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Landscape Ecology

, Volume 34, Issue 6, pp 1295–1311 | Cite as

An operational machine learning approach to predict mosquito abundance based on socioeconomic and landscape patterns

  • Shi ChenEmail author
  • Ari Whiteman
  • Ang Li
  • Tyler Rapp
  • Eric Delmelle
  • Gang Chen
  • Cheryl L. Brown
  • Patrick Robinson
  • Maren J. Coffman
  • Daniel Janies
  • Michael Dulin
Research Article

Abstract

Context

Socioeconomic and landscape factors influence mosquito abundance especially in urban areas. Few studies addressed how socioeconomic and landscape factors, especially at micro-scale for mosquito life history, determine mosquito abundance.

Objectives

We aim to predict mosquito abundance based on socioeconomic and/or landscape factors using machine learning framework. Additionally, we determine these factors’ response to mosquito abundance.

Methods

We identified 3985 adult mosquitoes (majority of which were Aedes mosquitoes) in 90 sampling sites from Charlotte, NC, USA in 2017. Seven socioeconomic and seven landscape factors were used to predict mosquito abundance. Three supervised learning models, k-nearest neighbor (kNN), artificial neural network (ANN), and support vector machine (SVM) were constructed, tuned, and evaluated using both continuous input factors and binary inputs. Random forest (RF) was used to assess individual input’s relative importance and response to mosquito abundance.

Results

We showed that landscape factors alone yielded equal or better predictability than socioeconomic factors. The inclusion of both types of factors further improved model accuracy using binary inputs. kNN also had robust performance regardless of inputs (accuracy > 95% for binary and > 99% for continuous input data). Landscape factors group had higher importance than socioeconomic group (54.4% vs. 45.6%). Landscape heterogeneity (measured by Shannon index) was the single most important input factor for mosquito abundance.

Conclusions

Landscape factors were the key for mosquito abundance. Machine learning models were powerful tools to handle complex datasets with multiple socioeconomic and landscape factors to accurately predict mosquito abundance.

Keywords

Socioeconomic gradient Landscape heterogeneity Mosquito abundance Machine learning Urban ecology 

Abbreviations

VBD

Vector-borne diseases

POP

Population size factor

INC

Income factor

EMP

Employment rate factor

EDU

Education status factor

DEN

Population density factor

PRI

Home sale price factor

VCR

Violent crime rate factor

TRE

Tree canopy factor

GRS

Grass factor

BLD

Building factor

ROD

Road factor

SHI

Shannon index

SMP

Simpson index

kNN

k-Nearest neighbor

ANN

Artificial neural network

SVM

Support vector machine

GLM

Generalized linear model

RF

Random forest

NMDS

Non-metric multidimensional scaling

TP

True positive

TN

True negative

FP

False positive

FN

False negative

RMSE

Root mean squared error

Notes

Acknowledgement

We thank Mecklenburg County Health Department for providing funding for the mosquito sampling work in 2017 and access to the orthophotos. We are also grateful for the field work of ten volunteering undergraduate students from UNC Charlotte.

Supplementary material

10980_2019_839_MOESM1_ESM.docx (15 kb)
Supplementary material 1 (DOCX 15 kb)

References

  1. Adjei PO-W, Kyei PO (2013) Linkages between income, housing quality and disease occurrence in rural Ghana. J Hous Built Environ 28(1):35–49CrossRefGoogle Scholar
  2. Adler NE, Ostrove Joan M (1999) Socioeconomic status and health: what we know and what we don’t. Ann N Y Acad Sci 896:3–15CrossRefGoogle Scholar
  3. Baltensperger AP, Huettmann F (2015) Predictive spatial niche and biodiversity hotspot models for small mammal communities in Alaska: applying machine-learning to conservation planning. Landscape Ecol 30:681–697CrossRefGoogle Scholar
  4. Benedict MQ, Levine RS, Hawley WA, Lounibos LP (2007) Spread of the tiger: global risk of invasion by the mosquito Aedes albopictus. Vector Borne Zoonotic Dis 7(1):76–85CrossRefGoogle Scholar
  5. Boone CG, Cook E, Hall SJ et al (2012) A comparative gradient approach as a tool for understanding and managing urban ecosystems. Urban Ecosyst 15(4):795–807CrossRefGoogle Scholar
  6. Brown ME, Grace K, Shively G, Johnson KB, Carroll M (2014) Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change. Popul Environ 36(1):48–72CrossRefGoogle Scholar
  7. Buckner EA, Blackmore MS, Golladay SW, Covich AP (2011) Weather and landscape factors associated with adult mosquito abundance in southwestern Georgia, U.S.A. J Vector Ecol 36(2):269–278CrossRefGoogle Scholar
  8. Burger SV (2018) Introduction to machine learning with R. O’Reilly, SebastopolGoogle Scholar
  9. Chaves LF, Hamer GL, Walker ED, Brown WM, Ruiz MO, Kitron UD (2011) Climatic variability and landscape heterogeneity impact urban mosquito diversity and vector abundance and infection. Ecosphere 2(6):70CrossRefGoogle Scholar
  10. Chen S, Blanford JI, Fleischer SJ, Hutchinson M, Saunders MC, Thomas MB (2013) Estimating West Nile virus transmission period in pennsylvania using an optimized degree-day model. Vector Borne Zoonotic Dis 13(7):489–497CrossRefGoogle Scholar
  11. Chen S, Fleischer SJ, Saunders MC, Thomas MB (2015) The influence of diurnal temperature variation on degree-day accumulation and insect life history. PLoS ONE 10(3):1–15Google Scholar
  12. Chetty R, Hendren N, Kline P, Saez E (2014) Where is the land of Opportunity? The geography of intergenerational mobility in the united states. Q J Econ 129(4):1553–1623CrossRefGoogle Scholar
  13. Cianci D, Hartemink N, Ibáñez-Justicia A (2015) Modelling the potential spatial distribution of mosquito species using three different techniques. Int J Health Geogr 14(1):10CrossRefGoogle Scholar
  14. Cushman SA, Heuttmann F (2010) Spatial complexity, informatics, and wildlife conservation. Springer, TokyoCrossRefGoogle Scholar
  15. Degroote S, Bermudez-Tamayo C, Ridde V (2018) Approach to identifying research gaps on vector-borne and other infectious diseases of poverty in urban settings: scoping review protocol from the VERDAS consortium and reflections on the project’s implementation. Infect Dis Poverty 7(1):98CrossRefGoogle Scholar
  16. Delmelle E, Hagenlocher M, Kienberger S, Casas I (2016) A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia. Acta Trop 164:169–176CrossRefGoogle Scholar
  17. Dowd JB, Zajacova A, Aiello A (2009) Early origins of health disparities: burden of infection, health, and socioeconomic status in U.S. children. Soc Sci Med 68(4):699–707CrossRefGoogle Scholar
  18. Dowling Z, Armbruster P, LaDeau SL, DeCotiis M, Mottley J, Leisnham PT (2013) Linking mosquito infestation to resident socioeconomic status, knowledge, and source reduction practices in suburban Washington, DC. EcoHealth 10(1):36–47CrossRefGoogle Scholar
  19. Drew CA, Wiersma Y, Heuttmann F (2011) Predictive species and habitat modeling in landscape ecology. Springer, New YorkCrossRefGoogle Scholar
  20. Eder M, Cortes F, de Siqueira Teixeira, Filha N et al (2018) Scoping review on vector-borne diseases in urban areas: transmission dynamics, vectorial capacity and co-infection. Infect Dis Poverty 7(1):90CrossRefGoogle Scholar
  21. Fielding AH (1999) Machine learning methods for ecological applications. Springer, BerlinCrossRefGoogle Scholar
  22. Foley DH, Wilkerson RC, Rueda LM (2009) Importance of the “what”, “when”, and “where” of mosquito collection events. J Med Entomol 46(4):717–722CrossRefGoogle Scholar
  23. Fournet F, Jourdain F, Bonnet E, Degroote S, Ridde V (2018) Effective surveillance systems for vector-borne diseases in urban settings and translation of the data into action: a scoping review. Infect Dis Poverty 7(1):99CrossRefGoogle Scholar
  24. Gordis L (2013) Epidemiology, 5th edn. Elsevier, CanadaGoogle Scholar
  25. Gottdenker NL, Streicker DG, Faust CL, Carroll CR (2014) Anthropogenic land use change and infectious diseases: a review of the evidence. EcoHealth 11(4):619–632CrossRefGoogle Scholar
  26. Gunther F, Fritsch S (2010) Neuralnet: training of neural networks. R Journal 2(1):30–38CrossRefGoogle Scholar
  27. Hall V, Walker WL, Lindsey NP, Lehman JA, Kolsin J, Dandry K, Rabe IB, Hills SL, Fischer M, Staples JE, Gould CV, Martin SW (2018) Update: noncongential Zika virus disease cases — 50 U.S. States and the District of Columbia, 2016. Morb Moral Wkly Rep 67(9):265–269CrossRefGoogle Scholar
  28. Hawe P, Shiell A (2000) Social capital and health promotion: a review. Soc Sci Med 51(6):871–885CrossRefGoogle Scholar
  29. Hayden MH, Uejio CK, Walker K et al (2010) Microclimate and human factors in the divergent ecology of Aedes aegypti along the Arizona, U.S./Sonora, MX Border. EcoHealth 7(1):64–77CrossRefGoogle Scholar
  30. Hemme RR, Thomas CL, Chadee DD, Severson DW (2010) Influence of urban landscapes on population dynamics in a short-distance migrant mosquito: evidence for the dengue vector Aedes aegypti. PLOS Negl Trop Dis 4(3):1–9CrossRefGoogle Scholar
  31. Homan T, Maire N, Hiscox A et al (2016) Spatially variable risk factors for malaria in a geographically heterogeneous landscape, western Kenya: an explorative study. Malar J 15(1):1CrossRefGoogle Scholar
  32. Humphries G, Magness DR, Huettmann F (2018) Machine learning for ecology and sustainable natural resource management. Springer, SwitzerlandCrossRefGoogle Scholar
  33. Jaeger JAG (2000) Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation. Landscape Ecol 15(2):115–130CrossRefGoogle Scholar
  34. Johnson MF, Gómez A, Pinedo-Vasquez M (2008) Land use and mosquito diversity in the Peruvian amazon. J Med Entomol 45(6):1023–1030CrossRefGoogle Scholar
  35. Jopp F, Reuter H, Brecklings B (2011) Modelling complex ecological dynamics. Springer, BerlinCrossRefGoogle Scholar
  36. Kabaria CW, Molteni F, Mandike R et al (2016) Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dares Salaam. Int J Health Geogr 15(1):26CrossRefGoogle Scholar
  37. Kalluri S, Gilruth P, Rogers D, Szczur M (2007) Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathog 3(10):1–11CrossRefGoogle Scholar
  38. Karatzoglou A, Meyer D, Hornik K (2006) Support vector machines in R. J Stat Softw 15:1–28CrossRefGoogle Scholar
  39. Keating J, Macintyre K, Mbogo C et al (2003) A geographic sampling strategy for studying relationships between human activity and malaria vectors in Urban Africa. Am J Trop Med Hyg 68(3):357–365CrossRefGoogle Scholar
  40. Khormi HM, Kumar L (2011) Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Sci Total Environ 409(22):4713–4719CrossRefGoogle Scholar
  41. Kikuti M, Cunha GM, Paploski IAD et al (2015) Spatial distribution of dengue in a brazilian urban slum setting: role of socioeconomic gradient in disease risk. PLOS Negl Trop Dis 9(7):1–18CrossRefGoogle Scholar
  42. LaDeau SL, Leisnham PT, Biehler D, Bodner D (2013) Higher mosquito production in low-income neighborhoods of Baltimore and Washington, DC: understanding ecological drivers and mosquito-borne disease risk in temperate cities. Int J Environ Res Public Health 10(4):1505–1526CrossRefGoogle Scholar
  43. Lana RM, Riback TIS, Lima TFM et al (2017) Socioeconomic and demographic characterization of an endemic malaria region in Brazil by multiple correspondence analysis. Malar J 16(1):397CrossRefGoogle Scholar
  44. Lantz B (2015) Machine learning with R, 2nd edn. Packt Publishing, BirminghamGoogle Scholar
  45. Lary DJ, Woof S, Faruque F et al (2014) Holistics 3.0 for health. Int J Geoinf 3:1023–1038Google Scholar
  46. Le Comber SC, Rossmo D, Hassan AN, Fuller DO, Beier JC (2011) Geographic profiling as a novel spatial tool for targeting infectious disease control. Int J Health Geogr 10(1):35CrossRefGoogle Scholar
  47. Leigh JP (1993) Multidisciplinary findings on socioeconomic status and health. Am J Public Health 83:289–290CrossRefGoogle Scholar
  48. Leisnham PT, Juliano SA (2012) Impacts of climate, land use, and biological invasion on the ecology of immature Aedes mosquitoes: implications for La Crosse emergence. EcoHealth 9(2):217–228CrossRefGoogle Scholar
  49. Lesmeister C (2015) Mastering machine learning with R. Packt Publishing, BirminghamGoogle Scholar
  50. Li Y, Kamara F, Zhou G et al (2014) Urbanization increases Aedes albopictus larval habitats and accelerates mosquito development and survivorship. PLOS Negl Trop Dis 8(11):1–12CrossRefGoogle Scholar
  51. Liaw A, Wiener M (2002) Classification and regression by randomForest. RNews 2(3):18–22Google Scholar
  52. Linard C, Ponçon N, Fontenille D, Lambin EF (2009) Risk of malaria reemergence in southern France: testing scenarios with a multiagent simulation model. EcoHealth 6(1):135CrossRefGoogle Scholar
  53. Little E, Biehler D, Leisnham PT, Jordan R, Wilson S, LaDeau SL (2017) Socio-ecological mechanisms supporting high densities of Aedes albopictus (Diptera: Culicidae) in Baltimore, MD. J Med Entomol 54(5):1183–1192CrossRefGoogle Scholar
  54. McGarigal K, Cushman, SA, Ene E (2012) Fragstats v4: spatial pattern analysis program for categorical and continous maps. http://www.umass.edu/landeco/research/fragstats/fragstats.html
  55. Monaghan AJ, Sampson KM, Steinhoff DF et al (2018) The potential impacts of 21st century climatic and population changes on human exposure to the virus vector mosquito Aedes aegypti. Clim Change 146(3):487–500CrossRefGoogle Scholar
  56. Norris DE (2004) Mosquito-borne diseases as a consequence of land use change. EcoHealth 1(1):19–24CrossRefGoogle Scholar
  57. Obenauer JF, Andrew JT, Harris JB (2017) The importance of human population characteristics in modeling Aedes aegypti distributions and assessing risk of mosquito-borne infectious diseases. Trop Med Health 45(1):38CrossRefGoogle Scholar
  58. Osorio L, Garcia JA, Parra LG et al (2018) A scoping review on the field validation and implementation of rapid diagnostic tests for vector-borne and other infectious diseases of poverty in urban areas. Infect Dis Poverty 7(1):87CrossRefGoogle Scholar
  59. Ozdenerol E, Bialkowska-Jelinska E, Taff GN (2008) Locating suitable habitats for West Nile Virus-infected mosquitoes through association of environmental characteristics with infected mosquito locations: a case study in Shelby County, Tennessee. Int J Health Geogr 7(1):12CrossRefGoogle Scholar
  60. R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  61. Rael RC, Peterson AC, Ghersi BM, Childs J, Blum MJ (2016) Disturbance, reassembly, and disease risk in socioecological systems. EcoHealth 13(3):450–455CrossRefGoogle Scholar
  62. Reiner RC, Perkins TA, Barker CM et al (2013) A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970–2010. J R Soc Interface 10(81):20120921CrossRefGoogle Scholar
  63. Robertson C (2017) Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions. GeoJournal 82(2):397–414CrossRefGoogle Scholar
  64. Roiz D, Ruiz S, Soriguer R, Figuerola J (2015) Landscape effects on the presence, abundance and diversity of mosquitoes in mediterranean wetlands. PLoS ONE 10(6):1–17CrossRefGoogle Scholar
  65. Rosenberg R, Lindsey NP, Fischer M et al (2018) Vital signs: trends in reported vectorborne disease cases -United States and territories, 2004-2016. Morb Mortal Wkly Rep 67(17):496–501CrossRefGoogle Scholar
  66. Ruiz MO, Walker ED, Foster ES, Haramis LD, Kitron UD (2007) Association of West Nile virus illness and urban landscapes in Chicago and Detroit. Int J Health Geogr 6(1):10CrossRefGoogle Scholar
  67. Ruiz-Moreno D (2016) Assessing Chikungunya risk in a metropolitan area of Argentina through satellite images and mathematical models. BMC Infect Dis 16(1):49CrossRefGoogle Scholar
  68. Rydin Y, Bleahu A, Davies M et al (2012) Shaping cities for health: complexity and the planning of urban environments in the 21st century. Lancet 379:2079–2108CrossRefGoogle Scholar
  69. Shao G, Wu J (2008) On the accuracy of landscape pattern analysis using remote sensing data. Landscape Ecol 23:505–511CrossRefGoogle Scholar
  70. The World Bank (2015) GINI index (world bank estimate). The World Bank: 1–16. http://data.worldbank.org/indicator/SI.POV.GINI
  71. Townsend AT (2006) Ecological niche modeling and spatial patterns of disease transmission. Emerg Infect Dis 12(12):1822–1826CrossRefGoogle Scholar
  72. Townsend AT, Vieglais DA (2001) Predicting species invasions using ecological niche modeling: new approaches from bioinformatics attack a pressing problem: a new approach to ecological nich modeling, based on new tolls drawn from biodiversity informatics, is applied to the challenge of predicting potential species’ invasions. Bioscience 51(5):363–371CrossRefGoogle Scholar
  73. Tusting LS, Rek J, Arinaitwe E et al (2016) Why is malaria associated with poverty? Findings from a cohort study in rural Uganda. Infect Dis Poverty 5(1):78CrossRefGoogle Scholar
  74. Unlu I, Farajollahi A, Healy SP et al (2011) Area-wide management of Aedes albopictus: choice of study sites based on geospatial characteristics, socioeconomic factors and mosquito populations. Pest Manag Sci 67(8):965–974CrossRefGoogle Scholar
  75. Whiteman A, Delmelle E, Rapp T, Chen S, Chen G, Dulin M (2018) A novel sampling method to measure socio-ecological drivers of Aedes albopictus distribution in Charlotte, NC. Int J Environ Res Public Health 15(10):2179CrossRefGoogle Scholar
  76. Winkleby MA, Jatulis DE, Frank E, Fortmann SP (1992) Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health 82:816–820CrossRefGoogle Scholar
  77. World Health Organization (2018) Vector-borne disease. http://www.who.int/heli/risks/vectors/vector/en/. Accessed 20 Oct 2018
  78. World Health Organization (2018) Handbook for integrated vector management. http://apps.who.int/iris/bitstream/handle/10665/44768/9789241502801_eng.pdf. Accessed 20 Oct 2018
  79. Wu J (2014) Urban ecology and sustainability: the state-of-the-science and future directions. Landsc Urban Plan 125:209–221CrossRefGoogle Scholar
  80. Wu J, Jenerette GD, Buyantuyev A, Redman CL (2011) Quantifying spatiotemporal patterns of urbanization: the case of the two fastest growing metropolitan regions in the United States. Ecol Complex 8:1–8CrossRefGoogle Scholar
  81. Young BD, Yarie J, Verbyla D, Huettmann F, Chapin FS (2018) Machine learning for ecology and sustainable natural resource management. Springer Nature, SwitzerlandGoogle Scholar
  82. Young BD, Yarie J, Verbyla D, Huttmann F, Herrick K, Chapin FS (2017) Modeling and mapping forest diversity in the boreal forest of interior Alaska. Landscape Ecol 32:397CrossRefGoogle Scholar
  83. Younsi M, Chakroun M (2016) Does social capital determine health? Empirical evidence from MENA countries. Soc Sci J 53(3):371–379CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Shi Chen
    • 1
    • 5
    • 6
    Email author
  • Ari Whiteman
    • 2
  • Ang Li
    • 3
  • Tyler Rapp
    • 2
  • Eric Delmelle
    • 2
  • Gang Chen
    • 2
  • Cheryl L. Brown
    • 4
  • Patrick Robinson
    • 6
  • Maren J. Coffman
    • 6
  • Daniel Janies
    • 7
  • Michael Dulin
    • 1
    • 2
    • 6
  1. 1.Department of Public Health SciencesUniversity of North Carolina CharlotteCharlotteUSA
  2. 2.Department of Geography and Earth SciencesUniversity of North Carolina CharlotteCharlotteUSA
  3. 3.State Key Laboratory of Vegetation and Environmental ChangeChinese Academy of SciencesBeijingChina
  4. 4.Department of Political Science and Public AdministrationUniversity of North Carolina CharlotteCharlotteUSA
  5. 5.Data Science InitiativeUniversity of North Carolina CharlotteCharlotteUSA
  6. 6.Academy for Population Health Innovation, College of Health and Human ServicesUniversity of North Carolina at CharlotteCharlotteUSA
  7. 7.Department of BioinformaticsUniversity of North Carolina CharlotteCharlotteUSA

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