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

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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.

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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

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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.

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Authors and Affiliations

  1. Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, NC, 28223, USA

    Shi Chen & Michael Dulin

  2. Department of Geography and Earth Sciences, University of North Carolina Charlotte, Charlotte, NC, 28223, USA

    Ari Whiteman, Tyler Rapp, Eric Delmelle, Gang Chen & Michael Dulin

  3. State Key Laboratory of Vegetation and Environmental Change, Chinese Academy of Sciences, Beijing, 100093, China

    Ang Li

  4. Department of Political Science and Public Administration, University of North Carolina Charlotte, Charlotte, NC, 28223, USA

    Cheryl L. Brown

  5. Data Science Initiative, University of North Carolina Charlotte, Charlotte, NC, 28223, USA

    Shi Chen

  6. Academy for Population Health Innovation, College of Health and Human Services, University of North Carolina at Charlotte, Charlotte, NC, 28211, USA

    Shi Chen, Patrick Robinson, Maren J. Coffman & Michael Dulin

  7. Department of Bioinformatics, University of North Carolina Charlotte, Charlotte, NC, 28223, USA

    Daniel Janies

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  1. Shi Chen
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Chen, S., Whiteman, A., Li, A. et al. An operational machine learning approach to predict mosquito abundance based on socioeconomic and landscape patterns. Landscape Ecol 34, 1295–1311 (2019). https://doi.org/10.1007/s10980-019-00839-2

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