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Towards the use of genetic programming in the ecological modelling of mosquito population dynamics

Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis.

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References

  1. M.S. Diamond, West Nile Encephalitis Virus Infection (Springer, Berlin, 2008)

    Google Scholar 

  2. L.D. Kramer, L.M. Styer, G.D. Ebel, A global perspective on the epidemiology of west nile virus. Annu. Rev. Entomol. 53, 61–81 (2008)

    Article  Google Scholar 

  3. G.L. Autorino, A. Battisti, V. Deubel, G. Ferrari, R. Forletta, A. Giovannini, R. Lelli, S. Murri, M.T. Scicluna, West Nile virus epidemic in horses, Tuscany region, Italy. Emerg. Infect. Dis. 8(1), 372–1378 (2002)

    Google Scholar 

  4. F. Monaco, R. Lelli, L. Teodori, C. Pinoni, A. Di Gennaro, A. Polci, P. Calistri, G. Savini, Re-emergence of West Nile virus in Italy. Zoonosis Public Health 57, 476–486 (2010)

    Article  Google Scholar 

  5. Ministero della Salute. West Nile Disease - Notifica alla Commissione europea e all’OIE - Piano di sorveglianza straordinaria. Gazzetta Ufficiale della Repubblica Italiana, N. 277, 26/11/2008

  6. D. Bisanzio, M. Giacobini, L. Bertolotti, A. Mosca, L. Balbo, U. Kitron, G.M. Vazquez-Prokopec, Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont Region, Italy. Parasites & Vectors, 4 (2011)

  7. O. Engler, G. Savini, A. Papa, J. Figuerola, M.H. Groschup, H. Kampen, European surveillance for west nile virus in mosquito populations. Int. J. Environ. Res. Public Health 10, 4869–4895 (2013)

    Article  Google Scholar 

  8. C. Talla, D. Diallo, I. Dia, Y. Ba, J.-A. Ndione et al., Statistical modeling of the abundance of vectors of West African Rift Valley Fever in Barkédji, Senegal. PLoS ONE 9(12), (2014)

  9. A.S. Walsh, G.E. Glass, C.R. Lesser, F.C. Curriero, Predicting seasonal abundance of mosquitoes based on off-season meteorological conditions. Environ. Ecol. Stat. 15, 279–291 (2008)

    Article  MathSciNet  Google Scholar 

  10. B. Shaeffer, B. Mondet, S. Touzeau, Using a climate-dependent model to predict mosquito abundance: application to Aedes (Stegomyia) africanus and Aedes (Diceromyia) furcifer (Diptera: Culicidae). Infect. Genetics Evol. 8, 422–432 (2008)

    Article  Google Scholar 

  11. J. Koza, Genetic programming: On the programming of computers by means of natural selection (1992)

  12. S. Marini, A. Conversi, Understanding zooplankton long term variability through genetic programming, in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012. Lecture Notes in Computer Science (2012)

  13. D.J. Papworth, S. Marini, A. Conversi, A novel, unbiased analysis approach for investigating population dynamics: A case study on Calanus finmarchicus and its decline in the North Sea. PLoS ONE 11(7), (2016)

  14. R. Gervasi, I. Azzali, D. Bisanzio, A. Mosca, L. Bertolotti, M. Giacobini, A genetic programming approach to predict mosquitoes abundance, in Genetic Programming, EuroGP 2019. Lecture Notes in Computer Science (2019)

  15. R. Wagner, M. Obach, H. Werner, H.-H. Schmidt, Artificial neural nets and abundance prediction of aquatic insects in small streams. Ecol. Inform. 1, 423–430 (2006)

    Article  Google Scholar 

  16. T. Santosh, D. Ramesh, Artificial neural network based prediction of malaria abundances using big data: a knowledge capturing approach. Clin. Epidemiol. Global Health 17, 121–126 (2019)

    Google Scholar 

  17. J.S. Evans, M.A. Murphy, Z.A. Holden, S.A. Cushman, Modeling species distribution and change using random forest, in Predictive Species and Habitat Modeling in Landscape Ecology, pp. 139–159 (2011)

  18. T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’16, pp. 785–794 (2016)

  19. ORNL DAAC Oak Ridge TennesseeUSA. ORNL DAAC 2018 MODIS and VIIRS land products global subsetting and visualization tool. https://modis.ornl.gov/

  20. Arpa Piemonte. http://www.arpa.piemonte.it

  21. S. Silva, GPLAB—A Genetic Programming Toolbox for MATLAB

  22. W.W. Stroup, Generalized Linear Mixed Models: Modern Concepts, Methods and Applications (CLC Press, Boca Raton, 2012)

    MATH  Google Scholar 

  23. D.J. Spiegelhalter, N. Best, B.P. Carlin, A. Linde, Bayesian measures of model complexity and fit. J. R. Stat. Soc. B 64, 1–34 (2002)

    Article  MathSciNet  Google Scholar 

  24. INLA. https://inla.r-inla-download.org/R/stable

  25. Random forest, 2002. https://CRAN.R-project.org/doc/Rnews/

  26. L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  27. XGBoost, R package version 0.82.1, 2019. https://CRAN.R-project.org/package=xgboost

  28. S. Haykin, Neural networks: a comprehensive foundation (1999)

  29. The MathWorks. MATLAB Neural Network Toolbox (2018)

  30. R. Poli, W.B. Langdon, N.F. McPhee, A field guide to genetic programming (2008)

  31. S. Luke, L. Panait, Lexicographic parsimony pressure, in GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 829–836 (2002)

  32. K. Levemberg, A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2(2), 164–168 (1944)

    Article  MathSciNet  Google Scholar 

  33. A.T. Ciota, A.C. Matacchiero, A.M. Kilpatrick, L.D. Kramer, The effect of temperature on life history traits of Culex mosquitoes. J. Med. Entomol. 51(1), 55–62 (2011)

    Article  Google Scholar 

  34. I. Azzali, L. Vanneschi, S. Silvia, I. Bakurov, M. Giacobini, A vectorial approach to genetic programmig. In Genetic Programming- 22nd European Conference EUROGP 2019, Lecture Notes in Computer Science (2019)

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Acknowledgements

This work was partially supported by Fundação para a Ciência e a Tecnologia (FCT), Portugal, through project BINDER (PTDC/CCI-INF/29168/2017). This study was also partially supported by Ministero dell’Istruzione, dell’Universitá e della Ricerca (MIUR) under the program “Dipartimenti di Eccellenza ex L.232/2016” to the Department of Veterinary Science, University of Turin.

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Azzali, I., Vanneschi, L., Mosca, A. et al. Towards the use of genetic programming in the ecological modelling of mosquito population dynamics. Genet Program Evolvable Mach 21, 629–642 (2020). https://doi.org/10.1007/s10710-019-09374-0

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  • DOI: https://doi.org/10.1007/s10710-019-09374-0

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