perception abilities in many animals. We show that speed-selective detection naturally introduces an optimal level of behavioral intermittence in order to solve the compromise between fast relocations and target detection capability
Intermittent Motion in Desert Locusts: Behavioural Complexity in Simple Environments
10 páginas, 4 figuras.Animals can exhibit complex movement patterns that may be the result of interactions with their environment or may be
directly the mechanism by which their behaviour is governed. In order to understand the drivers of these patterns we
examine the movement behaviour of individual desert locusts in a homogenous experimental arena with minimal external
cues. Locust motion is intermittent and we reveal that as pauses become longer, the probability that a locust changes
direction from its previous direction of travel increases. Long pauses (of greater than 100 s) can be considered reorientation
bouts, while shorter pauses (of less than 6 s) appear to act as periods of resting between displacements. We observe powerlaw
behaviour in the distribution of move and pause lengths of over 1.5 orders of magnitude. While Le´vy features do exist,
locusts’ movement patterns are more fully described by considering moves, pauses and turns in combination. Further
analysis reveals that these combinations give rise to two behavioural modes that are organized in time: local search
behaviour (long exploratory pauses with short moves) and relocation behaviour (long displacement moves with shorter
resting pauses). These findings offer a new perspective on how complex animal movement patterns emerge in nature.The authors acknowledge support from the Natural Environment Research Council (S.B.), the Spanish Ministry of Science and Innovation: MICINN-RyC
2009-04133 and BFU2010-22337 (F.B.) Searle Scholars Award 08-SPP-201 (I.D.C.), National Science Foundation Award PHY-0848755 (I.D.C.), Office of Naval
Research Award N00014-09-1-1074 (I.D.C.) and a DARPA Grant No. HR0011-09-1-0055 (to Princeton University) and an Army Research Office Grant W911NG-11-1-
0385 (I.D.C.).Peer reviewe
Epidemic thresholds and human mobility
A comprehensive view of disease epidemics demands a deep understanding of the complex interplay between human behaviour and infectious diseases. Here, we propose a flexible modelling framework that brings conclusions about the influence of human mobility and disease transmission on early epidemic growth, with applicability in outbreak preparedness. We use random matrix theory to compute an epidemic threshold, equivalent to the basic reproduction number [Formula: see text], for a SIR metapopulation model. The model includes both systematic and random features of human mobility. Variations in disease transmission rates, mobility modes (i.e. commuting and migration), and connectivity strengths determine the threshold value and whether or not a disease may potentially establish in the population, as well as the local incidence distribution.The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under Agreement HR-18-0036, and the program EU Horizon 2020, Grant Agreement “VEO” No. 874735.S
Modeling the impact of surveillance activities combined with physical distancing interventions on COVID-19 epidemics at a local level
Physical distancing and contact tracing are two key components in controlling the COVID-
19 epidemics. Understanding their interaction at local level is important for policymakers.
We propose a flexible modeling framework to assess the effect of combining contact
tracing with different physical distancing strategies. Using scenario tree analyses, we
compute the probability of COVID-19 detection using passive surveillance, with and
without contact tracing, in metropolitan Barcelona. The estimates of detection probability
and the frequency of daily social contacts are fitted into an age-structured susceptible-
exposed-infectious-recovered compartmental model to simulate the epidemics consid-
ering different physical distancing scenarios over a period of 26 weeks. With the original
Wuhan strain, the probability of detecting an infected individual without implementing
physical distancing would have been 0.465, 0.515, 0.617, and 0.665 in designated age
groups (0e14, 15e49, 50e64, and >65), respectively. As the physical distancing measures
were reinforced and the disease circulation decreased, the interaction between the two
interventions resulted in a reduction of the detection probabilities; however, despite this
reduction, active contact tracing and isolation remained an effective supplement to
physical distancing. If we relied solely on passive surveillance for diagnosing COVID-19, the
model required a minimal 50% (95% credible interval, 39e69%) reduction of daily social
contacts to keep the infected population under 5%, as compared to the 36% (95% credible
interval, 22e56%) reduction with contact tracing systems. The simulation with the B.1.1.7
and B.1.167.2 strains shows similar results. Our simulations showed that a functioning
contact tracing program would reduce the need for physical distancing and mitigate the
COVID-19 epidemics.info:eu-repo/semantics/publishedVersio
Signatures of chaos in animal search patterns
One key objective of the emerging discipline of movement ecology is to link animal movement patterns to underlying biological processes, including those operating at the neurobiological level. Nonetheless, little is known about the physiological basis of animal movement patterns, and the underlying search behaviour. Here we demonstrate the hallmarks of chaotic dynamics in the movement patterns of mud snails (Hydrobia ulvae) moving in controlled experimental conditions, observed in the temporal dynamics of turning behaviour. Chaotic temporal dynamics are known to occur in pacemaker neurons in molluscs, but there have been no studies reporting on whether chaotic properties are manifest in the movement patterns of molluscs. Our results suggest that complex search patterns, like the Levy walks made by mud snails, can have their mechanistic origins in chaotic neuronal processes. This possibility calls for new research on the coupling between neurobiology and motor properties.</p
El papel de la movilidad geográfica en la expansión de Aedes albopictus y en la transmisión de enfermedades infecciosas
Ponencia sobre el papel de la movilidad geográfica en la expansión de Aedes albopictus y en la transmisión de enfermedades infecciosas. Se estudia la dispersión de Aedes albopictus basados en el análisis de datos de la invasión en la provincia de Girona durante 2009-2011; y la dispersión epidémica en redes desarrollando un marco teórico para la modelización de la propagación de enfermedades infecciosas.N
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