Modeling and analysis of a time series of equine infectious anemia cases in the state of Tocantins, Brazil, between 2007 and 2019

Abstract

Equine infectious anemia (EIA) is a viral infectious disease that affects Equidae and is clinically characterized by intermittent fever, anemia, depression, emaciation, and edema. To evaluate disease dynamics in the state of Tocantins, Brazil, a time series of EIA cases in the period 2007–2019 was analyzed to describe the pattern of occurrence and define the autoregressive integrated by moving average (ARIMA) model best suited to make predictions of cases of this disease for the period 2020–2021. The modeling and statistical analysis of the time series were performed using R software. The ARIMA model (2,1,1) was evaluated by Holdout cross-validation, in which data from the periods 2007–2017 and 2018–2019 were used as training and test data, respectively. The analyses showed that EIA was endemic and non-seasonal in Tocantins. The ARIMA model (2,1,1) showed good predictive capacity adjusted for this time series. However, the prediction of 276 cases of EIA in Tocantins for the period 2020–2021 may vary depending on the demand for diagnostic tests for Equidae transportation and herd sanitation in farms considered infection foci. The ARIMA model helps predict the number of EIA cases in Tocantins and improves planning for disease control by the Official Veterinary Service.