Developing a predictive model for an emerging epidemic on cassava in sub-Saharan Africa

Abstract The agricultural productivity of smallholder farmers in sub-Saharan Africa (SSA) is severely constrained by pests and pathogens, impacting economic stability and food security. Since 2004, an epidemic of cassava brown streak disease (CBSD) has been spreading rapidly from Uganda, with the disease causing necrosis of the edible root tissue. Based on sparse surveillance data, the epidemic front is currently believed to be at least as far west as central DRC and as far south as Zambia. The DRC is the world’s highest per capita consumer of cassava and future spread threatens production in West Africa which includes Nigeria, the world’s largest producer of cassava. Here, we take a unique Ugandan CBSD surveillance dataset spanning 2004 to 2017 and develop, parameterise, and validate a landscape-scale, spatiotemporal epidemic model of CBSD at a 1 km 2 resolution. While this paper focuses on Uganda, the model is designed to be readily extended to make predictions beyond Uganda for all 32 major cassava producing countries of SSA, laying the foundations for a tool capable of informing strategic policy decisions at a national and regional scale.