A test of agent-based models as a tool for predicting patterns of pathogen transmission in complex landscapes.
BACKGROUND
Landscape complexity can mitigate or facilitate host dispersal, influencing patterns of pathogen transmission. Spatial transmission of pathogens through landscapes, therefore, presents an important but not fully elucidated aspect of transmission dynamics. Using an agent-based model (LiNK) that incorporates GIS data, we examined the effects of landscape information on the spatial patterns of host movement and pathogen transmission in a system of long-tailed macaques and their gut parasites. We first examined the role of the landscape to identify any individual or additive effects on host movement. We then compared modeled dispersal distance to patterns of actual macaque gene flow to both confirm our model's predictions and to understand the role of individual land uses on dispersal. Finally, we compared the rate and the spread of two gastrointestinal parasites, Entamoeba histolytica and E. dispar, to understand how landscape complexity influences spatial patterns of pathogen transmission.


RESULTS
LiNK captured emergent properties of the landscape, finding that interaction effects between landscape layers could mitigate the rate of infection in a non-additive way. We also found that the inclusion of landscape information facilitated an accurate prediction of macaque dispersal patterns across a complex landscape, as confirmed by Mantel tests comparing genetic and simulated dispersed distances. Finally, we demonstrated that landscape heterogeneity proved a significant barrier for a highly virulent pathogen, limiting the dispersal ability of hosts and thus its own transmission into distant populations.


CONCLUSIONS
Landscape complexity plays a significant role in determining the path of host dispersal and patterns of pathogen transmission. Incorporating landscape heterogeneity and host behavior into disease management decisions can be important in targeting response efforts, identifying cryptic transmission opportunities, and reducing or understanding potential for unintended ecological and evolutionary consequences. The inclusion of these data into models of pathogen transmission patterns improves our understanding of these dynamics, ultimately proving beneficial for sound public health policy.
Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA. lanedegraaf.kelly@gmail.com.
Kelly E
KE
Lane-deGraaf
Ryan C
RC
Kennedy
S M Niaz
SM
Arifin
Gregory R
GR
Madey
Agustin
A
Fuentes
Hope
H
Hollocher
D063147:Animal Distribution; D000818:Animals; D003198:Computer Simulation; D004463:Ecology / Q000379:methods; D004747:Entamoeba / Q000472:pathogenicity*; D004749:Entamoebiasis / Q000635:transmission*; D004777:Environment; D051456:Gene Flow; D040362:Geographic Information Systems; D008251:Macaca / Q000235:genetics / Q000469:parasitology*; D008954:Models, Biological