Influence of landscape factors and management decisions on spatial and temporal patterns of the transmission of chronic wasting disease in white-tailed deer

Ruiz, Marilyn O.; Kelly, Amy C.; Brown, William M.; Novakofski, Jan E.; Mateus-Pinilla, Nohra E.

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https://hdl.handle.net/2142/118055 Copy

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Title

Influence of landscape factors and management decisions on spatial and temporal patterns of the transmission of chronic wasting disease in white-tailed deer

Author(s)

• Ruiz, Marilyn O.
• Kelly, Amy C.
• Brown, William M.
• Novakofski, Jan E.
• Mateus-Pinilla, Nohra E.

Issue Date

2013

Keyword(s)

• chronic wasting disease
• landscape epidemiology
• white-tailed deer
• environmental transmission
• prion
• USA

Abstract

Chronic wasting disease (CWD) has been reported in white-tailed deer at the border of the US states of Illinois and Wisconsin since 2002. Transmission of infectious prions between animals and from the environment has resulted in spatial and temporal structure observable in the spatio-temporal patterns of reported cases. Case locations of 382 positive cases from 28,954 deer tested between 2002 and 2009 provided insight into the potential risk factors and landscape features asso- ciated with transmission using a combination of clustering, generalised linear modelling and descriptive evaluations of a risk map of predicted cases of CWD. A species distribution map of white-tailed deer developed using MaxEnt provided an esti- mate of deer locations. We found that deer probability increased in areas with larger forests and less urban and agricultur- al lands. Spatial clustering analysis revealed a core area of persistent CWD transmission in the northern part of the region. The regression model indicated that larger and more compact forests were associated with higher risk for CWD. High risk areas also had soils with less clay and more sand than other parts of the region. The transmission potential was higher where landscape features indicated the potential for higher deer concentrations. The inclusion of spatial lag variables improved the model. Of the 102 cases reported in the study area in the two years following the study period, 89 (87%) of those were in the 32% of the study area with the highest 50% of predicted risk of cases.

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