Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

Abstract

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists' mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists' sampling effort is focused more in Barcelona's lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city's upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists' sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Keywords: Aedes albopictus; Citizen science; Mosquito-borne diseases; Social inequality; Vector control; Vector surveillance.

Fig. 1

Map of Barcelona municipality showing census tracts colored by yearly net mean income per consumption unit. Cartographic image created by the authors from the the INE’s Digital Cartography Files using R 4.4.1 with ggplot2 3.4.4 and ggspatial 1.1.9.

Fig. 2

Maps of the census tracts of the municipality of Barcelona showing: (a) locations of adult mosquito and mosquito bite reports sent through Mosquito Alert during 2014-23, a darker shade of blue indicates a higher concentration of reports; (b) locations of catch basin drains in which mosquito activity was detected by ASPB during 2019-23, with colors indicating whether any (red) or no (blue) adult mosquito or mosquito bite report was sent from within 200 m of the drain during same year in which the ASPB detected the activity; (c) locations of adult mosquito traps from which data was collected between 2018 and 2022. Cartographic image created by the authors from the the INE’s Digital Cartography Files using R 4.4.1 with tmap 3.3.4.

Fig. 3

Conditional effects plot of the relationship between mean income per consumption unit (in euros) and predicted probability of citizen scientists reporting adult mosquitoes or mosquito bites in the Mosquito Alert General Participation Model, with all other variables held at their means.

Fig. 4

Conditional effects plot of the relationship between mean income per consumption unit (in euros) and predicted probability during a given year of citizen scientists reporting adult mosquitoes or mosquito bites within 200 m of a catch basin drain with known mosquito activity that year in the Active Catch Basin Drain Participation Model, with all other variables held at their means.

Fig. 5

Top: Conditional effects plot of the relationship between mean income per consumption unit (in euros) and predicted Ae. albopictus probability in the Mosquito Alert Vector Model, with all other variables are held at their means. Bottom: Conditional effects plot of the relationship between mean income per consumption unit (in euros) and predicted Ae. albopictus counts in the Mosquito Trap Vector Model, with all other variables are held at their means. Dotted vertical line in both plots indicates maximum income modelled in the Mosquito Trap Vector Model.

Fig. 6

Maps of Barcelona municipality showing: (a) predicted probability of Ae. albopictus presence based on the Mosquito Alert Vector Model controlling for sampling effort, and (b) differences between these predictions and those made without controlling for sampling effort. The differences are calculated as predictions controlling for sampling effort minus those not controlling for sampling effort, so values above zero (oranges and reds) represent zones where probabilities would be under-predicted without controlling for sampling effort, and values below zero (greens and blues) represent zones where those probabilities would be over-predicted. Cartographic image created by the authors from the INE’s Digital Cartography Files using R 4.4.1 with ggplot2 3.4.4 and ggspatial 1.1.9.