Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) threatens maize, sorghum, and millet production in Africa. Despite rigorous work done to reduce FAW prevalence, the dynamics and invasion mechanisms are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset to provide insights and projections on the intensity of FAW infestation across Africa. The data collected between January 2018 and December 2020 were matched with the monthly average data of the climatic and environmental variables. The multilevel analytics identified the key factors that influence the dynamics of spatial and temporal pest density and occurrence at a 2 km x 2 km grid resolution. The seasonal variations of the identified factors and dynamics were used to calibrate rule-based analytics employed to simulate the monthly densities and occurrence of the FAW for the years 2018, 2019, and 2020. Three FAW density level classes were inferred, i.e., low (0–10), moderate (11–30), and high (>30). Results show that monthly density projections were sensitive to the type of FAW host vegetation and the seasonal variability of climatic factors. Moreover, the diversity in the climate patterns and cropping systems across the African sub-regions are considered the main drivers of FAW abundance and variation. An optimum overall accuracy of 53% was obtained across the three years and at a continental scale, however, a gradual increase in prediction accuracy was observed among the years, with 2020 predictions providing accuracies greater than 70%. Apart from the low amount of data in 2018 and 2019, the average level of accuracy obtained could also be explained by the non-inclusion of data related to certain key factors such as the influence of natural enemies into the analysis. Further detailed data on the occurrence and efficiency of FAW natural enemies in the region may help to complete the tri-trophic

Following the invasion of Africa by the oriental fruit fly, Bactrocera dorsalis, Classical biological control (CBC) have been exploited as a safer alternative for its suppression by the introduction and release of the koinobiont endoparasitoid, Fopius arisanus. Although, the parasitoids have been released in several African countries, its extent of dispersal resulting in numbers of beneficiaries fruit growers has not yet been elucidated. This paper proposes an innovative multi-level CBC impact analysis combining cellular automata (CA) and ecological niche models to estimate parasitoid dispersal ranges and household beneficiary populations. Firstly, we provide a generic systematic methodological approach using CA rules incorporated into species distribution. Secondly, the model was used to estimate the dispersal range of the parasitoid based on the life history and bioecology of the host insect (fruit fly) and the parasitoid. Finally, the parasitoid dispersal coverage was mapped across fruit crops attacked by the target fruit fly, and the number of households that have benefitted from the parasitoids release programme was extracted from the area of the dispersal (first in Kenya), and the data was projected across all countries where the parasitoid have been released and validated. In Kenya, the model showed that F. arisanus had covered a total area of 50.34 km2 from the initial point of open field release; and at the continental scale, the model predicted that the parasitoid had covered a total area of 229.97 km2. The model estimated that 351,855 and 3,731,330 households have directly benefited from the release of F. arisanus between 2013 to 2018 in Kenya and at the continental level, respectively. The study’s outcome is appropriate for providing feedback information on the impact of CBC to government and development partners to make informed decisions on technological interventions.