A Deep Early Warning System of Mosquito Borne Diseases using Earth Observational Data
Authors: Tsantalidou Argyro , Tsaprailis Konstantinos , Arvanitakis George , Fornasiero Diletta , Wohlgemuth Daniel , Petric Dusan , Kontoes Charalampos 
Published: February 2023

Mosquito-borne diseases (MBDs) have been spreading across many countries including Europe over the past two decades, causing thousands of deaths annually. They are transmitted through the bites of infectious mosquitoes. Environmental, meteorological and other spatio-temporally variables affect the mosquito abundance (MA), and thus affect the circulation of the MBDs in the community. So an early warning system of MA based on these parameters could serve as a warning for the upcoming MBDs incidence.&#160;We propose Deep-MAMOTH, a data driven, generic and accurate early warning system for predicting MA in the upcoming period, based on earth observational (EO) environmental data and optionally in-situ entomological data. Deep-MAMOTH can be easily replicated and applied to multiple areas of interest without any special parametrization.The Deep-MAMOTH pipeline collects EO information from various data sources (temperature, rainfall, vegetation, distance from coast, elevation, etc.) and in-situ entomological data for each area of interest. Then, there is a feature extraction phase that combines the previous collected information to more complex features, and finally this data is fed into a Deep Neural Network responsible to capture the relationship between the above mentioned features and the MA, delivering a MA risk class ordered from 0 to 9 for the upcoming period (e.g. 15 days). The pipeline provides a standardized way to predict MA without depending on the area of interest or the mosquito genus and can be modified to predict the actual MA instead of a risk class. However, risk classes help to better propagate the severity of the situation.Two versions of Deep-MAMOTH were implemented, the first one is using recently collected entomological information in order to produce predictions (i.e. mosquitoes collected 1 week ago). The other version works when there is no recently collected entomological information for the area of interest. The latter version is expected to perform worse than the first one, but gives us the capability to produce predictions anywhere on earth without the need of recently collected entomological data.&#160;We applied Deep-MAMOTH in Veneto (Italy), in Upper Rhine region (Germany), and the Vojvodina region (Serbia) regarding the Culex spp. genus mosquito. The results are promising as Deep-MAMOTH in Italy achieves a mean absolute error (MAE) of 1.27 classes with the percentage of predictions that deviate at most 3 classes (e3) from the actual one reaching up to 95%. In Serbia MAE is 1.77 classes, with e3 equal to 88% and finally for Germany MAE is 0.92 classes and e3 equal to 94%.&#160;&#160;It&#8217;s worth mentioning that prediction performance in the version of Deep-MAMOTH without using entomological information remains promising. MAE in Italy was increased only by 0.02 and in Germany by 0.1, with e3 remaining at the same level in both cases, while in Serbia MAE increased by 0.2 with e3 decreasing by 8%. We conclude that the prediction of MA from EO data can be accurate with or without recently collected entomological data.Acknowledgment:This research has been co-financed by the ERD Fund of the EU and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, RESEARCH-CREATE-INNOVATE(project code:T2EDK-02070)