Spatiotemporal analysis and forecasting of lumpy skin disease outbreaks in Ethiopia based on retrospective outbreak...

Lumpy skin disease is a viral disease that affects cattle belonging to genus Capripoxvirus (Poxviridae) and lead to significant economic losses.

The objective of this study was to evaluate the distribution of lumpy skin disease (LSD) outbreaks and predict future patterns based on retrospective outbreak reports in Ethiopia.

Data were collected through direct communication with regional laboratories and a hierarchical reporting system from the Peasant Associations to Ministry of Agriculture. Time-series data for the LSD outbreaks were analyzed using classical additive time-series decomposition and STL decomposition. Four models (ARIMA, SARIMA, ETS, STLF) were also used to forecast the number of LSD outbreaks that occurred each month for the years (2021-2025) after the models' accuracy test was performed. Additionally, the space-time permutation model (STP) were also used to study retrospective space-time cluster analysis of LSD outbreaks in Ethiopia.

This study examined the geographical and temporal distribution of LSD outbreaks in Ethiopia from 2008 to 2020, reporting a total of 3,256 LSD outbreaks, 14,754 LSD-positive cases, 7,758 deaths, and 289 slaughters. It also covered approximately 68% of Ethiopia's districts, with Oromia reporting the highest LSD outbreaks. In the LSD's temporal distribution, the highest peak was reported following the rainy season in September to December and its lowest peak in the dry months of April and May. Out of the four models tested for forecasting, the SARIMA (3, 0, 0) (2, 1, 0) [12] model performed well for the validation data, while the STLF+Random Walk had a robust prediction for the training data. Thus, the SARIMA and STLF+Random Walk models produced a more accurate forecast of LSD outbreaks between 2020 and 2025. From retrospective Space-Time Cluster Analysis of LSD, eight possible clusters were also identified, with five of them located in central part of Ethiopia.

The study's time series and ST-cluster analysis of LSD outbreak data provide valuable insights into the spatial and temporal dynamics of the disease in Ethiopia. These insights can aid in the development of effective strategies to control and prevent the spread of the disease and holds great potential for improving efforts to combat LSD in the country.