Machine learning assists the classification of reports by citizens on disease-carrying mosquitoes

Mosquito Alert (www.mosquitoalert.com/en) is an expert-validated citizen science platform for tracking and controlling disease-carrying mosquitoes. Citizens download a free app and use their phones to send reports of presumed sightings of two world-wide disease vector mosquito species (the Asian Tiger and the Yellow Fever mosquito). These reports are then supervised by a team of entomologists and, once validated, added to a database. As the platform prepares to scale to much larger geographical areas and user bases, the expert validation by entomologists becomes the main bottleneck. In this paper we describe the use of machine learning on the citizen reports to automatically validate a fraction of them, therefore allowing the entomologists either to deal with larger report streams or to concentrate on those that are more strategic, such as reports from new areas (so that early warning protocols are activated) or from areas with high epidemiological risks (so that control actions to reduce mosquito populations are activated). The current prototype flags a third of the reports as “almost certainly positive” with high confidence. It is currently being integrated into the main workflow of the Mosquito Alert platform.Postprint (published version