scholarly journals FAVITES: simultaneous simulation of transmission networks, phylogenetic trees, and sequences

2018 ◽  
Author(s):  
Niema Moshiri ◽  
Manon Ragonnet-Cronin ◽  
Joel O. Wertheim ◽  
Siavash Mirarab
Keyword(s):  
Phylogenetic Trees ◽  
Simulated Data ◽  
Network Reconstruction ◽  
Generative Model ◽  
Model Parameters ◽  
Transmission Network ◽  
Transmission Networks ◽  
Link Type ◽  
Reconstruction Methods ◽  
The Impact

AbstractMotivationThe ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like HIV may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain “truth” sets on which to test them and properly measure their performance.ResultsWe introduce FAVITES, a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees, and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters.Availability and implementationFAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites).

scholarly journals FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences

2018 ◽  
Vol 35 (11) ◽  
pp. 1852-1861 ◽  
Author(s):  
Niema Moshiri ◽  
Manon Ragonnet-Cronin ◽  
Joel O Wertheim ◽  
Siavash Mirarab
Keyword(s):  
Phylogenetic Trees ◽  
Simulated Data ◽  
Network Reconstruction ◽  
Generative Model ◽  
Supplementary Information ◽  
Model Parameters ◽  
Transmission Network ◽  
Transmission Networks ◽  
Reconstruction Methods ◽  
The Impact

Abstract Motivation The ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like Human Immunodeficiency Virus (HIV) may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain ‘truth’ sets on which to test them and properly measure their performance. Results We introduce FrAmework for VIral Transmission and Evolution Simulation (FAVITES), a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters. Availability and implementation FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Estimating Epidemic Incidence and Prevalence from Genomic Data

2019 ◽  
Vol 36 (8) ◽  
pp. 1804-1816 ◽  
Author(s):  
Timothy G Vaughan ◽  
Gabriel E Leventhal ◽  
David A Rasmussen ◽  
Alexei J Drummond ◽  
David Welch ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Particle Filtering ◽  
Phylogenetic Trees ◽  
Stochastic Dynamics ◽  
Large Population ◽  
Simulated Data ◽  
Model Parameters ◽  
Epidemiological Models ◽  
Case Count ◽  
Birth Death

Abstract Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth–death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth–death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

scholarly journals Inferring person-to-person networks of pathogen transmission: is routine surveillance data up to the task?

2020 ◽  
Author(s):  
John H Huber ◽  
Michelle S Hsiang ◽  
Nomcebo Dlamini ◽  
Maxwell Murphy ◽  
Sibonakaliso Vilakati ◽  
...  
Keyword(s):  
Simulated Data ◽  
Pathogen Transmission ◽  
Spatiotemporal Patterns ◽  
Surveillance Data ◽  
Transmission Network ◽  
Data Types ◽  
Transmission Networks ◽  
Travel History ◽  
History Data ◽  
Routine Surveillance

Inference of person-to-person transmission networks using routinely collected surveillance data is being used increasingly to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated. We evaluated the influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum, the pathogen responsible for most human malaria. After developing a new inference framework and applying it to simulated data, we found that these data types have limited utility for inferring transmission networks and, in some combinations, tend to overestimate transmission. Only when outbreaks were highly focal in time or when travel histories were highly accurate was the inference algorithm able to accurately estimate the reproduction number under control, Rc, a key metric of transmission. Applying this approach to surveillance data from Eswatini indicated that inferences of Rc and spatiotemporal patterns therein are sensitive to the choice of data types and assumptions about the accuracy of travel-history data. Taken together, these results suggest that transmission network inferences made with routinely collected surveillance data should be interpreted with caution. As we have done here, future studies inferring transmission networks should apply their algorithm to data simulated under alternative assumptions to assess the robustness of their inferences.

scholarly journals Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets

2019 ◽  
Vol 52 (3) ◽  
pp. 397-423
Author(s):  
Luc Steinbuch ◽  
Thomas G. Orton ◽  
Dick J. Brus
Keyword(s):  
Prior Distribution ◽  
Crop Yields ◽  
Simulated Data ◽  
Monte Carlo Sampling ◽  
Model Parameters ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Minimal Impact ◽  
The Impact

AbstractArea-to-point kriging (ATPK) is a geostatistical method for creating high-resolution raster maps using data of the variable of interest with a much lower resolution. The data set of areal means is often considerably smaller ($$<\,50 $$<50 observations) than data sets conventionally dealt with in geostatistical analyses. In contemporary ATPK methods, uncertainty in the variogram parameters is not accounted for in the prediction; this issue can be overcome by applying ATPK in a Bayesian framework. Commonly in Bayesian statistics, posterior distributions of model parameters and posterior predictive distributions are approximated by Markov chain Monte Carlo sampling from the posterior, which can be computationally expensive. Therefore, a partly analytical solution is implemented in this paper, in order to (i) explore the impact of the prior distribution on predictions and prediction variances, (ii) investigate whether certain aspects of uncertainty can be disregarded, simplifying the necessary computations, and (iii) test the impact of various model misspecifications. Several approaches using simulated data, aggregated real-world point data, and a case study on aggregated crop yields in Burkina Faso are compared. The prior distribution is found to have minimal impact on the disaggregated predictions. In most cases with known short-range behaviour, an approach that disregards uncertainty in the variogram distance parameter gives a reasonable assessment of prediction uncertainty. However, some severe effects of model misspecification in terms of overly conservative or optimistic prediction uncertainties are found, highlighting the importance of model choice or integration into ATPK.

scholarly journals Interferometric Phase Reconstruction Based on Probability Generative Model: Toward Efficient Analysis of High-Dimensional SAR Stacks

2021 ◽  
Vol 13 (12) ◽  
pp. 2369
Author(s):  
Yunqi Wang ◽  
Kui Zhang ◽  
Faming Gong ◽  
Jinghan Mu ◽  
Shujun Liu
Keyword(s):  
Simulated Data ◽  
Generative Model ◽  
Eigenvalue Decomposition ◽  
High Dimensional ◽  
Current Phase ◽  
Phase Reconstruction ◽  
Reconstruction Process ◽  
Reconstruction Methods ◽  
Interferometric Phase ◽  
Multi Temporal

In order to minimize the influence of decorrelation noise on multi-temporal interferometric synthetic aperture radar (MT-InSAR) applications, a series of phase reconstruction methods have been proposed in recent years. Unfortunately, current phase reconstruction methods generally exhibit a low computational efficiency due to their high non-linearity, in particular in the case that the dimension of a SAR stack is high. In this paper, a new approach is proposed to efficiently resolve phase reconstruction problems. This approach is inspired by the theory of probabilistic principle component analysis. A complex valued probability generative model is constructed to portray a phase reconstruction process. Moreover, in order to resolve such a model, a targeted algorithm based on the idea of expectation maximization is designed and implemented. For validation purposes, the proposed approach is compared to the traditional eigenvalue decomposition-based method by using simulated data and 101 real Sentinel-1A SAR images. The experimental results demonstrate that the proposed method can accelerate the phase reconstruction process drastically, in particular when a high-dimensional SAR stack is required to be processed.

scholarly journals Estimating epidemic incidence and prevalence from genomic data

2017 ◽  
Author(s):  
Timothy G. Vaughan ◽  
Gabriel E. Leventhal ◽  
David A. Rasmussen ◽  
Alexei J. Drummond ◽  
David Welch ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Particle Filtering ◽  
Phylogenetic Trees ◽  
Stochastic Dynamics ◽  
Large Population ◽  
Simulated Data ◽  
Model Parameters ◽  
Epidemiological Models ◽  
Case Count ◽  
Birth Death

AbstractModern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death or behaviour change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models, and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

Analyzing the Impact of Healthy Behavior on Weight Change with a Provable Mathematical Model (Preprint)

2020 ◽  
Author(s):  
Ayan Chatterjee ◽  
Ram Bajpai ◽  
Pankaj Khatiwada
Keyword(s):  
Physical Activity ◽  
Mathematical Model ◽  
Health Behavior ◽  
Weight Change ◽  
Simulated Data ◽  
Negative Behavior ◽  
Healthy Behavior ◽  
Healthy Habits ◽  
Lifestyle Diseases ◽  
The Impact

BACKGROUND Lifestyle diseases are the primary cause of death worldwide. The gradual growth of negative behavior in humans due to physical inactivity, unhealthy habit, and improper nutrition expedites lifestyle diseases. In this study, we develop a mathematical model to analyze the impact of regular physical activity, healthy habits, and a proper diet on weight change, targeting obesity as a case study. Followed by, we design an algorithm for the verification of the proposed mathematical model with simulated data of artificial participants. OBJECTIVE This study intends to analyze the effect of healthy behavior (physical activity, healthy habits, and proper dietary pattern) on weight change with a proposed mathematical model and its verification with an algorithm where personalized habits are designed to change dynamically based on the rule. METHODS We developed a weight-change mathematical model as a function of activity, habit, and nutrition with the first law of thermodynamics, basal metabolic rate (BMR), total daily energy expenditure (TDEE), and body-mass-index (BMI) to establish a relationship between health behavior and weight change. Followed by, we verified the model with simulated data. RESULTS The proposed provable mathematical model showed a strong relationship between health behavior and weight change. We verified the mathematical model with the proposed algorithm using simulated data following the necessary constraints. The adoption of BMR and TDEE calculation following Harris-Benedict’s equation has increased the model's accuracy under defined settings. CONCLUSIONS This study helped us understand the impact of healthy behavior on obesity and overweight with numeric implications and the importance of adopting a healthy lifestyle abstaining from negative behavior change.

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