Abstract:
Background: Lumpy skin disease (LSD) is an infectious viral disease of cattle caused by a virus of the genus Capripoxvirus.
LSD was reported for the first time in Ethiopia in 1981 and subsequently became endemic. This time series study was
undertaken with the aims of identifying the spatial and temporal distribution of LSD outbreaks and to forecast the future
pattern of LSD outbreaks in Ethiopia.
Results: A total of 3811 LSD outbreaks were reported in Ethiopia between 2000 and 2015. In this period, LSD was reported
at least once in 82% of the districts (n = 683), 88% of the administrative zones (n = 77), and all of the regional states or city
administrations (n = 9 and n = 2) in the country. The average incidence of LSD outbreaks at district level was 5.58 per 16
years (0.35 year−1). The incidence differed between areas, being the lowest in hot dry lowlands and highest in warm moist
highland. The occurrence of LSD outbreaks was found to be seasonal. LSD outbreaks generally have a peak in October and
a low in May. The trend of LSD outbreaks indicates a slight, but statistically significant increase over the study period. The
monthly precipitation pattern is the reverse of LSD outbreak pattern and they are negatively but non-significantly correlated
at lag 0 (r = −0.05, p = 0.49, Spearman rank correlation) but the correlation becomes positive and significant when the series
are lagged by 1 to 6 months, being the highest at lag 3 (r = 0.55, p < 0.001). The forecast for the period 2016–2018 revealed
that the highest number of LSD outbreaks will occur in October for all the 3 years and the lowest in April for the year 2016
and in May for 2017 and 2018.
Conclusion: LSD occurred in all major parts of the country. Outbreaks were high at the end of the long rainy season.
Understanding temporal and spatial patterns of LSD and forecasting future occurrences are useful for indicating periods
when particular attention should be paid to prevent and control the disease.
Keywords: Ethiopia, Lumpy skin disease, Time series, Spatial, Temporal, Forecast
