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. In the literature scan the selected 37 studies focused mostly on infectious and parasitic diseases, predominantly for analytical, rather than descriptive or predictive, purposes. Trends and seasonality were investigated, and autocorrelation analyzed, in most studies, most commonly using R software. 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. This time-series was derived by summing veterinarian reports of confirmed CPV diagnoses. 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. We also illustrate prediction of CPV events by rainfall as a covariate. 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.

时间序列是任何一组 ñ对过程的时间顺序观察。在兽医流行病学中，我们通常将重点放在一段时间内的疾病发生（“过程”）上，但是动物生产，福利或其他特征也可能引起人们的兴趣。时间序列数据集的常见来源是动物疾病监测和监视系统。在这里，我们将扫描方法的应用，以分析经过同行评审的已发表文献中的时间序列数据。基于此文献资料，我们重点研究自相关性，并通过分析2009年至2015年澳大利亚宠物狗种群中细小病毒（CPV）事件的时间序列，使用ARIMA（自回归综合移动平均模型）方法来说明建议的步骤。我们通过确定在动物流行病学中应用ARIMA方法的障碍来得出结论，并提出一些可能的解决方案。在文献扫描中，选定的37项研究主要针对传染病和寄生虫病，主要用于分析目的，而不是描述性或预测性目的。在大多数研究中，最常使用R软件对趋势和季节性进行了调查，并对自相关进行了分析。然后使用澳大利亚国家宠物动物监测系统报告的澳大利亚宠物狗种群中CPV事件的时间序列（周和月单位），说明了一种使用ARIMA方法分析自相关的方法。该时间序列是通过汇总已确诊的CPV诊断的兽医报告得出的。我们展示了通过R统计环境生成的数据分析输出，并使该代码可供读者应用到此或其他时间序列数据集。我们还说明了通过降雨作为协变量对CPV事件的预测。在兽医流行病学中，使用ARIMA方法进行时间序列分析以了解和探索自相关似乎相对罕见。一些原因可能包括时间单位长度足够的数据的可用性有限，对分析方法和可用软件的不熟悉以及如何最好地使用生成的信息。我们建议在可行的情况下，应将此类时间序列数据同时用于分析和方法开发。缺乏对分析方法和可用软件的了解，以及如何最好地利用所生成的信息。我们建议在可行的情况下，应将此类时间序列数据同时用于分析和方法开发。缺乏对分析方法和可用软件的了解，以及如何最好地利用所生成的信息。我们建议在可行的情况下，应将此类时间序列数据同时用于分析和方法开发。