Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

BackgroundPlasmodium vivax transmission in Thailand has been substantially reduced over the past 10 years, yet it remains highly endemic along international borders. Understanding the genetic relationship of residual parasite populations can help track the origins of the parasites that are reintroduced into malaria-free regions within the country.Methodology/ResultsA total of 127 P. vivax isolates were genotyped from two western provinces (Tak and Kanchanaburi) and one eastern province (Ubon Ratchathani) of Thailand using 10 microsatellite markers. Genetic diversity was high, but recent clonal expansion was detected in all three provinces. Substantial population structure and genetic differentiation of parasites among provinces suggest limited gene flow among these sites. There was no haplotype sharing among the three sites, and a reduced panel of four microsatellite markers was sufficient to assign the parasites to their provincial origins.Conclusion/SignificanceSignificant parasite genetic differentiation between provinces shows successful interruption of parasite spread within Thailand, but high diversity along international borders implies a substantial parasite population size in these regions. The provincial origin of P. vivax cases can be reliably determined by genotyping four microsatellite markers, which should be useful for monitoring parasite reintroduction after malaria elimination.

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Plasmodium vivax resistance to chloroquine (CQ) was first reported over 60 years ago. Here we analyzed sequence variations in the multidrug resistance 1 gene (Pvmdr1), a putative molecular marker for P. vivax CQ resistance, in field isolates collected from three sites in Thailand during 2013-2016. Several single nucleotide polymorphisms previously implicated in reduced CQ sensitivity were found. These genetic variations encode amino acids in the two nucleotide-binding domains as well as the transmembrane domains of the protein. The high level of genetic diversity of Pvmdr1 provides insights into the evolutionary history of this gene. Specifically, there was little evidence of positive selection at amino acid F1076L in global isolates to be promoted as a possible marker for CQ resistance. Population genetic analysis clearly divided the parasites into eastern and western populations, which is consistent with their geographical separation by the central malaria-free area of Thailand. With CQ-primaquine remaining as the frontline treatment for vivax malaria in all regions of Thailand, such a population subdivision could be shaped and affected by the current drugs for P. falciparum since mixed P. falciparum/P. vivax infections often occur in this region.