This document discusses using ecological niche modeling to map disease transmission risk. It describes how spatial models can help identify areas to focus disease prevention and treatment resources. The document presents examples of past models mapping the distributions of Marburg virus in Africa and the sand fly Lutzomyia longipalpis. It advocates developing models that consider interactions between relevant species like pathogens, vectors and hosts to better anticipate transmission risk. The document outlines a proposed workflow for building such integrated models and applying them under future climate change scenarios. It acknowledges gaps in data and approaches that need more development to create truly predictive spatial epidemiology models.

1. Mapping Disease Transmission Risk
from Biogeographic and Ecological
Perspectives
A. Townsend Peterson
University of Kansas

2. Why Maps for Diseases?
• Where to focus resources for vaccination?
• Where to focus educational efforts?
• Where to place diagnostic facilities and
equipment?
• In short, where to expect a disease to occur,
and where not????

4. Published January 2004

5. • Marburg disease
distribution
• Spotty, patchy
potential distribution
across eastern and
southern Africa
• Distinct ecological
distribution from
ebola (open circles)
• Potential distribution
extends to Cameroon
and northern Angola

6. • Marburg disease
distribution
• Spotty, patchy
potential distribution
across eastern and
southern Africa
• Distinct ecological
distribution from
ebola (open circles)
• Potential distribution
extends to Cameroon
and northern Angola

9. Update to 2015:
• Approximate doubling in information
over 2004 efforts
• Species by species model
development
• Explicit consideration of uncertainty
in model predictions
• In review for publication

11. THE CURRENT DISEASE TOOLKIT

12. Dots on Maps

18. ?
?
?
?
?

22. The Situation …
• Spatial-only models do nothing to establish a
connection between occurrence and context
• No good way to anticipate disease
transmission risk responses to future climates
• Lots of talk, lots of discussion, not much data
• Some adaptations of transmission models to
the question, but not terribly spatially explicit
• These gaps left open many questions…

23. Geography
Disease
Incidence

24. Geography
Disease
Incidence
Spatial Modeling

25. Geography
Disease
Incidence
Environmental
Conditions

26. Geography
Disease
Incidence
Environmental
Conditions
Space-and-
environment Models

27. Geography
Disease
Incidence
Environmental
Conditions
Interactions
among species
Pathogens
Vectors
Hosts

29. Workflow
• Understand disease system in detail
• Identify suite of species relevant to the disease
(vectors, hosts, pathogen)
• Develop hypotheses of relevant regions (M) for each
species
• Fit ecological niche models individually for each
species
• Model or simulate interactions between the species
to create transmission system
• Model or simulate human presence and behavior to
create risk map
• Transfer present model to future (post climate-
change) environmental (and human) scenarios

34. Lutzomyia longipalpis

43. ResearchedRaw

46. Lassa: Uncareful Results

50. Effects of Quality Control

51. Effects of Reducing Oversampling

52. Effects of Error Balance

54. New Approaches, Gaps, and Impediments
• Mapping and modeling approaches based in ecology
and biogeography have much to offer to spatial
epidemiology
– Working to create a truly predictive methodology that
can anticipate disease occurrence
• Methods
– Need to assure that the methodology used is consistent
with the processes that are occurring
– Ecology, biogeography, etc.
• Data, data, and more data…
– Occurrence data for species
– Relevant geospatial data
– Archival storage of existing samples to allow data
recycling

55. CASE STUDIES

63. Lutzomyia longipalpis and Climate Change

70. Chile?
• Aedes albopictus • Aedes aegypti

71. CONSTRAINTS AND LIMITATIONS

73. Examples of Disease-relevant Data

74. Examples of Disease-relevant Data

75. Examples of Disease-relevant Data

76. Examples of Disease-relevant Data

77. Examples of Disease-relevant Data

78. Examples of Disease-relevant Data

79. Examples of Disease-relevant Data

80. Examples of Disease-relevant Data

81. town@ku.edu