Skip to main content
SpringerLink
Log in

Short-term prediction of Culex quinquefasciatus abundance in Central North Georgia, USA, based on the meteorological variability

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Culex quinquefasciatus is the main vector of West Nile Virus (WNV) in the southeast USA, and inter-annual variation in this vector abundance is mainly related to meteorological variability. In this study, short-term effects of meteorological conditions on seasonal variation in the vector abundance in the central north part of the State of Georgia, USA, from 2002 to 2009 were assessed. Four weeks moving average temperature, precipitation, potential evapotranspiration, and available moisture in the surface layer of soil were considered as risk factors. Cross-correlation maps were developed to investigate influences of preceding environmental conditions during a time-lagged interval on mosquito count data. The Poisson regression model and Artificial Neural Network (ANN) model were used for prediction purposes. Two sets of predictors were used: (1) the interval lagged climate data with the highest correlation and (2) single time lag antecedent Culex mosquito abundance up to 10 weeks prior to the events combined with lagged climate data. Results revealed that both models predicted the seasonal cycle of vector abundance fairly accurately, with ANN performing better than the regression model. The addition of antecedent mosquito data as input improved the prediction power of both models. The developed predictive models can be helpful in informed decision-making when high WNV activities are anticipated.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. ArboNET, Arboviral Diseases Branch, Centers for Disease Control and Prevention (2019) https://www.cdc.gov/mosquitoes/mosquito-control/professionals/ArboNET.html. Accessed Mar 2020

  2. Andreadis TG (2012) The contribution of Culex pipens complex mosquitoes to transmission and persistence of West Nile Virus in North America. J Am Mosq Control Assoc 28(4 Suppl):137–151. https://doi.org/10.2987/8756-971X-28.4s.137

    Article  Google Scholar 

  3. Roiz D, Ruiz S, Soriguer R, Figuerola J (2014) Climatic effects on mosquito abundance in Mediterranean wetlands. Parasit Vectors 7:1–13

    Article  Google Scholar 

  4. Paull SH, Horton DE, Ashfaq M, Rastogi D, Kramer LD, Diffenbaugh NS, Kilpatrick AM (2017) Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts. Proc R Soc B 284:20162078. https://doi.org/10.1098/rspb.2016.2078

    Article  Google Scholar 

  5. Mordecai EA, Caldwell JM, Grossman K (2019) Thermal biology of mosquito-borne disease. Ecol Let 2(10):1690–1708. https://doi.org/10.1111/ele.13335

    Article  Google Scholar 

  6. Strickman D (1988) Rate of oviposition by Culex Quinquafasciatus in San Antonio, Texas, during three years. J Am Mosq Control Assoc 4(3):339–344

    Google Scholar 

  7. Rey JR (2011) The mosquito ENY-727 (IN652), one of a series of the Entomology and Nematology Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida

  8. American Mosquito Control Association (2014) http://www.mosquito.org/

  9. Curriero FC, Shone SM, Glass GE (2005) Cross correlation maps: a tool for visualizing and modeling time lagged associations. Vector-Borne Zoonotic Dis 5:267–275

    Article  Google Scholar 

  10. Reisen W, Fang Y, Martinez VM (2006) Effects of temperature on the transmission of West Nile Virus by Culex tarsalis (Diptera: Culicidae). J Med Entomol 43:309–317

    Article  Google Scholar 

  11. Dohm DJ, O’Guinn ML, Turel MJ (2002) Effect of environmental temperature on the ability of Culex pipiens (Diptera: Culicidae) to transmit West Nile virus. J Med Entomol 39:221–225

    Article  Google Scholar 

  12. Epstein PR (2001) West Nile Virus and the climate. J Urban Health Bull N Y Acad Med 78(2):367–371. https://doi.org/10.1093/jurban/78.2.367

    Article  Google Scholar 

  13. Patz JA, Olson SH, Uejio ChK, Gibbs HK (2008) Disease emergence from global climate and land use change. Med Clin N Am 92:1473–1491

    Article  Google Scholar 

  14. Wang G, Minnis RB, Belant JR, Wax ChR (2010) Dry weather induces outbreaks of human West Nile virus infections. BMC Infect Dis 10:1–7

    Article  Google Scholar 

  15. Day JF (2001) Predicting St. Louis encephhalitis virus epidemics: lessons from recent, and not so recent. Outbreaks Annu Rev Entomol 46:111–138

    Article  Google Scholar 

  16. Shaman J, Stieglitz M, Stark C, Blancq SL, Cane M (2002) Using a dynamic hydrology model to predict mosquito abundances in flood and swamp water. Emerg Infect Dis 8:6–13

    Google Scholar 

  17. Soverow JE, Wellenius GA, Fisman DE, Mittleman MA (2009) Infectious disease in a warming world: how weather influenced west nile virus in the United States (2001–2005). Environ Health Perspect 117:1049–1052

    Article  Google Scholar 

  18. Ruiz MO, Chaves LF, Hamer GL, Sun T et al (2010) Local impact of temperature and precipitation on West Nile virus infection in Culex species mosquitoes in northeast Illinois, USA. Parasit Vectors 3:1–16

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Day J, Shaman J (2008) Using hydrologic conditions to forecast the risk of focal and epidemic arboviral transmission in Peninsular Florida. J Med Entomol 45:458–465

    Article  Google Scholar 

  21. Wang J, Ogden NH, Zhu H (2011) The impact of weather conditions on Culex pipiens and Culex restuans (Diptera: Culicidae) abundance: a case Study in Peel Region. J Med Entomol 48:468–475

    Article  Google Scholar 

  22. Walsh AS, Glass GE, Lesser CR, Curriero FC (2008) Predicting seasonal abundance of mosquitoes based on off-season meteorological conditions. Environ Ecol Stat 15:279–291

    Article  MathSciNet  Google Scholar 

  23. Ahumada JA, Lapointe D, Samuel MD (2004) Modeling the population dynamics of Culex quinquefasciatus (Diptera: Culicidae), along an elevational gradient in Hawaii. J Med Entomol 41:1157–1170

    Article  Google Scholar 

  24. Gong H, DeGaetano A, Harrington LC (2007) A climate based mosquito population model. In: Proceedings of the world congress on engineering and computer science, October 24–26, San Francisco, USA

  25. Morin CW, Comrie AC (2010) Modeled response to the West Nile virus vector Culex quinquefasciatus to changing climate using the dynamic mosquito simulation model. Int J Biometeorol 54:517–529

    Article  Google Scholar 

  26. Shone SM, Curriero FC, Lesser CR, Glass GE (2006) Characterizing population dynamics of aedes sollicitans (diptera: culicidae) using meteorological data. J Med Entomol 43:393–402

    Article  Google Scholar 

  27. Chuang TW, Ionides EL, Knepper RG, Stanuszek WW, Walker ED, Wilson ML (2012) Cross-correlation map analyses show weather variation influences on mosquito abundance patterns in Saginaw County, Michigan, 1989–2005. J Med Entomol 49:851–858

    Article  Google Scholar 

  28. Lebl K, Brugger K, Rubel F (2013) Predicting Culex pipiens/restuans population dynamics by interval lagged weather data. Parasit Vectors 6:1–11

    Article  Google Scholar 

  29. Huang J, Huug M, Dool VD, Georgakakos KP (1996) Analysis of model-calculated soil moisture over the United States (1931–93) and application to long-range temperature forecasts. J Climate 9(6):1350

    Article  Google Scholar 

  30. Van den Dool H, Huang J, Fan Y (2003) Performance and analysis of the constructed analogue method applied to US soil moisture over 1981–2001. J Geophys Res 108:1–12

    Google Scholar 

  31. Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94

    Article  Google Scholar 

  32. Vazquez-Prokopec GM, Vanden Eng JL, Kelly R, Mead DG, Kolhe P, Howgate J, Kitron U, Burkot TR (2010) The risk of west nile virus infection is associated with combined sewer overflow streams in urban Atlanta, Georgia, USA. Environ Health Persp 118:1382–1388

    Article  Google Scholar 

  33. Silver JB (2008) Sampling adults with light-traps. In: Mosquito ecology: field sampling methods, Springer, New York, pp 845–946

  34. Reiter P (1983) A portable, battery-powered trap for collecting gravid Culex mosquitoes. Mosq News 43(4):496–498

    Google Scholar 

  35. Stoddard ST, Wearing HJ, Reiner RC Jr et al (2014) Long-term and seasonal dynamics of Dengue in Iquitos. Peru PLoS Negl Trop Dis 8:1–15

    Google Scholar 

  36. R Core Team (2013) R: a language and environment for statistical computing. Reference Index. R Foundation for Statistical Computing, Vienna, Austria. Version 3.0.1

  37. Dawson CW, Wilby RL (2001) Hydrologic modeling using artificial neural networks. Progs Phys Geogr 25:80–108

    Article  Google Scholar 

  38. Kalin L, Isik S, Schoonover JE, Lockaby BG (2010) Predicting water quality in unmonitored watersheds using artificial neural networks. J Environ Qual 39:1429–1440

    Article  Google Scholar 

  39. Rezaeian Zadeh M, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manage 24:2673–2688

    Article  Google Scholar 

  40. Nash JE, Sutcliff JV (1970) River flow forecasting through conceptual models, part I: a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  41. Salas JD, Markus M, Tokar AS (2000) Streamflow forecasting based on artificial neural networks. In: Artificial neural networks in hydrology, Water Science and Technology Library, vol 36, Springer, pp 23–51

  42. Morin CW, Comrie AC (2013) Regional and seasonal response of a West Nile virus vector to climate change. PNAS 110:15620–15625

    Article  Google Scholar 

  43. Rosa R, Marini G, Bolzoni L, Neteler M et al (2014) Early warning of West Nile virus mosquito vector: climate and land use models successfully explain phenology and abundance of Culex pipiens mosquitoes in north-western Italy. Parasit Vectors 7:1–12

    Article  Google Scholar 

  44. Harrigan RJ, Thomassen HA, Buermann W, Smith ThB (2014) A continental risk assessment of West Nile virus under climate change. Glob Change Biol 20:2417–2425

    Article  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the Southeast Cooperative Wildlife Disease Study (SCWDS) at the College of Veterinary Medicine at University of Georgia, Athens, for providing the mosquito data.

Funding

This project is funded by USDA Forest Service, National Urban & Community Forestry Council and Center for Environmental Studies at the Urban–Rural Interface, School of Forestry and Wildlife Sciences, Auburn University.

Author information

Authors and Affiliations

  1. Institute for Disease Modeling, Bill and Melinda Gates Foundation, 500 5th Ave N, Seattle, WA, 98109, USA

    Navideh Noori

  2. School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL, 36849-5126, USA

    Latif Kalin & B. Graeme Lockaby

  3. Department of Biology, Eastern Washington University, 258 Science Building, Cheney, WA, 99004, USA

    Krisztian Magori

Authors
  1. Navideh Noori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Latif Kalin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. Graeme Lockaby
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krisztian Magori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Navideh Noori.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noori, N., Kalin, L., Lockaby, B.G. et al. Short-term prediction of Culex quinquefasciatus abundance in Central North Georgia, USA, based on the meteorological variability. Neural Comput & Applic 34, 14717–14728 (2022). https://doi.org/10.1007/s00521-022-07324-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07324-z

Keywords

Search

Navigation