AUTHOR=Ward Michael P. , Iglesias Rachel M. , Brookes Victoria J. 

TITLE=Autoregressive Models Applied to Time-Series Data in Veterinary Science

JOURNAL=Frontiers in Veterinary Science

VOLUME=Volume 7 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2020.00604

DOI=10.3389/fvets.2020.00604

ISSN=2297-1769

ABSTRACT=A time-series is any set of N time-ordered observations of a process. In veterinary epidemiology, our focus is generally on disease occurrence (the “process”) over time, but animal production, welfare or other traits might also be of interest. A common source of time-series datasets are animal disease monitoring and surveillance systems. Here, we scan the application of methods to analyse time-series data in the peer-reviewed, published literature. Based on this literature scan we focus on autocorrelation and illustrate the recommended steps using ARIMA (Autoregressive Integrated Moving Average Models) methods via analysis of a time-series of canine parvovirus (CPV) events in a pet dog population in Australia, 2009 to 2015. We conclude by identifying the barriers to the application of ARIMA methods in veterinary epidemiology and suggest some possible solutions.

Sixty articles were identified in the literature scan, of which 23 were unavailable or out-of-scope. The remaining 37 studies focused on a wide range of events, but mostly on infectious and parasitic diseases. The purpose of the analysis performed in the articles identified was predominantly analytical (18), followed by descriptive (12) and then predictive (7). Trends and seasonality were investigated in most studies (27 and 28 studies, respectively), and autocorrelation was analysed in most (23 of 37) studies. The most common (18) software used was R.

An approach to analyzing autocorrelation using ARIMA methods was then illustrated using a time-series (week and month units) of CPV events in a pet dog population in Australia, reported to a national companion animal disease surveillance system. We present data analysis output generated via the R statistical environment, and make this code available for the reader to apply to this or other time-series datasets.

Time-series analysis using ARIMA methods to understand and explore autocorrelation appears to be relatively uncommon in veterinary epidemiology. Some of the reasons might include limited availability of data of sufficient time unit length, lack of familiarity with analytical methods and available software, and how to best use the information generated. We recommend that wherever feasible, such time-series data be made available both for analysis and for methods development.