162 research outputs found
The statistics of fixation times for systems with recruitment
We investigate the statistics of the time taken for a system driven by
recruitment to reach fixation. Our model describes a series of experiments
where a population is confronted with two identical options, resulting in the
system fixating on one of the options. For a specific population size, we show
that the time distribution behaves like an inverse Gaussian with an exponential
decay. Varying the population size reveals that the timescale of the decay
depends on the population size and allows the critical population number, below
which fixation occurs, to be estimated from experimental data
Noise-Induced Bistable States and Their Mean Switching Time in Foraging Colonies
We investigate a type of bistability where noise not only causes transitions
between stable states, but also constructs the states themselves. We focus on
the experimentally well-studied system of ants choosing between two food
sources to illustrate the essential points, but the ideas are more general. The
mean time for switching between the two bistable states of the system is
calculated. This suggests a procedure for estimating, in a real system, the
critical population size above which bistability ceases to occur.Comment: 8 pages, 5 figures. See also a "light-hearted" introduction:
http://www.youtube.com/watch?v=m37Fe4qjeZ
Prospects for detecting early warning signals in discrete event sequence data : application to epidemiological incidence data
Early warning signals (EWS) identify systems approaching a critical transition, where the system undergoes a sudden change in state. For example, monitoring changes in variance or autocorrelation offers a computationally inexpensive method which can be used in real-time to assess when an infectious disease transitions to elimination. EWS have a promising potential to not only be used to monitor infectious diseases, but also to inform control policies to aid disease elimination. Previously, potential EWS have been identified for prevalence data, however the prevalence of a disease is often not known directly. In this work we identify EWS for incidence data, the standard data type collected by the Centers for Disease Control and Prevention (CDC) or World Health Organization (WHO). We show, through several examples, that EWS calculated on simulated incidence time series data exhibit vastly different behaviours to those previously studied on prevalence data. In particular, the variance displays a decreasing trend on the approach to disease elimination, contrary to that expected from critical slowing down theory; this could lead to unreliable indicators of elimination when calculated on real-world data. We derive analytical predictions which can be generalised for many epidemiological systems, and we support our theory with simulated studies of disease incidence. Additionally, we explore EWS calculated on the rate of incidence over time, a property which can be extracted directly from incidence data. We find that although incidence might not exhibit typical critical slowing down properties before a critical transition, the rate of incidence does, presenting a promising new data type for the application of statistical indicators
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eInk versus paper: exploring the effects of medium and typographic quality on recall and reading speed
This study compares the effects of reading from paper and an eInk display on recall and reading speed alongside the effects of changes in typographic quality (fluent and disfluent conditions). Both medium and typographic quality were between-subject variables resulting in four groups of participants. Each participant was timed while they read one text. They then completed a general questionnaire before answering multiple-choice questions evaluating their recall of the content they had read. Comparable reading speeds for paper and eInk were recorded and these were slower for disfluent conditions. Improved typographic quality significantly enhanced recall on eInk, whereas for paper participants who read the disfluent condition recalled more. These findings suggest that typographic quality has a significant effect on reading, which is also influenced by the medium. Although recall was better in the disfluent paper condition, some caution should be observed in translating this into recommendations that would result in more effortful reading
A chemotactic model of trunk neural crest cell migration
Trunk neural crest cells follow a common ventral migratory pathway but are distributed into two distinct locations to form discrete sympathetic and dorsal root ganglia along the vertebrate axis. Although fluorescent cell labeling and time‐lapse studies have recorded complex trunk neural crest cell migratory behaviors, the signals that underlie this dynamic patterning remain unclear. The absence of molecular information has led to a number of mechanistic hypotheses for trunk neural crest cell migration. Here, we review recent data in support of three distinct mechanisms of trunk neural crest cell migration and develop and simulate a computational model based on chemotactic signaling. We show that by integrating the timing and spatial location of multiple chemotactic signals, trunk neural crest cells may be accurately positioned into two distinct targets that correspond to the sympathetic and dorsal root ganglia. In doing so, we honor the contributions of Wilhelm His to his identification of the neural crest and extend the observations of His and others to better understand a complex question in neural crest cell biology
Insights from quantitative and mathematical modelling on the proposed 2030 goals for Yaws.
The World Health Organization is currently developing 2030 goals for neglected tropical diseases (NTDs). In these, yaws has been targeted for eradication by 2030, with 50% of member states certified free of yaws transmission by 2023. Here we summarise the yaws modelling literature and discuss the proposed goal and strategy. The current Morges strategy involves rounds of Total Community Treatment (TCT), in which all members of the community are treated, and Total Targeted Treatment (TTT), treating active cases and their contacts. However, modelling and empirical work suggest that latent infections are often not found in the same household as active cases, reducing the utility of household-based contact tracing for a TTT strategy. Economic modelling has also discovered uncertainty in the cost of eradication, requiring further data to give greater information. We also note the need for improved active surveillance in previously endemic countries, in order to plan future intervention efforts and ensure global eradication
Exploring the role of the potential surface in the behaviour of early warning signals
The theory of critical slowing down states that a system displays increasing relaxation times as it approaches a critical transition. These changes can be seen in statistics generated from timeseries data, which can be used as early warning signals of a transition. Such early warning signals would be of value for emerging infectious diseases or to understand when an endemic disease is close to elimination. However, in applications to a variety of epidemiological models there is frequent disagreement with the general theory of critical slowing down, with some indicators performing well on prevalence data but not when applied to incidence data. Furthermore, the alternative theory of critical speeding up predicts contradictory behaviour of early warning signals prior to some stochastic transitions. To investigate the possibility of observing critical speeding up in epidemiological models we characterise the behaviour of common early warning signals in terms of a system’s potential surface and noise around a quasi-steady state. We then describe a method to obtain these key features from timeseries data, taking as a case study a version of the SIS model, adapted to demonstrate either critical slowing down or critical speeding up. We show this method accurately reproduces the analytic potential surface and diffusion function, and that these results can be used to determine the behaviour of early warning signals and correctly identify signs of both critical slowing down and critical speeding up
Approximating steady state distributions for household structured epidemic models
Household-structured infectious disease models consider the increased transmission potential between ind[a,]ividuals of the same household when compared with two individuals in different households. Accounting for these heterogeneities in transmission enables control measures to be more effectively planned. Ideally, pre-control data may be used to fit such a household-structured model at an endemic steady state, before making dynamic forward-predictions under different proposed strategies. However, this requires the accurate calculation of the steady states for the full dynamic model. We observe that steady state SIS dynamics with household structure cannot necessarily be described by the master equation for a single household, instead requiring consideration of the full system. However, solving the full system of equations becomes increasingly computationally intensive, particularly for higher-dimensional models. We compare two approximations to the full system: the single household master equation; and a proposed alternative method, using the Fokker-Planck equation. Moment closure is another commonly used method, but for more complicated systems, the equations quickly become unwieldy and very difficult to derive. In comparison, using the master equation for a single household is easily implementable, however it can be quite inaccurate. In this paper we compare these methods in terms of accuracy and ease of implementation. We find that there are regions of parameter space in which each method outperforms the other, and that these regions of parameter space can be characterised by the infection prevalence, or by the correlation between household states
Onset of collective motion in locusts is captured by a minimal model
We present a minimal model to describe the onset of collective motion seen when a population of locusts are placed in an annular arena. At low densities motion is disordered, while at high densities locusts march in a common direction, which may reverse during the experiment. The data are well captured by an individual-based model, in which demographic noise leads to the observed density-dependent effects. By fitting the model parameters to equation-free coefficients, we give a quantitative comparison, showing time series, stationary distributions, and the mean switching times between states
A generation of junior faculty is at risk from the impacts of COVID-19
For junior investigators starting their independent careers, the challenges of the Coronavirus Disease 2019 (COVID-19) pandemic extend beyond lost time and are career threatening. Without intervention, academic science could lose a generation of talent
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