scholarly journals Conditional Probabilities of AIDS Disease Transitions Using Semi-Markov Models

2019 ◽  
pp. 1-9
Author(s):  
Tilahun Ferede Asena ◽  
Ayele Taye Goshu
Keyword(s):  
Markov Models ◽  
Transition Probability ◽  
Clinical Care ◽  
Disease Status ◽  
Clinic Visit ◽  
Good State ◽  
Partially Observed ◽  
Patient Will ◽  
Time Of Infection ◽  
Over Time

Analyzing progression of diseases is vital to monitor patient's traversal over time through a disease. Clinical study settings present modeling challenges, as patients' disease trajectories are only partially observed, and patients' disease statuses are only assessed at clinic visit times. HIV disease is a continuum of progressive damage to the immune system from the time of infection to the manifestation of severe immunologic damage. We proposed a semi-Markov model and collected data at Yirgalem General Hospital. Our study found that for an HIV/AIDS patient the transition probability from a given state to the next worse state increases within the good states as time gets optimum and then decreases with increasing time during a follow up. In a specific state of the disease a patient will stay in that state with a non- zero probability in good states and a patient will transit to the next state either to the worst or to the good state with a non-zero probability. The probability of being in same state decreases over time.  With the good or alive states, the probability of being in a better state is non-zero, but less than the probability of being in worst states.  The survival probabilities are decreasing with increasing time. Therefore, we recommend that increased clinical care for patients on ART services should be strengthen and patients need to regularly check their CD4 T cell count in the appropriate day based on physician order to timely know and monitor their disease status to improve the survival probability and to reduce mortality.

scholarly journals Comparison of Sojourn Time Distributions in Modeling HIV/AIDS Disease Progression

2017 ◽  
Vol 54 (2) ◽  
pp. 155-174 ◽  
Author(s):  
Tilahun Ferede Asena ◽  
Ayele Taye Goshu
Keyword(s):  
Weibull Distribution ◽  
Disease Progression ◽  
Sojourn Time ◽  
Markov Models ◽  
Transition Probability ◽  
Good State ◽  
Rate Of Decline ◽  
Sojourn Time Distributions ◽  
Over Time ◽  
Hiv Aids

Summary An application of semi-Markov models to AIDS disease progression was utilized to find best sojourn time distributions. We obtained data on 370 HIV/AIDS patients who were under follow-up from September 2008 to August 2015, from Yirgalim General Hospital, Ethiopia. The study reveals that within the “good” states, the transition probability of moving from a given state to the next worst state has a parabolic pattern that increases with time until it reaches a maximum and then declines over time. Compared with the case of exponential distribution, the conditional probability of remaining in a good state before moving to the next good state grows faster at the beginning, peaks, and then declines faster for a long period. The probability of remaining in the same good disease state declines over time, though maintaining higher values for healthier states. Moreover, the Weibull distribution under the semi-Markov model leads to dynamic probabilities with a higher rate of decline and smaller deviations. In this study, we found that the Weibull distribution is flexible in modeling and preferable for use as a waiting time distribution for monitoring HIV/AIDS disease progression.

scholarly journals Estimation of Malaria Symptom Data Set using Hidden Markov Model

2019 ◽  
pp. 1-29
Author(s):  
Drinold Mbete ◽  
Kennedy Nyongesa ◽  
Joseph Rotich
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Disease Status ◽  
Clinic Visit ◽  
Reliable Prediction ◽  
Data Set ◽  
Susceptibility To Infection ◽  
Partially Observed ◽  
Intense Research

Clinical study of malaria presents a modeling challenge as patients disease status and progress is partially observed and assessed at discrete clinic visit times. Since patients initiate visits based on symptoms, intense research has focused on identication of reliable prediction for exposure, susceptibility to infection and development of severe malaria complications. Despite detailed literature on malaria infection and transmission, very little has been documented in the existing literature on malaria symptoms modeling, yet these symptoms are common. Furthermore, imperfect diagnostic tests may yield misclassication of observed symptoms. Place and Duration of Study: The main objective of this study is to develop a Bayesian Hidden Markov Model of Malaria symptoms in Masinde Muliro University of Science and Technology student population. An expression of Hidden Markov Model is developed and the parameters estimated through the forward-backward algorithm.

scholarly journals Acute symptoms of mild to moderate COVID-19 are highly heterogeneous across individuals and over time

2021 ◽  
Author(s):  
Thomas L Rodebaugh ◽  
Madelyn R Frumkin ◽  
Angela M Reiersen ◽  
Eric J Lenze ◽  
Michael S Avidan ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Survey Methods ◽  
Clinical Care ◽  
Clinical Deterioration ◽  
Symptom Profiles ◽  
Fluctuating Course ◽  
The Common ◽  
Symptom Changes ◽  
Over Time

Abstract Background The symptoms of COVID-19 appear to be heterogenous, and the typical course of these symptoms is unknown. Our objectives were to characterize the common trajectories of COVID-19 symptoms and assess how symptom course predicts other symptom changes as well as clinical deterioration. Methods 162 participants with acute COVID-19 responded to surveys up to 31 times for up to 17 days. Several statistical methods were used to characterize the temporal dynamics of these symptoms. Because nine participants showed clinical deterioration, we explored whether these participants showed any differences in symptom profiles. Results Trajectories varied greatly between individuals, with many having persistently severe symptoms or developing new symptoms several days after being diagnosed. A typical trajectory was for a symptom to improve at a decremental rate, with most symptoms still persisting to some degree at the end of the reporting period. The pattern of symptoms over time suggested a fluctuating course for many patients. Participants who showed clinical deterioration were more likely to present with higher reports of severity of cough and diarrhea. Conclusion The course of symptoms during the initial weeks of COVID-19 is highly heterogeneous and is neither predictable nor easily characterized using typical survey methods. This has implications for clinical care and early-treatment clinical trials. Additional research is needed to determine whether the decelerating improvement pattern seen in our data is related to the phenomenon of patients reporting long-term symptoms, and whether higher symptoms of diarrhea in early illness presages deterioration.

scholarly journals Markov-Modulated Continuous-Time Markov Chains to Identify Site- and Branch-Specific Evolutionary Variation in BEAST

2020 ◽  
Vol 70 (1) ◽  
pp. 181-189
Author(s):  
Guy Baele ◽  
Mandev S Gill ◽  
Paul Bastide ◽  
Philippe Lemey ◽  
Marc A Suchard
Keyword(s):  
High Performance ◽  
Markov Models ◽  
Marginal Likelihood ◽  
Likelihood Estimation ◽  
Phylogenetic Inference ◽  
Time Variability ◽  
Substitution Process ◽  
Wide Range ◽  
Markov Modulated ◽  
Over Time

Abstract Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference. [Bayesian inference; BEAGLE; BEAST; covarion, heterotachy; Markov-modulated models; phylogenetics.]

scholarly journals Coupling Disease-Progress-Curve and Time-of-Infection Functions for Predicting Yield Loss of Crops

2000 ◽  
Vol 90 (8) ◽  
pp. 788-800 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes ◽  
M. E. Irwin
Keyword(s):  
Yield Loss ◽  
Numerical Solutions ◽  
Disease Incidence ◽  
Time Interval ◽  
Short Time Interval ◽  
Time Duration ◽  
Plant Yield ◽  
Disease Progress ◽  
Time Of Infection ◽  
Over Time

A general approach was developed to predict the yield loss of crops in relation to infection by systemic diseases. The approach was based on two premises: (i) disease incidence in a population of plants over time can be described by a nonlinear disease progress model, such as the logistic or monomolecular; and (ii) yield of a plant is a function of time of infection (t) that can be represented by the (negative) exponential or similar model (ζ(t)). Yield loss of a population of plants on a proportional scale (L) can be written as the product of the proportion of the plant population newly infected during a very short time interval (X′(t)dt) and ζ(t), integrated over the time duration of the epidemic. L in the model can be expressed in relation to directly interpretable parameters: maximum per-plant yield loss (α, typically occurring at t = 0); the decline in per-plant loss as time of infection is delayed (γ; units of time-1); and the parameters that characterize disease progress over time, namely, initial disease incidence (X0), rate of disease increase (r; units of time-1), and maximum (or asymptotic) value of disease incidence (K). Based on the model formulation, L ranges from αX0 to αK and increases with increasing X0, r, K, α, and γ-1. The exact effects of these parameters on L were determined with numerical solutions of the model. The model was expanded to predict L when there was spatial heterogeneity in disease incidence among sites within a field and when maximum per-plant yield loss occurred at a time other than the beginning of the epidemic (t > 0). However, the latter two situations had a major impact on L only at high values of r. The modeling approach was demonstrated by analyzing data on soybean yield loss in relation to infection by Soybean mosaic virus, a member of the genus Potyvirus. Based on model solutions, strategies to reduce or minimize yield losses from a given disease can be evaluated.

scholarly journals On the evolution of corruption patterns in the post-communist countries

Equilibrium ◽  
2015 ◽  
Vol 10 (1) ◽  
pp. 33 ◽  
Author(s):  
Andrzej Cieślik ◽  
Łukasz Goczek
Keyword(s):  
Transition Probability ◽  
Transition Probability Matrix ◽  
Markov Transition Probability ◽  
Long Run ◽  
Markov Transition ◽  
Corruption Index ◽  
Communist Countries ◽  
Control Of Corruption ◽  
Over Time

In this paper, we study the evolution of corruption patterns in 27 post-communist countries during the period 1996-2012 using the Control of Corruption Index and the corruption category Markov transition probability matrix. This method allows us to generate the long-run distribution of corruption among the post-communist countries. Our empirical findings suggest that corruption in the post-communist countries is a very persistent phenomenon that does not change much over time. Several theoretical explanations for such a result are provided.

Literacy by Way of Automatic Speech Recognition

2011 ◽  
pp. 2074-2118
Author(s):  
Russell Gluck ◽  
John Fulcher
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Warping ◽  
Oral Storytelling ◽  
Pattern Recognition Techniques ◽  
Dynamic Time ◽  
Over Time

The chapter commences with an overview of automatic speech recognition (ASR), which covers not only the de facto standard approach of hidden Markov models (HMMs), but also the tried-and-proven techniques of dynamic time warping and artificial neural networks (ANNs). The coverage then switches to Gluck’s (2004) draw-talk-write (DTW) process, developed over the past two decades to assist non-text literate people become gradually literate over time through telling and/or drawing their own stories. DTW has proved especially effective with “illiterate” people from strong oral, storytelling traditions. The chapter concludes by relating attempts to date in automating the DTW process using ANN-based pattern recognition techniques on an Apple Macintosh G4™ platform.

P1612CAN THE URINARY NITRATE TO CREATININE RATIO BE USED AS A MARKER FOR KIDNEY TRANSPLANT REJECTION?

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Amy Riddell ◽  
John Kirkwood ◽  
Miranda Smallwood ◽  
Paul Winyard ◽  
Bridget Knight ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Renal Transplant ◽  
Kidney Transplant ◽  
Nitrate Concentration ◽  
Clinical Care ◽  
Transplant Rejection ◽  
Creatinine Ratio ◽  
Unequal Variance ◽  
Renal Transplant Rejection ◽  
Over Time

Abstract Background and Aims Early identification and treatment of kidney transplant rejection episodes is vital to limit loss of function and prolong the life of the transplanted kidney and recipient. Current practice depends on detecting a creatinine rise. A biomarker to diagnose transplant rejection, either at an earlier time point, or to inform earlier decision making to biopsy, could be transformative. Urinary nitrate concentration is elevated in renal transplant rejection. Nitrate is a nitric oxide (NO) oxidation product. Transplant rejection upregulates NO synthesis via inducible nitric oxide synthase leading to elevations in urinary nitrate concentration. Historically, assays for measurement of inorganic nitrate in biological fluids have been too time consuming to be useful in a clinical setting. We have recently validated a urinary nitrate concentration assay which could provide results in a clinically relevant timeframe. Our aim was to determine whether urinary nitrate concentration is a useful tool to predict renal transplant rejection in the context of contemporary clinical practice. Method We conducted a prospective observational study, recruiting renal transplant participants over an 18 month period. We made no alterations to the patients’ clinical care including medications, immunosuppression, diet and frequency of visits. We collected urine samples from every clinical attendance including routine attendances, unscheduled attendances for acute clinical indications, and on the day of attendance for biopsy, for those who underwent biopsy. We measured the urinary nitrate to creatinine ratio (uNCR) between patient groups including routine attendances, biopsy proven rejection and biopsy proven “no rejection”. uNCR was examined over time for those with transplant rejection. Groups were compared using a 2 tail t-test of unequal variance, for statistical significance. Results A total of 2656 samples were collected. uNCR during biopsy proven rejection, median 49 µmol/mmol, IQR 23-61, was not significantly different from that of routine samples, median 55 µmol/mmol, IQR 37-82 (p=0.56), or biopsy proven “no rejection”, median 39 µmol/mmol, IQR 21-89, (P=0.77). Overall uNCR was highly variable; median 52 µmol/mmol, IQR 31-81, with no diagnostic threshold for kidney transplant rejection. Furthermore, within-patient uNCR was highly variable over time, and thus it was not possible to produce individualised patient thresholds to identify rejection. Conclusion The urinary nitrate to creatinine ratio is not a useful biomarker for renal transplant rejection.

scholarly journals Longitudinal Changes in Resting Metabolic Rates with Aging Are Accelerated by Diseases

Nutrients ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 3061
Author(s):  
Marta Zampino ◽  
Majd AlGhatrif ◽  
Pei-Lun Kuo ◽  
Eleanor Marie Simonsick ◽  
Luigi Ferrucci
Keyword(s):  
Chronic Diseases ◽  
Resting Metabolic Rate ◽  
Disease Status ◽  
Linear Regression Models ◽  
Two Phase ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Age Related ◽  
Baltimore Longitudinal Study ◽  
Over Time

Resting metabolic rate (RMR) declines with aging and is related to changes in health status, but how specific health impairments impact basal metabolism over time has been largely unexplored. We analyzed the association of RMR with 15 common age-related chronic diseases for up to 13 years of follow-up in a population of 997 participants to the Baltimore Longitudinal Study of Aging. At each visit, participants underwent measurements of RMR by indirect calorimetry and body composition by DEXA. Linear regression models and linear mixed effect models were used to test cross-sectional and longitudinal associations of RMR and changes in disease status. Cancer and diabetes were associated with higher RMR at baseline. Independent of covariates, prevalent COPD and cancer, as well as incident diabetes, heart failure, and CKD were associated with a steeper decline in RMR over time. Chronic diseases seem to have a two-phase association with RMR. Initially, RMR may increase because of the high cost of resiliency homeostatic mechanisms. However, as the reserve capacity becomes exhausted, a catabolic cascade becomes unavoidable, resulting in loss of total and metabolically active mass and consequent RMR decline.

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