scholarly journals The shape of the contact–density function matters when modelling parasite transmission in fluctuating populations

2017 ◽  
Vol 4 (11) ◽  
pp. 171308 ◽  
Author(s):  
Benny Borremans ◽  
Jonas Reijniers ◽  
Niel Hens ◽  
Herwig Leirs
Keyword(s):  
Density Function ◽  
Disease Transmission ◽  
Field Data ◽  
Infectious Agent ◽  
Real Data ◽  
Infection Prevalence ◽  
Contact Density ◽  
Rodent Host ◽  
Constant Frequency ◽  
Fluctuating Populations

Models of disease transmission in a population with changing densities must assume a relation between infectious contacts and density. Typically, a choice is made between a constant (frequency-dependence) and a linear (density-dependence) contact–density function, but it is becoming increasingly clear that intermediate, nonlinear functions are more realistic. It is currently not clear, however, what the exact consequences would be of different contact–density functions in fluctuating populations. By combining field data on rodent host ( Mastomys natalensis ) demography, experimentally derived contact–density data, and laboratory and field data on Morogoro virus infection dynamics, we explored the effects of different contact–density function shapes on transmission dynamics and invasion/persistence. While invasion and persistence were clearly affected by the shape of the function, the effects on outbreak characteristics such as infection prevalence and seroprevalence were less obvious. This means that it may be difficult to distinguish between the different shapes based on how well models fit to real data. The shape of the transmission–density function should therefore be chosen with care, and is ideally based on existing information such as a previously quantified contact– or transmission–density relationship or the underlying biology of the host species in relation to the infectious agent.

scholarly journals The shape of the contact-density function matters when modelling parasite transmission in fluctuating populations

2017 ◽  
Author(s):  
Benny Borremans ◽  
Jonas Reijniers ◽  
Niel Hens ◽  
Herwig Leirs
Keyword(s):  
Density Function ◽  
Disease Transmission ◽  
Field Data ◽  
Infectious Agent ◽  
Real Data ◽  
Infection Prevalence ◽  
Contact Density ◽  
Rodent Host ◽  
Constant Frequency ◽  
Fluctuating Populations

AbstractModels of disease transmission in a population with changing densities must assume a relation between infectious contacts and density. Typically, a choice is made between a constant (frequency-dependence) and a linear (density-dependence) contact-density function, but it is becoming increasingly clear that intermediate, nonlinear functions intermediate are more realistic. It is currently not clear however what the exact consequences would be of different contact-density functions in fluctuating populations. By combining field data on rodent host (Mastomys natalensis) demography, experimentally-derived contact-density data, and laboratory and field data Morogoro virus infection dynamics, we explored the effects of different contact-density function shapes on transmission dynamics and invasion/persistence. While invasion and persistence were clearly affected by the shape of the function, the effects on outbreak characteristics such as infection prevalence and seroprevalence were less obvious. This means that it may be difficult to distinguish between the different shapes based on how well models fit to real data. The shape of the transmission-density function should therefore be chosen with care, and is ideally based on existing information such as a previously quantified contact- or transmission-density relationship or the underlying biology of the host species in relation to the infectious agent.

scholarly journals Who acquires infection from whom? Estimating herpesvirus transmission rates between wild rodent host groups

2020 ◽  
Author(s):  
Diana Erazo ◽  
Amy B. Pedersen ◽  
Kayleigh Gallagher ◽  
Andy Fenton
Keyword(s):  
Population Dynamic ◽  
Disease Transmission ◽  
Field Data ◽  
Dynamic Models ◽  
Research Effort ◽  
Host Population ◽  
Transmission Model ◽  
Rodent Host ◽  
Demographic Groups ◽  
Host Sex

AbstractTo date, few studies of parasite epidemiology have investigated ‘who acquires infection from whom’ in wildlife populations. Nonetheless, identifying routes of disease transmission within a population, and determining the key groups of individuals that drive parasite transmission and maintenance, are fundamental to understanding disease dynamics. Gammaherpesviruses are a widespread group of DNA viruses that infect many vertebrate species, and murine gammaherpesviruses (i.e. MuHV-4) are a standard lab model for studying human herpesviruses, for which much about the pathology and immune response elicited to infection is well understood. However, despite this extensive research effort, primarily in the lab, the transmission route of murine gammaherpesviruses within their natural host populations is not well understood. Here, we aimed to understand wood mouse herpesvirus (WMHV) transmission, by fitting a series of population dynamic models to field data on wood mice naturally infected with WMHV and then estimating transmission parameters within and between demographic groups of the host population. Different models accounted for different combinations of host sex (male/female), age (subadult/adult) and transmission functions (density/frequency-dependent). We found that a density-dependent transmission model incorporating explicit sex groups fitted the data better than all other proposed models. Male-to-male transmission was the highest among all demographic groups, suggesting that male behaviour is a key factor driving WMHV transmission. Our models also suggest that transmission between sexes, although important, wasn’t symmetrical, with infected males playing a significant role in infecting naïve females but not vice versa. Overall this work shows the power of coupling population dynamic models with long-term field data to elucidate otherwise unobservable transmission processes in wild disease systems.

scholarly journals Linking Zoonosis Emergence to Farmland Invasion by Fluctuating Herbivores: Common Vole Populations and Tularemia Outbreaks in NW Spain

2021 ◽  
Vol 8 ◽  
Author(s):  
Silvia Herrero-Cófreces ◽  
François Mougeot ◽  
Xavier Lambin ◽  
Juan José Luque-Larena
Keyword(s):  
Disease Transmission ◽  
Common Vole ◽  
Future Research ◽  
Nw Spain ◽  
Rodent Host ◽  
Host Group ◽  
Muroid Rodents ◽  
Cyclic Variations ◽  
Common Voles ◽  
Fluctuating Populations

The expansion and intensification of agriculture are driving profound changes in ecosystems worldwide, favoring the (re)emergence of many human infectious diseases. Muroid rodents are a key host group for zoonotic infectious pathogens and frequently invade farming environments, promoting disease transmission and spillover. Understanding the role that fluctuating populations of farm dwelling rodents play in the epidemiology of zoonotic diseases is paramount to improve prevention schemes. Here, we review a decade of research on the colonization of farming environments in NW Spain by common voles (Microtus arvalis) and its public health impacts, specifically periodic tularemia outbreaks in humans. The spread of this colonizing rodent was analogous to an invasion process and was putatively triggered by the transformation and irrigation of agricultural habitats that created a novel terrestrial-aquatic interface. This irruptive rodent host is an effective amplifier for the Francisella tularensis bacterium during population outbreaks, and human tularemia episodes are tightly linked in time and space to periodic (cyclic) variations in vole abundance. Beyond the information accumulated to date, several key knowledge gaps about this pathogen-rodent epidemiological link remain unaddressed, namely (i) did colonizing vole introduce or amplified pre-existing F. tularensis? (ii) which features of the “Francisella—Microtus” relationship are crucial for the epidemiology of tularemia? (iii) how virulent and persistent F. tularensis infection is for voles under natural conditions? and (iv) where does the bacterium persist during inter-epizootics? Future research should focus on more integrated, community-based approaches in order to understand the details and dynamics of disease circulation in ecosystems colonized by highly fluctuating hosts.

scholarly journals Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain

2021 ◽  
Vol 11 (11) ◽  
pp. 5025
Author(s):  
David González-Peña ◽  
Ignacio García-Ruiz ◽  
Montserrat Díez-Mediavilla ◽  
Mª. Isabel Dieste-Velasco ◽  
Cristina Alonso-Tristán
Keyword(s):  
Energy Production ◽  
Field Data ◽  
Real Data ◽  
Software Tools ◽  
Horizontal Axis ◽  
Real Field ◽  
Tracking Systems ◽  
Northern Spain ◽  
Commercial Software ◽  
Solar Tracking

Prediction of energy production is crucial for the design and installation of PV plants. In this study, five free and commercial software tools to predict photovoltaic energy production are evaluated: RETScreen, Solar Advisor Model (SAM), PVGIS, PVSyst, and PV*SOL. The evaluation involves a comparison of monthly and annually predicted data on energy supplied to the national grid with real field data collected from three real PV plants. All the systems, located in Castile and Leon (Spain), have three different tilting systems: fixed mounting, horizontal-axis tracking, and dual-axis tracking. The last 12 years of operating data, from 2008 to 2020, are used in the evaluation. Although the commercial software tools were easier to use and their installations could be described in detail, their results were not appreciably superior. In annual global terms, the results hid poor estimations throughout the year, where overestimations were compensated by underestimated results. This fact was reflected in the monthly results: the software yielded overestimates during the colder months, while the models showed better estimates during the warmer months. In most studies, the deviation was below 10% when the annual results were analyzed. The accuracy of the software was also reduced when the complexity of the dual-axis solar tracking systems replaced the fixed installation.

Discrete linear transformations of potential field data

Geophysics ◽  
1989 ◽  
Vol 54 (4) ◽  
pp. 497-507 ◽  
Author(s):  
Jorge W. D. Leão ◽  
João B. C. Silva
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Amazon Basin ◽  
Real Data ◽  
Damping Factor ◽  
Linear Transformations ◽  
Potential Field Data ◽  
Data Window ◽  
Green’S Matrix ◽  
Equivalent Layer

We present a new approach to perform any linear transformation of gridded potential field data using the equivalent‐layer principle. It is particularly efficient for processing areas with a large amount of data. An N × N data window is inverted using an M × M equivalent layer, with M greater than N so that the equivalent sources extend beyond the data window. Only the transformed field at the center of the data window is computed by premultiplying the equivalent source matrix by the row of the Green’s matrix (associated with the desired transformation) corresponding to the center of the data window. Since the inversion and the multiplication by the Green’s matrix are independent of the data, they are performed beforehand and just once for given values of N, M, and the depth of the equivalent layer. As a result, a grid operator for the desired transformation is obtained which is applied to the data by a procedure similar to discrete convolution. The application of this procedure in reducing synthetic anomalies to the pole and computing magnetization intensity maps shows that grid operators with N = 7 and M = 15 are sufficient to process large areas containing several interfering sources. The use of a damping factor allows the computation of meaningful maps even for unstable transformations in the presence of noise. Also, an equivalent layer larger than the data window takes into account part of the interfering sources so that a smaller damping factor is employed as compared with other damped inversion methods. Transformations of real data from Xingú River Basin and Amazon Basin, Brazil, demonstrate the contribution of this procedure for improvement of a preliminary geologic interpretation with minimum a priori information.

scholarly journals On a new distribution based on the arccosine function

2021 ◽  
Author(s):  
Christophe Chesneau ◽  
Lishamol Tomy ◽  
Jiju Gillariose
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Hazard Rate ◽  
Survival Function ◽  
Real Data ◽  
Cumulative Distribution ◽  
Data Sets ◽  
Power Functions ◽  
New Distribution

AbstractThis note focuses on a new one-parameter unit probability distribution centered around the inverse cosine and power functions. A special case of this distribution has the exact inverse cosine function as a probability density function. To our knowledge, despite obvious mathematical interest, such a probability density function has never been considered in Probability and Statistics. Here, we fill this gap by pointing out the main properties of the proposed distribution, from both the theoretical and practical aspects. Specifically, we provide the analytical form expressions for its cumulative distribution function, survival function, hazard rate function, raw moments and incomplete moments. The asymptotes and shape properties of the probability density and hazard rate functions are described, as well as the skewness and kurtosis properties, revealing the flexible nature of the new distribution. In particular, it appears to be “round mesokurtic” and “left skewed”. With these features in mind, special attention is given to find empirical applications of the new distribution to real data sets. Accordingly, the proposed distribution is compared with the well-known power distribution by means of two real data sets.

scholarly journals A Probability Density Function Generator Based on Deep Learning

2018 ◽  
Author(s):  
Chi-Hua Chen ◽  
Fangying Song ◽  
Feng-Jang Hwang ◽  
Ling Wu
Keyword(s):  
Deep Learning ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Probability Distributions ◽  
Real Data ◽  
Learning Model ◽  
Cumulative Distribution ◽  
Function Generator ◽  
Deep Learning Model

To generate a probability density function (PDF) for fitting probability distributions of real data, this study proposes a deep learning method which consists of two stages: (1) a training stage for estimating the cumulative distribution function (CDF) and (2) a performing stage for predicting the corresponding PDF. The CDFs of common probability distributions can be adopted as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of trained deep learning model can be used to estimate the PDF. To evaluate the proposed method, numerical experiments with single and mixed distributions are performed. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method.

scholarly journals Spatiotemporal multi-disease transmission dynamic measure for emerging diseases: an application to dengue and zika integrated surveillance in Thailand

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Chawarat Rotejanaprasert ◽  
Andrew B. Lawson ◽  
Sopon Iamsirithaworn
Keyword(s):  
Disease Transmission ◽  
Hierarchical Modeling ◽  
Model Fitting ◽  
Real Data ◽  
Emerging Diseases ◽  
Integrated Analysis ◽  
Alternative Measure ◽  
Surveillance Systems ◽  
Modeling Framework ◽  
Important Challenge

Abstract Background New emerging diseases are public health concerns in which policy makers have to make decisions in the presence of enormous uncertainty. This is an important challenge in terms of emergency preparation requiring the operation of effective surveillance systems. A key concept to investigate the dynamic of infectious diseases is the basic reproduction number. However it is difficult to be applicable in real situations due to the underlying theoretical assumptions. Methods In this paper we propose a robust and flexible methodology for estimating disease strength varying in space and time using an alternative measure of disease transmission within the hierarchical modeling framework. The proposed measure is also extended to allow for incorporating knowledge from related diseases to enhance performance of surveillance system. Results A simulation was conducted to examine robustness of the proposed methodology and the simulation results demonstrate that the proposed method allows robust estimation of the disease strength across simulation scenarios. A real data example is provided of an integrative application of Dengue and Zika surveillance in Thailand. The real data example also shows that combining both diseases in an integrated analysis essentially decreases variability of model fitting. Conclusions The proposed methodology is robust in several simulated scenarios of spatiotemporal transmission force with computing flexibility and practical benefits. This development has potential for broad applicability as an alternative tool for integrated surveillance of emerging diseases such as Zika.

Event Location with Sparse Data: When Probabilistic Global Search is Important

2020 ◽  
Author(s):  
Stephen Arrowsmith ◽  
Junghyun Park ◽  
Il-Young Che ◽  
Brian Stump ◽  
Gil Averbuch
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Arrival Time ◽  
Real Data ◽  
Parameter Estimates ◽  
Model Parameter ◽  
Event Location ◽  
Seismic Location ◽  
Probability Density Function Pdf

Abstract Locating events with sparse observations is a challenge for which conventional seismic location techniques are not well suited. In particular, Geiger’s method and its variants do not properly capture the full uncertainty in model parameter estimates, which is characterized by the probability density function (PDF). For sparse observations, we show that this PDF can deviate significantly from the ellipsoidal form assumed in conventional methods. Furthermore, we show how combining arrival time and direction-of-arrival constraints—as can be measured by three-component polarization or array methods—can significantly improve the precision, and in some cases reduce bias, in location solutions. This article explores these issues using various types of synthetic and real data (including single-component seismic, three-component seismic, and infrasound).

A method for inversion of 1D vertical soundings of gravity anomalies

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. G15-G23
Author(s):  
Andrea Vitale ◽  
Domenico Di Massa ◽  
Maurizio Fedi ◽  
Giovanni Florio
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Finite Size ◽  
Gravity Anomalies ◽  
Real Data ◽  
Gravity Inversion ◽  
Chain Process ◽  
Potential Field Data ◽  
Gamma Density

We have developed a method to interpret potential fields, which obtains 1D models by inverting vertical soundings of potential field data. The vertical soundings are built through upward continuation of potential field data, measured on either a profile or a surface. The method assumes a forward problem consisting of a volume partitioned in layers, each of them homogeneous and horizontally finite, but with the density changing versus depth. The continuation errors, increasing with the altitude, are automatically handled by determining the coefficients of a third-order polynomial function of the altitude. Due to the finite size of the source volume, we need a priori information about the total horizontal extent of the volume, which is estimated by boundary analysis and optimized by a Markov chain process. For each sounding, a 1D inverse problem is independently solved by a nonnegative least-squares algorithm. Merging of the several inverted models finally yields approximate 2D or 3D models that are, however, shown to generate a good fit to the measured data. The method is applied to synthetic models, producing good results for either perfect or continued data. Even for real data, i.e., the gravity data of a sedimentary basin in Nevada, the results are interesting, and they are consistent with previous interpretation, based on 3D gravity inversion constrained by two gamma-gamma density logs.

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