Increasing globalization has promoted the spread of exotic species, including disease vectors. Understanding the evolutionary processes involved in such colonizations is both of intrinsic biological interest and important to predict and mitigate future disease risks. The Aedes aegypti mosquito is a major vector of dengue, chikungunya and Zika, the worldwide spread of which has been facilitated by Ae. aegypti's adaption to human-modified environments. Understanding the evolutionary processes involved in this invasion requires characterization of the genetic make-up of the source population (s). The application of approximate Bayesian computation (ABC) to sequence data from four nuclear and one mitochondrial marker revealed that African populations of Ae. aegypti best fit a demographic model of lineage diversification, historical admixture and recent population structuring. As ancestral Ae. aegypti were dependent on forests, this population history is consistent with the effects of forest fragmentation and expansion driven by Pleistocene climatic change. Alternatively, or additionally, historical human movement across the continent may have facilitated their recent spread and mixing. ABC analysis and haplotype networks support earlier inferences of a single out-of-Africa colonization event, while a cline of decreasing genetic diversity indicates that Ae. aegypti moved first from Africa to the Americas and then to Asia. ABC analysis was unable to verify this colonization route, possibly because the genetic signal of admixture obscures the true colonization pathway. By increasing genetic diversity and forming novel allelic combinations, divergence and historical admixture within Africa could have provided the adaptive potential needed for the successful worldwide spread of Ae. aegypti.

The purpose of this research is to design, deploy and validate artificial intelligence (AI) algorithms operating on drone videos to enable a real time methodology for optimizing predictive mapping unknown, geographic locations (henceforth, geolocation) of potential, seasonal, Anopheles (gambiae, and funestus) larval habitats in an agro-village, epi-entomological, intervention site (Akonyibedo village) in Gulu District, Northern Uganda. Formulae are developed for classifying the drone swath, capture point, land cover in Akonyibedo agro-village. An AI algorithm is designed for constructing a smartphone application (app) in order to enable automatic detection of potential larval habitats from drone videos. The aim of this work is to enable scaling up to larger intervention sites (e.g., district level, sub-county) and then throughout entire Uganda. We demonstrate how capture point, stratifiable, drone swath coverage in Akonyibedo village can be accomplished employing temporal series of re-centered, real time, imaged, Anopheline, larval habitat, seasonal, map projections. We also define a remote methodology for detecting unknown, georeferenceable, capture point geolocations of potential, seasonal, breeding sites employing multispectral, wavelength, signature, reflux emissivities in a drone spectral library. Our results show that high-resolution drone imagery when processed employing state of the art AI algorithms can discriminate a profile of water bodies where Anopheles mosquitoes are most likely to breed (overall ground truth accuracy of 100%). Live, high definition, Anopheline larval habitat signature maps can be generated in real-time drone AI app on a smartphone or Apple device while the image is being captured or larvicidal application is taking place.

IntroductionYellow fever continues to be a problem in sub-Saharan Africa with repeated epidemics occurring. The mosquito Aedes bromeliae is a major vector of yellow fever, but it cannot be readily differentiated from its non-vector zoophilic sister species Ae. lilii using morphological characters. Genetic differences have been reported between anthropophilic Ae. bromeliae and zoophilic Ae. lilii and between forest and domestic populations. However, due to the application of different molecular markers and non-overlapping populations employed in previous studies, interpretation of species delimitation is unclear.Methodology/Principle FindingsDNA sequences were generated from specimens of Ae. simpsoni s.l. from the Republic of Benin, Tanzania and Uganda for two nuclear genes apolipophorin 2 (apoLp2) and cytochrome p450 (CYPJ92), the ribosomal internal transcribed spacer region (ITS) and the mitochondrial cytochrome c oxidase (COI) barcoding region. Nuclear genes apoLp2 and CYPJ92 were unable to differentiate between species Ae. bromeliae and Ae. lilii due to ancestral lineage sorting, while ITS sequence data provided clear topological separation on a phylogeny. The standard COI barcoding region was shown to be subject to species introgression and unable to clearly distinguish the two taxa. Here we present a reliable direct PCR-based method for differentiation of the vector species Ae. bromeliae from its isomorphic, sympatric and non-biomedically important sister taxon, Ae. lilii, based on the ITS region. Using molecular species verification, we describe novel immature habitats for Ae. lilii and report both sympatric and allopatric populations. Whereas only Ae. lilii is found in the Republic of Benin and only Ae. bromeliae in Tanzania, both species are sympatric in Uganda.Conclusions/SignificanceOur accurate identification method will allow informed distribution and detailed ecological studies that will facilitate assessment of arboviral disease risk and development of future targeted vector control.

This study provided important insights into new, real time, control measures at reducing larval, vector density [Macro Seek and Destroy (S&D) and blood parasite level [Micro S&D] in a malaria treated and suspected intervened population. Initially, this study employed a low-cost (< $1000) drone (DJI Phantom) for eco-geographically locating, water bodies including natural water bodies, irrigated rice paddies, cultivated swamps, ditches, ponds, and other geolocations, which are among the common breeding sites for Anopheles mosquitoes in Gulu district of Northern Uganda. Our hypothesis was that by integrating real time, scaled up, sentinel site, spectral signature, unmanned aerial vehicle (UAV) or drone imagery with satellite data using geospatial artificial intelligence [geo-AI] infused into an iOS application (app), a local, vector control officer could retrieve a ranked list of visually similar, breeding site, aquatic foci of An.gambiae s.l. arabiensis s.s. fuentsus s.s. mosquitoes, and their respective district-level, capture point, GPS indexed, centroid coordinates. We real time retrieved (hence, no lag time between seasonal, aquatic, Anopheles, larval habitat, mapping and treatment of foci) each georeferenced sentinel site signature which was subsequently archived in the drone dashboard spectral library using the smartphone app. Each georeferenced, UAV sensed, capture point was inspected using a mobile field team (i.e., trained local village residents led by a vector control officer) on the same day the habitats were geo-AI signature mapped, spatially forecasted and treated. A second hypothesis was that a real time, environmentally friendly, habitat alteration [i.e., Macro S&D] could reduce vector larval habitat density and blood parasite levels in treated and not suspected malaria patients at an entomological intervention site. A

Vibrio cholerae, a bacterium that causes cholera, poses a human health risk when consumed via untreated or contaminated water. Monthly investigations into the presence of V. cholerae from Lakes Albert, George and Victoria were conducted, with the goal being to examine the relationship between the occurrences of V. cholerae with various water quality parameters at fish landing sites in major water bodies in Uganda. The pH, temperature and electrical conductivity were measured at three fishing sites in each of the three study lakes; namely Gabba in Lake Victoria, Butiaba in Lake Albert and Kayanzi in Lake George. The pH values varied from 7.76 to 9.36 at Butiaba, 8.68 to 9.85 at Kayanzi and 6.6 to 9.88 at Ggaba. The temperature ranged from 17.9 to 32.3 °C at Butiaba, 22.5 to 29 °C at Kayanzi and 18.2 to 30.5 °C at Ggaba. The electrical conductivity ranged from 129.2 to 984 μS cm−1 at Butiaba, 658 to 1090 μS cm−1 at Kayanzi and 119 to 218 μS cm−1 at Ggaba, for Lakes Albert, George and Victoria, respectively. Enrichment techniques were used to detect culturable V. cholerae on TCBS culture media. Seventy‐five (75%) of the samples (n = 90) were positive for V. cholera. The occurrence of V. cholerae was positively associated with water quality parameters over the 10‐month period of study. Vibrio cholerae was more frequently detected during the dry season (warmer) than during the wet season. These study results suggest the investigated study lakes are natural reservoirs for V. cholerae.