Skip to main content

Statistical Tools for West Nile Virus Disease Analysis

  • Protocol
  • First Online:
West Nile Virus

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2585))

Abstract

West Nile virus (WNV) is the most widespread arbovirus in the world and endemic to much of the United States. Its range continues to expand as land use patterns change, creating more habitable environments for the mosquito vector. Though WNV is endemic, the year-to-year risk is highly variable, thus making it difficult to understand the risk for human spillover events. Abatement districts monitor for infected mosquitoes to help understand these potential risks and to help guide our understanding of the risk posed by these observed infected mosquitoes. Creating optimal monitoring networks will provide more informed decision-making tools for abatement districts and policy makers. Investment in these monitoring networks that capture robust observations on mosquito infection rates will allow for environmentally informed inference systems to help guide decision-making and WNV risk. In turn, enhanced decision-making tools allow for faster response times of more targeted and economical surveillance and mosquito population reduction efforts and the overall reduction of WNV transmission. Here we discuss the data streams, their processing, and specifically three ways to calculate WNV infection rates in mosquitoes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

CoC:

City of Chicago

MIR:

minimum infection rate

MLE:

maximum likelihood estimate

MPN:

mosquitoes per night

PPN:

pools per night

PPPN:

positive pools per night

VI:

vector index

References

  1. DeFelice NB, Schneider Z, Little E, Barker C, Caillouet KA, Campbell SR, Damian D, Irwin P, Jones HMP, Townsend J, Shaman J (2018) Use of temperature to improve West Nile virus forecasts. PLoS Comput Biol 14(3). https://doi.org/10.1371/journal.pcbi.1006047

  2. Shocket MS, Verwillow AB, Numazu MG, Slamani H, Cohen JM, El Moustaid F et al (2020) Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23 C and 26 C. elife 9:e58511

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Little E, Campbell SR, Shaman J (2016) Development and validation of a climate-based ensemble prediction model for West Nile virus infection rates in Culex mosquitoes, Suffolk County, New York. Parasit Vectors 9(1):443

    Article  PubMed  PubMed Central  Google Scholar 

  4. Barber LM, Schleier JJ, Peterson RK (2010) Economic cost analysis of West Nile virus outbreak, Sacramento county, California, USA, 2005. Emerg Infect Dis 16(3):480–486

    Article  PubMed  PubMed Central  Google Scholar 

  5. DeFelice NB, Birger R, DeFelice N, Gagner A, Campbell SR, Romano C, Santoriello M, Henke J, Wittie J, Cole B, Kaiser C, Shaman J (2019) Modeling and surveillance of reporting delays of mosquitoes and humans infected with West Nile virus and associations with accuracy of West Nile virus forecasts. JAMA Netw Open 2(4):e193175. https://doi.org/10.1001/jamanetworkopen.2019.3175

    Article  PubMed  PubMed Central  Google Scholar 

  6. Nasci R, Fischer M, Lindsey N, Lanciotti R, Savage H, Komar N et al (2013) West Nile virus in the United States: guidelines for surveillance, prevention, and control. Centers for Disease Control and Prevention, Atlanta

    Google Scholar 

  7. Paull SH, Horton DE, Ashfaq M, Rastogi D, Kramer LD, Diffenbaugh NS et al (2017) Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts. Proc R Soc B 284(1848):20162078

    Article  PubMed  PubMed Central  Google Scholar 

  8. Shaman J, Day JF, Stieglitz M (2005) Drought-induced amplification and epidemic transmission of West Nile virus in southern Florida. J Med Entomol 42(2):134–141

    Article  PubMed  Google Scholar 

  9. Gu W, Lampman R, Novak R (2004) Assessment of arbovirus vector infection rates using variable size pooling. Med Vet Entomol 18(2):200–204

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicholas B. DeFelice .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Ward, M.J., Sorek-Hamer, M., Vemuri, K.K., DeFelice, N.B. (2023). Statistical Tools for West Nile Virus Disease Analysis. In: Bai, F. (eds) West Nile Virus. Methods in Molecular Biology, vol 2585. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2760-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2760-0_16

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2759-4

  • Online ISBN: 978-1-0716-2760-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics