Sri Lankan cassava mosaic virus was first detected in Southeast Asia in 2015. Surveillance and a survey of cassava seed networks in Vietnam and Cambodia were conducted. This resulted in two publications integrating this information to model the combined effects on risk of virus spread. Impact network analysis was used to combine environmental risk factors, seed network data, and surveillance data to generate risk maps. This identifies key areas for disease surveillance, predicts likely spread pathways, and identifies areas for clean seed interventions. The model can incorporate updated data to recalibrate predictions in real time.

1. Impact networks - cassava mosaic virus in Southeast Asia
ERIK DELAQUIS • CC2.1 ANNUAL MEETING – IBADAN, NIGERIA
Regional risk model Regional risk model +
landcover
Risk model + network
simulations
Sri Lankan cassava mosaic virus (SLCMV) was first detected in Southeast Asia in 2015 (Wang et al.,
2016). Following this, CIAT et al. conducted surveillance for the disease and a survey-based analysis of
cassava seed use and exchange networks in Vietnam and Cambodia at national scale (419 fields, 6480
samples).
This resulted in 2 recent RTB publications (Delaquis et al., 2018; Minato et al., 2019).
To integrate this information and to model the combined effects on risk for SLCMV spread, impact
network analysis was conducted to combine environmental risk factors for SLCMV, seed network data,
and surveillance data for SLCMV.
References:
Wang HL, Cui XY, Wang XW, Liu SS, Zhang ZH, Zhou XP. First report of Sri Lankan cassava mosaic virus infecting cassava in Cambodia. Plant Dis. 2016; 100:
1029–1029.
Delaquis E, Andersen KF, Minato N, Cu TTL, Karssenberg ME, Sok S, et al. Raising the stakes: cassava seed networks at multiple scales in Cambodia and
Vietnam. Front. Sustain. Food Syst. 2018; 2:73.
Minato N, Sok S, Chen S, Delaquis E, Phirun I, Le VX, et al. (2019) Surveillance for Sri Lankan cassava mosaic virus (SLCMV) in Cambodia and Vietnam one year
after its initial detection in a single plantation in 2015. PLoS ONE 14(2): e0212780.
All additional published
SLCMV observations
Seed network data
Machine learning – extraction
of 19 bioclimatic variables
Regional cassava
area spatial data
Impact network analysis is an integrative, iterative analysis to generate risk maps based on the
combination of environmental characteristics, cassava area spatial data, and seed exchange networks.
These results can identify key areas for disease surveillance, predict likely disease spread pathways, and
identify key areas for clean seed interventions. The model readily recalibrates predictions as updated
data becomes available, making it a useful tool for planning and responding in real time.