Mosquito-Borne Diseases (MBDs) are known to be more prevalent in the tropics, and yet, in the last two decades, they are spreading to many other countries, especially in Europe. The set (volume) of environmental, meteorological and other spatio-temporally variable parameters affecting mosquito abundance makes the modeling and prediction tasks quite challenging. Up to now, mosquito abundance prediction problems were addressed with ad-hoc area-specific and genus-tailored approaches. We propose and develop MAMOTH, a generic and accurate Machine Learning model that predicts mosquito abundances for the upcoming period (the Mean Absolute Error of the predictions do not deviate more than $14\%$). The designed model relies on satellite Earth Observation and other in-situ geo-spatial data to tackle the problem. MAMOTH is not site- nor mosquito genus-dependent; thus, it can be easily replicated and applied to multiple cases without any special parametrization. The model was applied to different mosquito genus and species Culex spp. as potential vectors for West Nile Virus, Anopheles spp. for Malaria and Aedes albopictus for Zika/Chikungunya/Dengue) and in different areas of interest (Italy, Serbia, France, Germany). The results show that the model performs accurately and consistently for all case studies. Additionally, the evaluation of different cases, with the model using the same principles, provides an opportunity for multi-case and multi-scope comparative studies.

Mosquito-borne diseases have been spreading across Europe over the past two decades, with climate change contributing to this spread. Temperature and precipitation are key factors in a mosquito’s life cycle, and are greatly affected by climate change. Using a machine learning framework, Earth Observation data, and future climate projections of temperature and precipitation, this work studies three different cases (Veneto region in Italy, Upper Rhine Valley in Germany and Pancevo, Serbia) and focuses on (i) evaluating the impact of climate factors on mosquito abundance and (ii) long-term forecasting of mosquito abundance based on EURO-CORDEX future climate projections under different Representative Concentration Pathways (RCPs) scenarios. The study shows that increases in precipitation and temperature are directly linked to increased mosquito abundance, with temperature being the main driving factor. Additionally, as the climatic conditions become more extreme, meaning higher variance, the mosquito abundance increases. Moreover, we show that in the upcoming decades mosquito abundance is expected to increase. In the worst-case scenario (RCP8.5) Serbia will face a 10% increase, Italy around a 40% increase, and Germany will reach almost a 200% increase by 2100, relative to the decade 2010–2020. However, in terms of absolute numbers both in Italy and Germany, the expected increase is similar. An interesting finding is that either strong (RCP2.6) or moderate mitigation actions (RCP4.5) against greenhouse gas concentration lead to similar levels of future mosquito abundance, as opposed to no mitigation action at all (RCP8.5), which is projected to lead to high mosquito abundance for all cases studied.

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. 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. 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%.  It’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)