A Genetic Programming Approach to Predict Mosquitoes Abundance

  • Riccardo Gervasi
  • Irene Azzali
  • Donal Bisanzio
  • Andrea Mosca
  • Luigi Bertolotti
  • Mario GiacobiniEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11451)


In ecology, one of the main interests is to understand species population dynamics and to describe its link with various environmental factors, such as habitat characteristics and climate. It is especially important to study the behaviour of animal species that can hosts pathogens, as they can be potential disease reservoirs and/or vectors. Pathogens of vector borne diseases can only be transmitted from an infected to a susceptible individual by a vector. Thus, vector ecology is a crucial factor influencing the transmission dynamics of vector borne diseases and their complexity. The formulation of models able to predict vector abundance are essential tools to implement intervention plans aiming to reduce the spread of vector-borne diseases (e.g. West Nile Virus). The goal of this paper is to explore the possible advantages in using Genetic Programming (GP) in the field of vector ecology. In this study, we present the application of GP to predict the distribution of Culex pipiens, a mosquito species vector of West Nile virus (WNV), in Piedmont, Italy. Our modelling approach took into consideration the ecological factors which affect mosquitoes abundance. Our results showed that GP was able to outperform a statistical model that was used to address the same problem in a previous work. Furthermore, GP performed an implicit feature selection, discovered automatically relationships among variables and produced fully explorable models.


Genetic Programming Ecological modeling Prediction West Nile virus 


  1. 1.
    Diamond, M.S. (ed.): West Nile Encephalitis Virus Infection: Viral Pathogenesis and the Host Immune Response. Emerging Infectious Diseases of the 21st Century. Springer, New York (2009). Scholar
  2. 2.
    Chambers, T., Monath, T.: The Flaviviruses: Detection, Diagnosis and Vaccine Development. Advances in Virus Research. Elsevier Science, Amsterdam (2003). Scholar
  3. 3.
    Sfakianos, J.N.: West Nile Virus. Chelsea House Publications, Langhorne (2005)Google Scholar
  4. 4.
    Kramer, L.D., Styer, L.M., Ebel, G.D.: A global perspective on the epidemiology of West Nile virus. Ann. Rev. Entomol. 53, 61–81 (2008). Scholar
  5. 5.
    Istituto Superiore di Sanità.
  6. 6.
    Autorino, G.L., et al.: West Nile virus epidemic in horses, Tuscany region, Italy. Emerg. Infect. Dis. 8(12), 1372–1378 (2002). Scholar
  7. 7.
    Ministero della Salute: Piano di sorveglianza nazionale per la encefalomielite di tipo West Nile (West Nile Disease). Gazzetta Ufficiale della Repubblica Italiana, N. 113, 16 May 2002Google Scholar
  8. 8.
    EpiCentro - Portale di epidemiologia.
  9. 9.
    Zeller, H.G., Schuffenecker, I.: West Nile virus: an overview of its spread in Europe and the Mediterranean basin in contrast to its spread in the Americas. Eur. J. Clin. Microbiol. Infect. Dis.: Off. Publ. Eur. Soc. Clin. Microbiol. 23(3), 147–156 (2004). Scholar
  10. 10.
    Becker, N., Jöst, A., Weitzel, T.: The Culex pipiens complex in Europe. J. Am. Mosq. Control Assoc. 28(4 Suppl.), 53–67 (2012). Scholar
  11. 11.
    Bisanzio, D., et al.: Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont region, Italy. Parasites Vectors 4, 230 (2011). Scholar
  12. 12.
  13. 13.
  14. 14.
    Stroup, W.: Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press, Boca Raton (2012)zbMATHGoogle Scholar
  15. 15.
    Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Van-der Linde, A.: Bayesian measures of model complexity and fit. J. Roy. Stat. Soc. 64(4), 583–639 (2002). Scholar
  16. 16.
    Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 71(2), 319–392 (2009). Scholar
  17. 17.
    Rosà, R., et al.: 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. Parasites Vectors 7, 269 (2014). Scholar
  18. 18.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  19. 19.
    Silva, S.: GPLAB - A Genetic Programming Toolbox for MATLAB.
  20. 20.
    Luke, S., Panait, L.: Lexicographic parsimony pressure. In: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, GECCO 2002, pp. 829–836. Morgan Kaufmann Publishers Inc., San Francisco (2002)Google Scholar
  21. 21.
    Vargha, A., Delaney, H.D.: A critique and improvement of the “CL" common language effect size statistics of McGraw and Wong. J. Educ. Behav. Stat. 25(2), 101–132 (2000). Scholar
  22. 22.
    A vectorial approach to genetic programming. Submitted to EuroGP 2019Google Scholar
  23. 23.
    Poli, R., Langdon, W., McPhee, N., Koza, J.: A Field Guide to Genetic Programming., Morrisville (2008). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DAMU - Data Analysis and Modeling Unit, Department of Veterinary SciencesUniversity of TorinoTurinItaly
  2. 2.Department of Management and Production Engineering (DIGEP)Politecnico di TorinoTurinItaly
  3. 3.RTI InternationalWashington, DCUSA
  4. 4.Division of Epidemiology and Public Health, School of MedicineUniversity of NottinghamNottinghamUK
  5. 5.Istituto per le Piante da Legno e l’Ambiente (IPLA), regional government-owned corporation of Regione PiemonteTurinItaly

Personalised recommendations