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Twenty-seven villages were selected in southwest Burkina Faso to implement new vector control strategies in addition to long lasting insecticidal nets (LLINs) through a Randomized Controlled Trial (RCT). We conducted entomological surveys in the villages during the dry cold season (January 2017), dry hot season (March 2017) and rainy season (June 2017) to describe malaria vectors bionomics, insecticide resistance and transmission prior to this trial. We carried out hourly catches (from 17:00 to 09:00) inside and outside 4 houses in each village using the Human Landing Catch technique. Mosquitoes were identified using morphological taxonomic keys. Specimens belonging to the Anopheles gambiae complex and Anopheles funestus group were identified using molecular techniques as well as detection of Plasmodium falciparum infection and insecticide resistance target-site mutations. Eight Anopheles species were detected in the area. Anopheles funestus s.s was the main vector during the dry cold season. It was replaced by Anopheles coluzzii during the dry hot season whereas An. coluzzii and An. gambiae s.s. were the dominant species during the rainy season. Species composition of the Anopheles population varied significantly among seasons. All insecticide resistance mechanisms (kdr-w, kdr-e and ace-1 target site mutations) investigated were found in each members of the An. gambiae complex but at different frequencies. We observed early and late biting phenotypes in the main malaria vector species. Entomological inoculation rates were 2.61, 2.67 and 11.25 infected bites per human per month during dry cold season, dry hot season and rainy season, respectively. The entomological indicators of malaria transmission were high despite the universal coverage with LLINs. We detected early and late biting phenotypes in the main malaria vector species as well as physiological insecticide resistance mechanisms. These data will be used to evaluate the impact of complementary tools to LLINs in an upcoming RCT.

The global population at risk from mosquito-borne diseases—including dengue, yellow fever, chikungunya and Zika—is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus . The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally. Statistical mapping techniques provide insights into the spread of two key arbovirus vectors in Europe and the United States, and predict the future distributions of both mosquitoes in response to accelerating urbanization, connectivity and climate change.

Key aspects of 36 mosquito-borne arboviruses indigenous to Africa are summarized, including lesser or poorly-known viruses which, like Zika, may have the potential to escape current sylvatic cycling to achieve greater geographical distribution and medical importance. Major vectors are indicated as well as reservoir hosts, where known. A series of current and future risk factors is addressed. It is apparent that Africa has been the source of most of the major mosquito-borne viruses of medical importance that currently constitute serious global public health threats, but that there are several other viruses with potential for international challenge. The conclusion reached is that increased human population growth in decades ahead coupled with increased international travel and trade is likely to sustain and increase the threat of further geographical spread of current and new arboviral disease.

Background Estimates of the geographical distribution of Culex mosquitoes in the Americas have been limited to state and provincial levels in the United States and Canada and based on data from the 1980s. Since these estimates were made, there have been many more documented observations of mosquitoes and new methods have been developed for species distribution modeling. Moreover, mosquito distributions are affected by environmental conditions, which have changed since the 1980s. This calls for updated estimates of these distributions to understand the risk of emerging and re-emerging mosquito-borne diseases. Methods We used contemporary mosquito data, environmental drivers, and a machine learning ecological niche model to create updated estimates of the geographical range of seven predominant Culex species across North America and South America: Culex erraticus , Culex nigripalpus , Culex pipiens , Culex quinquefasciatus , Culex restuans , Culex salinarius , and Culex tarsalis . Results We found that Culex mosquito species differ in their geographical range. Each Culex species is sensitive to both natural and human-influenced environmental factors, especially climate and land cover type. Some prefer urban environments instead of rural ones, and some are limited to tropical or humid areas. Many are found throughout the Central Plains of the USA. Conclusions Our updated contemporary Culex distribution maps may be used to assess mosquito-borne disease risk. It is critical to understand the current geographical distributions of these important disease vectors and the key environmental predictors structuring their distributions not only to assess current risk, but also to understand how they will respond to climate change. Since the environmental predictors structuring the geographical distribution of mosquito species varied, we hypothesize that each species may have a different response to climate change. Graphical abstract

Malaria still has a devastating impact on public health and welfare in Cameroon. Despite the increasing number of studies conducted on disease prevalence, transmission patterns or treatment, there are to date, not enough studies summarising findings from previous works in order to identify gaps in knowledge and areas of interest where further evidence is needed to drive malaria elimination efforts. The present study seeks to address these gaps by providing a review of studies conducted so far on malaria in Cameroon since the 1940s to date. Over 250 scientific publications were consulted for this purpose. Although there has been increased scale-up of vector control interventions which significantly reduced the morbidity and mortality to malaria across the country from a prevalence of 41% of the population reporting at least one malaria case episode in 2000 to a prevalence of 24% in 2017, the situation is not yet under control. There is a high variability in disease endemicity between epidemiological settings with prevalence of Plasmodium parasitaemia varying from 7 to 85% in children aged 6 months to 15 years after long-lasting insecticidal nets (LLINs) scale-up. Four species of Plasmodium have been recorded across the country: Plasmodium falciparum , P. malariae , P. ovale and P. vivax . Several primate-infecting Plasmodium spp. are also circulating in Cameroon. A decline of artemisinin-based combinations therapeutic efficacy from 97% in 2006 to 90% in 2016 have been reported. Several mutations in the P. falciparum chloroquine resistance ( Pfcrt) and P. falciparum multidrug resistance 1 ( Pfmdr1 ) genes conferring resistance to either 4-amino-quinoleine, mefloquine, halofanthrine and quinine have been documented. Mutations in the Pfdhfr and Pfdhps genes involved in sulfadoxine-pyrimethamine are also on the rise. No mutation associated with artemisinin resistance has been recorded. Sixteen anopheline species contribute to malaria parasite transmission with six recognized as major vectors: An. gambiae , An. coluzzii , An. arabiensis , An. funestus , An. nili and An. moucheti. Studies conducted so far, indicated rapid expansion of DDT, pyrethroid and carbamate resistance in An. gambiae , An. coluzzii , An. arabiensis and An. funestus threatening the performance of LLINs. This review highlights the complex situation of malaria in Cameroon and the need to urgently implement and reinforce integrated control strategies in different epidemiological settings, as part of the substantial efforts to consolidate gains and advance towards malaria elimination in the country.

Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.

Image-based automatic classification of vector mosquitoes has been investigated for decades for its practical applications such as early detection of potential mosquitoes-borne diseases. However, the classification accuracy of previous approaches has never been close to human experts’ and often images of mosquitoes with certain postures and body parts, such as flatbed wings, are required to achieve good classification performance. Deep convolutional neural networks (DCNNs) are state-of-the-art approach to extracting visual features and classifying objects, and, hence, there exists great interest in applying DCNNs for the classification of vector mosquitoes from easy-to-acquire images. In this study, we investigated the capability of state-of-the-art deep learning models in classifying mosquito species having high inter-species similarity and intra-species variations. Since no off-the-shelf dataset was available capturing the variability of typical field-captured mosquitoes, we constructed a dataset with about 3,600 images of 8 mosquito species with various postures and deformation conditions. To further address data scarcity problems, we investigated the feasibility of transferring general features learned from generic dataset to the mosquito classification. Our result demonstrated that more than 97% classification accuracy can be achieved by fine-tuning general features if proper data augmentation techniques are applied together. Further, we analyzed how this high classification accuracy can be achieved by visualizing discriminative regions used by deep learning models. Our results showed that deep learning models exploit morphological features similar to those used by human experts.

With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

The emergence of the Asian invasive malaria vector, Anopheles stephensi , has been identified in Khartoum, the capital city of Sudan. This is the first report that confirms the geographical expansion of this urban mosquito into Central Sudan. We urgently recommend the launch of a national entomological survey to determine the distribution of this invasive disease vector and to generate essential information about its bionomics and susceptibility to available malaria control measures. Graphical Abstract

Mosquitoes collected from mid-December 2020 to early March 2021 from hibernacula in northeastern Germany, a region of West Nile virus (WNV) activity since 2018, were examined for WNV-RNA. Among the 6101 mosquitoes tested in 722 pools of up to 12 specimens, one pool of 10 Culex pipiens complex mosquitoes collected in early March 2021 in the cellar of a medieval castle in Rosslau, federal state of Saxony-Anhalt, tested positive. Subsequent mosquito DNA analysis produced Culex pipiens biotype pipiens. The pool homogenate remaining after nucleic acid extraction failed to grow the virus on Vero and C6/36 cells. Sequencing of the viral NS2B-NS3 coding region, however, demonstrated high homology with virus strains previously collected in Germany, e.g., from humans, birds, and mosquitoes, which have been designated the East German WNV clade. The finding confirms the expectation that WNV can overwinter in mosquitoes in Germany, facilitating an early start to the natural transmission season in the subsequent year. On the other hand, the calculated low infection prevalence of 0.016–0.20%, depending on whether one or twelve of the mosquitoes in the positive pool was/were infected, indicates a slow epidemic progress and mirrors the still-hypoendemic situation in Germany. In any case, local overwintering of the virus in mosquitoes suggests its long-term persistence and an enduring public health issue.