Trends and future prediction of livestock diseases outbreaks by periodic regression analysis

Abstract

Livestock disease outbreaks become a burden to the animal husbandry farmers and cause great economic loss in India. Period regression analysis is used to find the periodic or cyclic character of livestock disease outbreaks in animals, as many other natural phenomena in environment is periodic or cyclic in nature. In present study, livestock disease outbreaks of anthrax (AX), black quarter (BQ), enterotoxaemia (ET), haemorrahgic septicemia (HS), bluetongue (BT), foot-and-mouth disease (FMD), peste des petits ruminants (PPR), sheep and goat pox (SGP), babesiosis (BA), fasciolosis (FA), theileriosis (TH) and trypanosomosis (TR) were analyzed using periodic regression to know the trend and future prediction of outbreaks. Time series data on disease outbreaks, month and year was collected from National Animal Disease Referral Expert System database for 2001–2016. The regression curves were prepared with baseline, observed outbreaks and upper bound curves for 12 livestock diseases. The analysis revealed decreasing trend for AX, BQ, ET, HS, FMD, PPR, SGP and a cyclical trend of peak occurrence for every 4–5 years was observed in BQ, PPR, SGP, FA and TR. However, TR showed increasing trend and BT, BA, FA, TH outbreaks were maintained at the same trend in the past and future also. Further, BQ in 2026, ET in 2020, HS in 2022, FMD in 2023, outbreak numbers may touch the zero point, if the preventive measures are continued for these diseases effectively. Thus, continuous and constant efforts are needed for prevention of livestock diseases outbreaks from all stakeholders, which will improve the economy of farmers in India.