Predicting the cross-continental spread of the cassava brown streak disease epidemic in sub-Saharan Africa
Godding, D.; Stutt, R. O. J. H.; Savi, M. K.; Ahanhanzo, C.; Tiendrebeogo, F.; Doungous, O.; Godefroid, M.; Bakelana, Z.; Mavoungou, J. F.; Oppong, A.; Ateka, E.; Mbewe, W.; Cossa, N.; Nkere, C.; Mohammed, I. U.; Kanyange, M. C.; Samura, A. E.; Djodji, A.; Alicai, T.; Chikoti, P. C.; Tairo, F.; Sseruwagi, P.; Ndunguru, J.; Eni, A. O.; Pita, J. S.; Gilligan, C. A.
Show abstract
Cassava brown streak disease (CBSD) is a major threat to smallholder farmers in sub-Saharan Africa (SSA), where cassava is a staple crop. Caused by cassava brown streak ipomoviruses (CBSIs), CBSD has spread extensively since its detection in Uganda in 2004, raising concerns about ongoing spread through Southern and Central Africa and potential expansion to West Africa, home to the worlds largest cassava producer, Nigeria. Building on a stochastic epidemiological model that predicts CBSD spread at the scale of Uganda, we incorporate extensive field surveillance records to extend the model to all thirty-two major cassava-producing countries in SSA. We then deploy the model to address key strategic questions such as estimating the present day CBSD distribution and predicting rates of ongoing spread towards West Africa. We also evaluate the risk of direct introductions resulting from long range movement of infected planting material, which could trigger outbreaks beyond predicted localised dispersal limits. Our model predicts the likely arrival of CBSD in Nigeria via cross-continental spread within 25 years, and if directly introduced anywhere in West Africa, spreading to most West African nations within 10 years. The risks of ongoing and future CBSD spread highlighted in this study underscore the need for proactive phytosanitation measures, including clean seed programs, vector control, and quarantine policies to curb CBSD spread. Moreover, the model described in this study not only provides estimates for arrival times across SSA, but also lays the foundations for a continental-scale quantitative framework wherein both surveillance and management options can be explored and optimised.
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