ALBIS: integrated system for risk-based surveillance of invasive mosquito Aedes albopictus
- Published
- Accepted
- Subject Areas
- Computer Networks and Communications, Data Science, Spatial and Geographic Information Systems
- Keywords
- tiger mosquito, low cost sensors, open source, sensor observation service, istsos, LoRa, IoT
- Copyright
- © 2018 Antonovic et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. ALBIS: integrated system for risk-based surveillance of invasive mosquito Aedes albopictus. PeerJ Preprints 6:e27251v1 https://doi.org/10.7287/peerj.preprints.27251v1
Abstract
According to predictions bases on a climate-driven large-scale model the areas surrounding Lake Léman and, to some extent, the Swiss Plateau are suitable for the spread of Ae. albopictus North of the Alps, while other areas in Switzerland (e.g., the city of Zürich) seem currently too cold in winter for the survival of eggs. However, this model does not take into account particular micro-climate conditions in urban areas where the specie thrives. Climate conditions in urban micro-habitats (in particular catch basins) increase the probability of the survival of diapausing eggs in the winter season favoring the colonization of new cities that were thought to be too cold for the survival of the eggs. Therefore, there is an urgent need for appropriate monitoring tools and risk-based surveillance of Ae. albopictus populations. In 2018 a multidisciplinary group of researchers from the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) has joined launching the project ALBIS (Albopictus Integrated System). The designed system focuses on the monitoring of urban catch basins, primarily on micro-climate environmental sensing, data transmission, data acquisition and data dissemination. The gathered data are the input for an empirical machine learning model for the prediction of spatial and temporal distribution of the Ae. albopictus. The first real time monitoring tests are in progress in the pilot area in the city of Lugano in the Canton Ticino. Fully functional prototypes have been engineered by the Institute of Earth Science in collaboration with a local electronics manufacturer (TECinvent) combined with the Open Source istSOS OGC Sensor Observation Service software for data acquisition and dissemination, and in the first tests cases have demonstrated good quality in terms of energy efficiency, data quality and data transmission reliability. The first results demonstrated that temperature in catch basins can be different from outside temperature that is detected by traditional terrain measures: in February 2018 during a period of cold air temperature in Canton Ticino of down to -8°C, the prototype sensor monitoring the catch basins' wall surface shows temperatures up to 6°C higher. Considering that one of the Ae. albopictus establishment thresholds is to have a mean January temperature of >0°C to allow egg overwintering, taking into account this micro-climate environments could lead to more realistic predictions.
Author Comment
This submission is intended for the OGRS'2018 Collection