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Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
Ecoepidemiology of West Nile virus transmission in urban areas:
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Ecoepidemiology of West Nile virus transmission in urban areas:

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GRF 2nd One Health Summit 2013: Presentation by Edward N. Walker, Michigan State University

GRF 2nd One Health Summit 2013: Presentation by Edward N. Walker, Michigan State University

Published in: Health & Medicine, Technology
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  • Redo this to make it easier to read.
  • Adjust symbols / fonts on model graph
  • This shows 3 time periods stacked on each other. Not intended to give absolute MIR value, but to show the relative difference in MIR in the time periods.
  • Transcript

    • 1. Ecoepidemiology of West Nile virus transmission in urban areas: Processes and Predictions of Disease Outbreaks Edward D. Walker Michigan State University
    • 2. Acknowledgements Michigan State University • Edward Walker University of Illinois • Marilyn Ruiz • Uriel Kitron University of Wisconsin • Tony Goldberg Ecology of Infectious Diseases Program 080403 - Eco-epidemiology of West Nile Virus Transmission in Urban Areas Graduate students: Gabe Hamer, Scott Loss, Allie Gardner, Bethany Krebs, Christina Newman Postdoctorals: Gabe Hamer, Luis Chavez, Tavis Anderson
    • 3. Risk of introduction and spread of vector-borne and zoonotic disease agents is great but our predictive capacity is poor. Hierarchical transition risk analysis of emerging zoonotic infections such as West Nile virus offers a conceptualization and algorithm that may serve: Probabilistic events at branch points lead to success or failure depending upon context, in particular receptivity of the environment under invasion. I am unaware of any application of this algorithm to emergence of zoonotic infections but it could be robust. Kolar C, Lodge D. Ecological predictions and risk assessment for alien fishes in North America. Science 2002; 1233-1236.
    • 4. Geographic range expansion and epidemiologic impact of West Nile virus Study site: Chicago metropolitan region
    • 5. Super-spreader bird species (e.g., American robin, Turdus americanus) promote seasonal transmission Culex pipiens: enzootic and epizootic vector amongst birds, and importantly epidemic/bridge vector to humans West Nile virus: a mosquito-borne flavivirus in the Japanese encephalitis virus complex, showing extraordinary host range and invasibility into a wide range of suitable environments
    • 6. Chicago metro area Observation 1: there is spatial aggregation of human, bird, and mosquito infections. Research question: Can this spatial aggregation be explained by urban landscape factors and biological associations? Ruiz et al. 2004. Environmental and social determinants of human risk during a West Nile virus outbreak in the greater Chicago area, 2002. International Journal of Health Geographics 3:8-18.
    • 7. Spatial clustering of human cases in one of 5 classified urban landscapes: Spatial clustering of mosquito infection in these same landscapes in close association with human cases Two thirds of all cases in one urban landscape
    • 8. Receptive habitat and risk landscape for West Nile virus: the suburban backyard setting of post World War II “old suburbs.” WNV is an anthropogenic zoonosis. Good vegetation structure for bird habitat and mosquito harborage Water source: watering lawns Lots of resident people, including retired elderly, living close together Mosquito production sites
    • 9. Observation 2: There is substantial variation in intensity and timing of West Nile virus amplification amongst years in the Chicago metropolitan area. Some years are high risk years, others are low risk. Presupposing that this variation relates to human risk of infection annually, why does this variation occur and can we use it to model human risk of infection? Amplification: seasonal increase in infection rate for virus in mosquito populations.
    • 10. Abnormal Degree Week Degree Week (DW) difference from average by week 35.00 2012 High risk WNV years DW Degree-Week 25.00 15.00 2010 2005 2011 2002 2006 5.00 2001 2007 2008 -5.00 2003 2009 2004 -15.00 Low risk WNV years July Temperature over 22 C (~ 72 F) accumulated each week -25.00 16 20 24 28 32 Week Week of year 36 40 44
    • 11. The model successfully predicts weekly Culex mosquito WNV infection (MIR) with precipitation and temperature. 25 --------Used to develop model---- 20 ----Weekly weather predicts MIR----- 15 10 5 0 -5 1 14 27 40 1 14 27 40 1 14 27 40 1 14 27 40 1 14 27 40 1 14 27 40 1 14 27 40 1 14 27 40 1 14 27 40 -10 03 Model2 + AVE 2004 2005 2006 2007 Observed 2008 2009 2011 2010 2012 Number of WNV Cases by Year in Chicago metropolitan area 2004 2005 2006 2007 2008 2009 2010 2011 2012
    • 12. Number of WNV cases 250 Early 200 150 T1 100 50 0 003 Middle 0.00 5.00 10.00 MIR for T2 2004 15.00 2005 20.00 T2 Late (summer time periods) T3 The prevalence of human infection increases with increasing mosquito infection rate (MIR) in early to mid-summer 2006 2007 2008 2009 2010 2011 2012 Higher MIR during T2 - June to mid-July – is associated with more human illness (not with T1 or T3 periods) Number of WNV Cases by Year in Cook & DuPage counties, Illinois 2004 2005 2006 2007 2008 2009 2010 2011 2012
    • 13. Conclusions • West Nile virus infections in humans and mosquitoes has marked spatial and temporal structure in the Chicago metropolitan area. • Analysis of the urban landscape reveals a strong association of human and mosquito infection with one of five landscape types, the “post World War II suburbs.” This green, residential, anthropogenic landscape comprises the greatest risk for human infection. Abundant bird habitat and copious mosquito production sites immediately associated with human dwellings offers an explanation for this association. • Temporal dynamics of seasonal virus amplification in the mosquito population can be modeled and predicted based largely on Degree Week accumulation with base 22 degrees C. The “heat effect” is modulated by precipitation. More rain means less West Nile virus infection risk. • Risk of human infection increases with linear rise in mosquito infection in the time period from early to mid summer when amplification intensifies. Human risk can be predicted from Degree Week accumulation. • Can the temporal pattern of mosquito infection be predicted? Yes, reasonably well. Abiotic factors (climate, weather) operate strongly in this system. Hot and dry promote transmission and increase risk.

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