Spatial Spreading Research Articles

Modeling future spread of infections via mobile geolocation data and population dynamics.An application to COVID-19 in Brazil.

Mobile geolocation data is a valuable asset in the assessment of movement patterns of a population. Once a highly contagious disease takes place in a location the movement patterns aid in predicting the potential spatial spreading of the disease, hence mobile data becomes a crucial tool to epidemic models. In this work, based on millions of anonymized mobile visits data in Brazil, we investigate the most probable spreading patterns of the COVID-19 within states of Brazil. The study is intended to help public administrators in action plans and resources allocation, whilst studying how mobile geolocation data may be employed as a measure of population mobility during an epidemic. This study focuses on the states of São Paulo and Rio de Janeiro during the period of March 2020, when the disease first started to spread in these states. Metapopulation models for the disease spread were simulated in order to evaluate the risk of infection of each city within the states, by ranking them according to the time the disease will take to infect each city. We observed that, although the high-risk regions are those closer to the capital cities, where the outbreak has started, there are also cities in the countryside with great risk. The mathematical framework developed in this paper is quite general and may be applied to locations around the world to evaluate the risk of infection by diseases, in special the COVID-19, when geolocation data is available.

How the individual human mobility spatio-temporally shapes the disease transmission dynamics

Human mobility plays a crucial role in the temporal and spatial spreading of infectious diseases. During the past few decades, researchers have been extensively investigating how human mobility affects the propagation of diseases. However, the mechanism of human mobility shaping the spread of epidemics is still elusive. Here we examined the impact of human mobility on the infectious disease spread by developing the individual-based SEIR model that incorporates a model of human mobility. We considered the spread of human influenza in two contrasting countries, namely, Belgium and Martinique, as case studies, to assess the specific roles of human mobility on infection propagation. We found that our model can provide a geo-temporal spreading pattern of the epidemics that cannot be captured by a traditional homogenous epidemic model. The disease has a tendency to jump to high populated urban areas before spreading to more rural areas and then subsequently spread to all neighboring locations. This heterogeneous spread of the infection can be captured by the time of the first arrival of the infection (T_{fi} ), which relates to the landscape of the human mobility characterized by the relative attractiveness. These findings can provide insights to better understand and forecast the disease spreading.

First-principles study of 2,6-dimethyl-3,5-heptanedione: a β-diketone molecular switch induced by hydrogen transfer

In this research, using nonequilibrium green’s function integrated with density functional theory, we investigate the electronic transport properties of a β-diketone (2,6-dimethyl-3,5-heptanedione) molecular wire induced by hydrogen transfer. The title molecule can be converted between two enol and keto forms. The electronic transmission factors, spatial spreading of molecular projected self-consistent Hamiltonian orbitals, on–off ratio, I–V characteristics, three different adsorption types (hollow, top, and bridge), the alteration of the electrode materials, Y, (Y = Au, Ag, and Pt), and HOMO–LUMO gaps relevant to these forms are thoroughly discussed. It can be concluded that due to the deformation of the title molecule (enol → keto), there is a noticeable change in conductivity. As a result of this deformation, the conductivity is switched from on state (high conductivity and low resistance) to off state (low conductivity and high resistance).

On the performance of code word diversity based quasi orthogonal space time block codes in multiple-input-multiple-output systems

In the recent past, a lot of researches have been put into designing a Multiple-Input-Multiple-Output (MIMO) system to provide multimedia services with higher quality and at higher data rate. On par with these requirements, a novel Quasi Orthogonal Space Time Block Code (QOSTBC) scheme based on code word diversity is proposed, which is a multi-dimensional approach, in this paper. The term code word diversity is coined, since the information symbols were spread across many code words in addition to traditional time and spatial spreading, without increasing transmission power and bandwidth. The receiver with perfect channel state information estimates the transmitted symbols with less probability of error, as more number of samples is available to estimate given number of symbols due to the extra diversity due to code words. The simulation results show a significant improvement in the Bit Error Rate (BER) performance of the proposed scheme when compared with the conventional schemes.

Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh.

Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.

Numerical study of an unsteady confined thermal plume under the influence of gas radiation

Influence of gas radiation on a thermal plume initiated by a linear heat source in a confined cavity is investigated through numerical simulations. A 2D pure convection case is first considered to validate the numerical code by comparing the critical Rayleigh number against literature results. Then, simulations are extended to 3D configurations for three different values of the Rayleigh number: 106, 1.2×106and 1.2×107. The evolution of the plume is analyzed and shows that the transition to unsteadiness appears much earlier for 3D case. Periodic solutions in time exhibit a stationary plane wave which is further broken in the chaotic regime. Finally, gas radiation is introduced at Ra=1.2×107by considering different gaseous media: on one hand, a gray gas model with various optical thicknesses, and on the other hand, a real gas model for humid air (air - H2O mixture). Results show a strong influence of the radiative transfer on flow regimes. By increasing the optical thickness, radiation tends to stabilize the plume and delays the onset of unsteadiness. Comparison of the time-averaged temperature and velocity distributions for the considered gas models indicates that gas radiation reduces the spatial spreading of the plume but has little effects on the kinetic field.

Modelling the dynamics of tuberculosis lesions in a virtual lung: Role of the bronchial tree in endogenous reinfection.

Tuberculosis (TB) is an infectious disease that still causes more than 1.5 million deaths annually. The World Health Organization estimates that around 30% of the world’s population is latently infected. However, the mechanisms responsible for 10% of this reserve (i.e., of the latently infected population) developing an active disease are not fully understood, yet. The dynamic hypothesis suggests that endogenous reinfection has an important role in maintaining latent infection. In order to examine this hypothesis for falsifiability, an agent-based model of growth, merging, and proliferation of TB lesions was implemented in a computational bronchial tree, built with an iterative algorithm for the generation of bronchial bifurcations and tubes applied inside a virtual 3D pulmonary surface. The computational model was fed and parameterized with computed tomography (CT) experimental data from 5 latently infected minipigs. First, we used CT images to reconstruct the virtual pulmonary surfaces where bronchial trees are built. Then, CT data about TB lesion’ size and location to each minipig were used in the parameterization process. The model’s outcome provides spatial and size distributions of TB lesions that successfully reproduced experimental data, thus reinforcing the role of the bronchial tree as the spatial structure triggering endogenous reinfection. A sensitivity analysis of the model shows that the final number of lesions is strongly related with the endogenous reinfection frequency and maximum growth rate of the lesions, while their mean diameter mainly depends on the spatial spreading of new lesions and the maximum radius. Finally, the model was used as an in silico experimental platform to explore the transition from latent infection to active disease, identifying two main triggering factors: a high inflammatory response and the combination of a moderate inflammatory response with a small breathing amplitude.

Modelling of Spatial Spreading of Invasions in the Discrete Homogeneous Environment

Modelling of Spatial Spreading of Invasions in the Discrete Homogeneous Environment

Imitation dynamics in the mitigation of the novel coronavirus disease (COVID-19) outbreak in Wuhan, China from 2019 to 2020.

BackgroundThe coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China on December 2019 in patients presenting with atypical pneumonia. Although ‘city-lockdown’ policy reduced the spatial spreading of the COVID-19, the city-level outbreaks within each city remain a major concern to be addressed. The local or regional level disease control mainly depends on individuals self-administered infection prevention actions. The contradiction between choice of taking infection prevention actions or not makes the elimination difficult under a voluntary acting scheme, and represents a clash between the optimal choice of action for the individual interest and group interests.MethodsWe develop a compartmental epidemic model based on the classic susceptible-exposed-infectious-recovered model and use this to fit the data. Behavioral imitation through a game theoretical decision-making process is incorporated to study and project the dynamics of the COVID-19 outbreak in Wuhan, China. By varying the key model parameters, we explore the probable course of the outbreak in terms of size and timing under several public interventions in improving public awareness and sensitivity to the infection risk as well as their potential impact.ResultsWe estimate the basic reproduction number, R0, to be 2.5 (95% CI: 2.4−2.7). Under the current most realistic setting, we estimate the peak size at 0.28 (95% CI: 0.24−0.32) infections per 1,000 population. In Wuhan, the final size of the outbreak is likely to infect 1.35% (95% CI: 1.00−2.12%) of the population. The outbreak will be most likely to peak in the first half of February and drop to daily incidences lower than 10 in June 2020. Increasing sensitivity to take infection prevention actions and the effectiveness of infection prevention measures are likely to mitigate the COVID-19 outbreak in Wuhan.ConclusionsThrough an imitating social learning process, individual-level behavioral change on taking infection prevention actions have the potentials to significantly reduce the COVID-19 outbreak in terms of size and timing at city-level. Timely and substantially resources and supports for improving the willingness-to-act and conducts of self-administered infection prevention actions are recommended to reduce to the COVID-19 associated risks.

Regional spreading of symptoms at diagnosis as a prognostic marker in amyotrophic lateral sclerosis: a population-based study

ObjectiveThe lack of prognostic biomarkers in patients with amyotrophic lateral sclerosis (ALS) induced researchers to develop clinical evaluation tools for stratification and survival prediction. We assessed the correlation between patterns...

Research advances in intervening opportunity class models for predicting human mobility

Predicting human mobility between locations is of great significance for investigating the population migration, traffic forecasting, epidemic spreading, commodity trade, social interaction and other relevant areas. The intervening opportunity (IO) model is the model established earliest from the perspective of individual choice behavior to predict human mobility. The IO model takes the total number of opportunities between the origin location and the destination as a key factor in determining human mobility, which has inspired researchers to propose many new IO class models. In this paper, we first review the research advances in the IO class models, including the IO model, radiation class models, population-weighted opportunity class models, exploratory IO class models and universal opportunity model. Among them, although the IO model has an important theoretical value, it contains parameters and has low prediction accuracy, so it is rarely used in practice. The radiation class models are built on the basis of the IO model on the assumption that the individual will choose the closest destination whose benefit is higher than the best one available in origin location. The radiation class models can better predict the commuting behavior between locations. The population-weighted opportunity class models are established on the assumption that when seeking a destination, the individual will not only consider the nearest locations with relatively large benefits, but also consider all locations in the range of alternative space. The population-weighted opportunity class models can better predict intracity trips and intercity travels. The exploratory IO class models are built on condition that the destination selected by the individual presents a higher benefit than the benefit of the origin and the benefits of the intervening opportunities. The exploratory IO class models can better predict the social interaction between individuals, intracity trips and intercity travels. The universal opportunity model is developed on the assumption that when an individual selects a destination, she/he will comprehensively compare the benefits between the origin and the destination and their intervening opportunity. The universal opportunity model presents a new universal framework for IO class models and can accurately predict the movements on different spatiotemporal scales. The IO class models have also been widely used in many fields, including predicting trip distribution in transportation science, modeling the purchasing behaviors of consumers in economics, detecting complex network communities in network science, measuring spatial interaction in economic geography and predicting infectious disease transmission in epidemiology. This paper focuses on the applications of IO class models in spatial interaction and epidemic spreading, and finally presents the discussion on the possible future research directions of these models.

A model for spatial spreading and dynamics of fox rabies on a growing domain

A model for spatial spreading and dynamics of fox rabies on a growing domain

Statistical Analysis of Spatial Spreading of Bicycling on the Left Side of Streets by Introducing Bicycle Marking at Residential Streets

Statistical Analysis of Spatial Spreading of Bicycling on the Left Side of Streets by Introducing Bicycle Marking at Residential Streets

Influence of the mode of reproduction on dispersal evolution during species invasion

We consider a reaction-diffusion-reproduction equation, modeling a population which is spatially heterogeneous. The dispersion of each individuals is influenced by its phenotype. In the literature, the asymptotic propagation speed of an asexual population has already been rigorously determined. In this paper we focus on the difference between the asexual reproduction case, and the sexual reproduction case, involving a non-local term modeling the reproduction. This comparison leads to a different invasion speed according to the reproduction. After a formal analysis of both cases, leading to a heuristic of the asymptotic behaviour of the invasion fronts, we give some numerical evidence that the acceleration rate of the spatial spreading of a sexual population is slower than the acceleration rate of an asexual one. The main difficulty to get sharper results on a transient comes from the non-local sexual reproduction term.

Dynamics of crime activities in the network of city community areas

Understanding criminal activities, their structure and dynamics are fundamental for designing tools for crime prediction that can also guide crime prevention. Here, we study crimes committed in city community areas based on police crime reports and demographic data for the City of Chicago collected over 16 consecutive years. Our goal is to understand how the network of city community areas shapes dynamics of criminal offenses and demographic characteristics of their inhabitants. Our results reveal the presence of criminal hot-spots and expose the dynamic nature of criminal activities. We identify the most influential features for forecasting the per capita crime rate in each community. Our results indicate that city community crime is driven by spatio-temporal dynamics since the number of crimes committed in the past among the spatial neighbors of each community area and in the community itself are the most important features in our predictive models. Moreover, certain urban characteristics appear to act as triggers for the spatial spreading of criminal activities. Using the k-Means clustering algorithm, we obtained three clearly separated clusters of community areas, each with different levels of crimes and unique demographic characteristics of the district’s inhabitants. Further, we demonstrate that crime predictive models incorporating both demographic characteristics of a community and its crime rate perform better than models relying only on one type of features. We develop predictive algorithms to forecast the number of future crimes in city community areas over the periods of one-month and one-year using varying sets of features. For one-month predictions using just the number of prior incidents as a feature, the critical length of historical data, τc, of 12 months arises. Using more than τc months ensures high accuracy of prediction, while using fewer months negatively impacts prediction quality. Using features based on demographic characteristics of the district’s inhabitants weakens this impact somewhat. We also forecast the number of crimes in each community area in the given year. Then, we study in which community area and over what period an increase in crime reduction funding in this area will yield the largest reduction of the crime in the entire city. Finally, we study and compare the performance of various supervised machine learning algorithms classifying reported crime incidents into the correct crime category. Using the temporal patterns of various crime categories improves the classification accuracy. The methodologies introduced here are general and can be applied to other cities for which data about criminal activities and demographics are available.

Phraseological units for denote the concets hungry, to starve in dialect language

The article explores the peculiarities of the use and the spatial spreading of phraseological to denote the concepts of hungry, to starve on the material of modern vocabulary of dialect speech. It is determined that the condition and feeling of a person are subject of phraseologization through codes of somatism, manifestation of a greater or lesser degree of a feature. Phraseologisms with dialectic components encode a person's general conceptions and their ability to characterize or evaluate something. In certain territories, we determine the specificity of metaphorical figurativeness, the emphasis on a different scale of feature expression.The lexicographic works on dialect language make it possible to study the locally dialect features of phraseologisms and their components, in particular in time and space. Fixed expressions for denoting the concepts hungry, to starve differ by figurativeness and emotiveness, anthropomorphic features, manifested through somatism, nominations of household items, elements of the house and the like.The given analysis of examples of phraseological units applying is only preliminary, but it testifies that in a live speech of the native speakers, the greatest functional possibilities of phrases are revealed both as expressors of states and feelings of the person, and as realizers of communicative potential.

A qualitative study on the development and rectification of advanced medical students' misconceptions about the physiological electrocardiogram (ECG).

Purpose: The study takes a qualitative and explorative approach to capture the concepts that German medical students have of the physiological electrocardiogram (ECG) which they acquired during their preclinical training. These concepts are then considered for possible misconceptions. Afterwards a theory-based intervention which allows subjects to see the connection of the curve progression with the spatial spreading of excitation (animated vector loop) is put to the test.Methods: In the course of a diagnosis of learning potentials, individual students participated in problem-centred, guided interviews. The developed intervention was tested in separately conducted teaching experiments using thinking aloud protocols. The data evaluation was done through qualitative content analysis. Based on the conceptual metaphor theory, conceptions and their underlying embodied cognition were analysed.Results: One of the subjects’ typical misinterpretations is taking the progression of the ECG tracing for a mere increase and decrease of the myocardium’s electrical activity, rather than connecting it with its spatial and temporal aspects. The data evaluation has shown that the newly developed theory-based intervention can lead to re-learning. Reconstructed metaphorical concepts illustrate this process of understanding. It is exemplarily shown how, through the course of the interviews, students are enabled to appropriately explain ECGs as the two-dimensional representation of the spatial excitation propagation in the heart.Conclusion: By capturing typical misconceptions of the physiological electrocardiogram and demonstrating interventions that support learning, the study provides a contribution to comprehensive learning which can be used in the basic education of medical students.

Spreading speed for a nonlocal diffusive delayed model without quasi-monotonicity

In this article we study the spatial spreading dynamics for a nonlocal diffusive equation with general nonmonotone monostable nonlinearity. On the basis of a sandwich technique and a comparison theorem for solutions of the Cauchy problems of three systems, the spreading speed c⁎ is established for the model. As an application of the results, we consider some models as special cases. It is shown that the results in this article generalize and improve some results in the existing literature.

Continuum descriptions of spatial spreading for heterogeneous cell populations: Theory and experiment

Variability in cell populations is frequently observed in both in vitro and in vivo settings. Intrinsic differences within populations of cells, such as differences in cell sizes or differences in rates of cell motility, can be present even within a population of cells from the same cell line. We refer to this variability as cell heterogeneity. Mathematical models of cell migration, for example, in the context of tumour growth and metastatic invasion, often account for both undirected (random) migration and directed migration that is mediated by cell-to-cell contacts and cell-to-cell adhesion. A key feature of standard models is that they often assume that the population is composed of identical cells with constant properties. This leads to relatively simple single-species homogeneous models that neglect the role of heterogeneity. In this work, we use a continuum modelling approach to explore the role of heterogeneity in spatial spreading of cell populations. We employ a three-species heterogeneous model of cell motility that explicitly incorporates different types of experimentally-motivated heterogeneity in cell sizes: (i) monotonically decreasing; (ii) uniform; (iii) non-monotonic; and (iv) monotonically increasing distributions of cell size. Comparing the density profiles generated by the three-species heterogeneous model with density profiles predicted by a more standard single-species homogeneous model reveals that when we are dealing with monotonically decreasing and uniform distributions a simple and computationally efficient single-species homogeneous model can be remarkably accurate in describing the evolution of a heterogeneous cell population. In contrast, we find that the simpler single-species homogeneous model performs relatively poorly when applied to non-monotonic and monotonically increasing distributions of cell sizes. Additional results for heterogeneity in parameters describing both undirected and directed cell migration are also considered, and we find that similar results apply.

Temporal and spatial spread of Hypoderma actaeon infection in roe deer from peninsular Spain determined by an indirect enzyme-linked immunosorbent assay.

The host switching of Hypoderma actaeon (Diptera: Oestridae), a specific parasite of red deer (Cervus elaphus), towards roe deer (Capreolus capreolus) has been recently reported in Spain. To provide information about the temporal and spatial spreading of H. actaeon infection in roe deer, 244 serum samples from animals hunted in Spain between 2013 and 2018 were analysed by an indirect enzyme-linked immunosorbent assay. The overall seropositivity was 13.9%. Seropositivity was higher in continental (27.7%) and mountainous (12%) areas from central Spain, followed by southern-Mediterranean (11.2%) and northern-oceanic regions (3.5%). Differences were significant between central-continental and northern-oceanic regions (P = 0.003). No differences were found according to the sex and age of roe deer (P > 0.05). In 2013, all seropositive animals were concentrated in two distant areas in central and southern Spain, suggesting that the host switch could have occurred independently in both regions. Changes in the pattern of distribution of red deer and roe deer could have favoured the spreading of this myiasis towards roe deer, indicating that roe deer may become infested by H. actaeon in areas where both cervids coexist at high densities.