Title S. M. Niaz Arifin Department of Computer Science and Engineering, University of Notre Dame, IN 46556, USA

A GIS-equipped, spatial agent-based model of Anopheles gambiae for malaria

•  Agent-based modeling & simulation (ABMS): an agent-based model (ABM) of Anopheles gambiae mosquitoes for malaria •  Vector dynamics lifecycle of An. gambiae •  Mosquito agents: adult and aquatic (eggs, larvae, and pupae) •  Each mosquito agent is represented individually •  Temperature-dependent development rates for aquatic stages •  Age-specific mortality rates for adults and larvae •  Density-dependent oviposition for Gravid females •  Resources for adult mosquitoes: bloodmeal locations (houses) and aquatic habitats •  A non-spatial, point model: no spatial representation •  Agents do not have spatial locations: they do not move

Figure  1:  Mosquito  lifecycle  in  the  ABM  

Part 1: Agent-Based (Core) Model

Figure  4:  Results  of  replica;on;  x-­‐axis  denotes  simula;on  day,  y-­‐axis  denotes  adult  abundance  

•  A spatial extension of the core model •  Malaria dynamics is subject to substantial local variations (e.g. locations of aquatic habitats and bloodmeal events) •  Important to model the spatial heterogeneity for an effective representation of the mosquito environment •  Spatial ABM allows more realistic modeling of these events •  Adult female mosquito agents move within the environment •  Resource-seeking events: host-seeking and oviposition, necessary to complete the gonotrophic cycle •  Mobility: in order to seek resources, agents move around from one cell to another •  Movement restricted within current Moore neighborhood

Part 2: Spatial Extension

VectorLand: a Landscape Simulator •  A tool to generate artificial landscapes for the spatial ABM •  For a landscape with hundreds of resources, it automates the task of generating spatial attributes (e.g. location, capacity, etc.) for the resources •  Builds the landscape in a format ready to feed into the ABM

Part 3: Replication/Validation

Part 4: Integrating GIS

•  We replicated the study "Agent-based modelling of mosquito foraging behaviour for malaria control", Weidong Gu, Robert J. Novak, Tran. Royal Soc of Trop. Med & Hyg (2009) 103 •  To study the impact of reduced availability of aquatic habitats on resource-seeking (host-seeking and oviposition) •  Three landscapes:

•  Diagonal R0, Horizontal R1, and Vertical R2, •  Different arrangements of 20 houses and 70 habitats

•  Different scenarios of source reduction (interventions) •  Targeted (T1, T2, T3): cover all aquatic habitats within 100, 200 and 300 m of surrounding houses, thus removing 4, 17 and 28 of 70 habitats, respectively •  Non-targeted (C1, C2, C3): randomly eliminate same numbers of habitats as of corresponding targeted interventions

Acknowledgements •  Drs. Frank H. Collins, Gregory R. Madey, Neil F. Lobo, and Dilkushi de Alwis Pitts, University of Notre Dame. •  Drs. William A. Hawley, John E. Gimnig, and Allen W. Hightower, CDC, for sharing the Asembo, Kenya dataset. •  Rumana Reaz Arifin, Center for Research Computing, University of Notre Dame. Figure  5:  Study  area  of  Asembo,  Kenya  

Figure  7:  Female  adults  by  loca;on;  LeH:  21K,  Right:  150K;  legends  shown  on  right  reflect  magnitudes  

Figure  8:  Number  of  bloodmeal  events  (per  house,  cumula;ve);  LeH:  21K,  Right:  150K;  legends  shown  on  right  reflect  magnitudes  

•  Augment the spatial ABM with georeferenced data from a GIS (Geographic Information System) •  Explore biological insights by studying the spatial and temporal patterns from the GIS maps

•  Dataset: the CDC dataset collected for Asembo, Kenya •  Resolution (of each cell) is 50m x 50m •  Study area reflects ~ 23 km2

Workflow •  Separate layers of GIS data: identify relevant habitats •  Aquatic habitats: three types:

•  Breeding Site 1, Breeding Site 2, Borehole (Borrow pit) •  Bloodmeal locations: Houses, huts, etc. •  Habitat Capacity (HC)

•  reflects the relative size of each habitat in the ABM •  For different habitat types, we assign arbitrary capacities that reflect their relative sizes •  Create two hypothetical scenarios with different Combined Habitat Capacity (CHC): 21K and 150K

•  For a fixed geographic region, once the data layers, features, etc. are decided, they do not need to change for different spatial analysis with ABM •  Simplicity: using the same workflow, different scenarios (regions) can be fed into the ABM

Advantages

•  Adult abundance is higher near habitats with higher habitat capacities (Figure 7) •  Number of bloodmeal events (per house, cumulative) is higher in houses which are near habitats with higher habitat capacities (Figure 8) •  Increasing CHC increases abundance and number of bloodmeal events (Figures 7 & 8) •  Proximity to resources (habitats and houses): important regulating factor for variables of interest (e.g. adult abundance)

Summary

Summary •  Targeted reductions more effective than random reductions •  For these measures of outputs, the study has been successfully replicated and validated

Results •  Targeted interventions (T1, T2, T3)

•  T1 was marginally effective for all three (R0, R1, R2) •  T2 had sharp declines for two landscapes (R0, R2) •  T3 was the most effective for all three: mosquito populations

were not sustainable •  Non-targeted interventions (C1, C2, C3)

•  In all landscapes, abundance declined with increased coverage

Figure  3:  Selected  landscapes/interven;ons  

Figure  2:  VectorLand  screenshot  

Figure  6:  Habitat  legends  for  Figures  7  &  8  (below)