Predicting the age of field Anopheles mosquitoes using mass spectrometry and deep learning.

Mosquito-borne diseases like malaria are rising globally, and improved mosquito vector surveillance is needed. Survival of Anopheles mosquitoes is key for epidemiological monitoring of malaria transmission and evaluation of vector control strategies targeting mosquito longevity, as the risk of pathogen transmission increases with mosquito age. However, the available tools to estimate field mosquito age are often approximate and time-consuming. Here, we show a rapid method that combines matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry with deep learning for mosquito age prediction. Using 2763 mass spectra from the head, legs, and thorax of 251 field-collected Anopheles arabiensis mosquitoes, we developed deep learning models that achieved a best mean absolute error of 1.74 days. We also demonstrate consistent performance at two ecological sites in Senegal, supported by age-related protein changes. Our approach is promising for malaria control and the field of vector biology, benefiting other disease vectors like Aedes mosquitoes.

Distribution and dynamics of Anopheles gambiae s.l. larval habitats in three Senegalese cities with high urban malaria incidence.

Urban malaria has become a challenge for most African countries due to urbanization, with increasing population sizes, overcrowding, and movement into cities from rural localities. The rapid expansion of cities with inappropriate water drainage systems, abundance of water storage habitats, coupled with recurrent flooding represents a concern for water-associated vector borne diseases, including malaria. This situation could threaten progress made towards malaria elimination in sub-Saharan countries, including Senegal, where urban malaria has presented as a threat to national elimination gains. To assess drivers of urban malaria in Senegal, a 5-month study was carried out from August to December 2019 in three major urban areas and hotspots for malaria incidence (Diourbel, Touba, and Kaolack) including the rainy season (August-October) and partly dry season (November-December). The aim was to characterize malaria vector larval habitats, vector dynamics across both seasons, and to identify the primary eco- environmental entomological factors contributing to observed urban malaria transmission. A total of 145 Anopheles larval habitats were found, mapped, and monitored monthly. This included 32 in Diourbel, 83 in Touba, and 30 in Kaolack. The number of larval habitats fluctuated seasonally, with a decrease during the dry season. In Diourbel, 22 of the 32 monitored larval habitats (68.75%) were dried out by December and considered temporary, while the remaining 10 (31.25%) were classified as permanent. In the city of Touba 28 (33.73%) were temporary habitats, and of those 57%, 71% and 100% dried up respectively by October, November, and December. However, 55 (66.27%) habitats were permanent water storage basins which persisted throughout the study. In Kaolack, 12 (40%) permanent and 18 (60%) temporary Anopheles larval habitats were found and monitored during the study. Three malaria vectors (An. arabiensis, An. pharoensis and An. funestus s.l.) were found across the surveyed larval habitats, and An. arabiensis was found in all three cities and was the only species found in the city of Diourbel, while An. arabiensis, An. pharoensis, and An. funestus s.l. were detected in the cities of Touba and Kaolack. The spatiotemporal observations of immature malaria vectors in Senegal provide evidence of permanent productive malaria vector larval habitats year-round in three major urban centers in Senegal, which may be driving high urban malaria incidence. This study aimed to assess the presence and type of anopheline larvae habitats in urban areas. The preliminary data will better inform subsequent detailed additional studies and seasonally appropriate, cost-effective, and sustainable larval source management (LSM) strategies by the National Malaria Control Programme (NMCP).