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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Autoregressive Models Applied to Time-Series Data in Veterinary Science.

Michael P Ward1, Rachel M Iglesias2, Victoria J Brookes3,4

  • 1Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW, Australia.

Frontiers in Veterinary Science
|October 23, 2020
PubMed
Summary

Veterinary epidemiology can use time-series analysis, like Autoregressive Integrated Moving Average (ARIMA) models, to study disease patterns. Applying ARIMA to canine parvovirus (CPV) data reveals its potential and identifies barriers to wider adoption in animal health surveillance.

Keywords:
animal diseasecanine parvovirusmethodstime-series analysisveterinary science

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Area of Science:

  • Veterinary Epidemiology
  • Time-Series Analysis
  • Biostatistics

Background:

  • Time-series data are crucial in veterinary epidemiology for monitoring disease occurrence and other animal health parameters.
  • Existing literature shows a focus on infectious diseases, with autocorrelation analysis being common, often using R software.

Purpose of the Study:

  • To scan the literature on time-series analysis methods in veterinary epidemiology.
  • To illustrate the application of Autoregressive Integrated Moving Average (ARIMA) models for analyzing autocorrelation in disease data.
  • To identify barriers and propose solutions for implementing ARIMA in veterinary epidemiology.

Main Methods:

  • Literature scan of peer-reviewed publications on time-series analysis in veterinary epidemiology.
  • Application of ARIMA models to a time-series dataset of canine parvovirus (CPV) events in Australia (2009-2015).
  • Utilized R statistical software for data analysis and prediction, including rainfall as a covariate.

Main Results:

  • The literature scan identified 37 studies, predominantly analyzing infectious diseases for analytical purposes.
  • ARIMA methods were illustrated using CPV data, demonstrating autocorrelation analysis and prediction with covariates.
  • Time-series analysis using ARIMA is relatively uncommon in veterinary epidemiology.

Conclusions:

  • Limited data availability, unfamiliarity with methods/software, and challenges in interpreting results are barriers to ARIMA adoption.
  • Making time-series data accessible for analysis and methods development is recommended.
  • ARIMA offers a valuable approach for understanding disease dynamics and autocorrelation in veterinary epidemiology.