Forecasting and Intervention Time Series Analysis Using Autoregressive Integrated Moving Average (ARIMA) Models: Evaluating the impact of 2018 and 2021 Rift Valley Fever Outbreaks on Kenyan food Price Index

The Rift Valley fever (RVF) disease, a climate-sensitive zoonosis, causes 100% abortions and death in infected animals. This shock has an immediate impact on food prices, particularly for animal-sourced foods. This study used an Interrupted Time Series (ITS) approach and an Autoregressive Integrated Moving Average (ARIMA) model to assess the effects of economic disruptions, specifically the RVF outbreak on Kenya's food price index during two consecutive RVF outbreaks in 2018 and 2021. Data from several Kenyan cities, including Nairobi, Kisumu, Eldoret, and Mombasa, were analyzed to identify inflation trends across different markets. The findings show significant price index fluctuations, with inflation escalating following critical intervention periods, particularly during the outbreak. The ARIMA model successfully identified these changes, highlighting the distinct effects across all regions, with some areas exhibiting significant forecasting inaccuracies. This analysis generates new knowledge, provides critical insights into market dynamics, and presents a predictive framework for dealing with future economic disruptions in Kenya and elsewhere. Policymakers can use these findings to create targeted strategies for stabilizing food prices and ensuring economic resilience.