Search

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used for proteomic fingerprinting and taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method to complement other well-established techniques for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens., Competing Interests: The authors have declared that no competing interests exist.

Competing Interests: The authors have declared that no competing interests exist.

Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales., Competing Interests: The authors have declared that no competing interests exist.

From 2002-2005, Panama experienced a malaria epidemic that has been associated with El Niño Southern Oscillation weather patterns, decreased funding for malaria control, and landscape modification. Case numbers quickly decreased afterward, and Panama is now in the pre-elimination stage of malaria eradication. To achieve this new goal, the characterization of epidemiological risk factors, foci of transmission, and important anopheline vectors is needed. Of the 24,681 reported cases in these analyses (2000-2014), ~62% occurred in epidemic years and ~44% in indigenous comarcas (5.9% of Panama's population). Sub-analyses comparing overall numbers of cases in epidemic and non-epidemic years identified females, comarcas and some 5-year age categories as those disproportionately affected by malaria during epidemic years. Annual parasites indices (APIs; number of cases per 1,000 persons) for Plasmodium vivax were higher in comarcas compared to provinces for all study years, though P. falciparum APIs were only higher in comarcas during epidemic years. Interestingly, two comarcas report increasing numbers of cases annually, despite national annual decreases. Inclusion of these comarcas within identified foci of malaria transmission confirmed their roles in continued transmission. Comparison of species distribution models for two important anophelines with Plasmodium case distribution suggest An. albimanus is the primary malaria vector in Panama, confirmed by identification of nine P. vivax-infected specimen pools. Future malaria eradication strategies in Panama should focus on indigenous comarcas and include both active surveillance for cases and comprehensive anopheline vector surveys.