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. An epidemic of cassava brown streak disease, causing significant yield loss, is spreading rapidly from Uganda into surrounding countries. Based on sparse surveillance data, the epidemic front is reported to be as far west as central DRC, the world’s highest per capita consumer, and as far south as Zambia. Future spread threatens production in West Africa including Nigeria, the world’s largest producer of cassava. Using innovative methods we develop, parameterise and validate a landscape-scale, stochastic epidemic model capturing the spread of the disease throughout Uganda. The model incorporates real-world management interventions and can be readily extended to make predictions for all 32 major cassava producing countries of SSA, with relevant data, and lays the foundations for a tool capable of informing policy decisions at a national and regional scale.

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

“…The effects described were only demonstrated on small, grid-shaped systems. In realistic, plant focused systems, the subpopulations would often be formed from a raster grid over a landscape (Cunniffe et al 2016, Mastin et al 2020, Godding et al 2023, Nguyen et al 2023, Meentemeyer et al 2011, Harwood et al 2011) and so this can be viewed as a minimal version suitable for easy experimentation with MPC. In real use cases, iterations on the control strategy might occur every few weeks at most and so the runtimes of MPC iterations on larger systems could be acceptable.…”