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This study is the first in the United States to characterize mosquito vector abundance and diversity on commercial swine farms. Between June and October 2024, bi-weekly mosquito sampling was conducted on ten farms (five sow farms and five wean-to-market farms (WTM)) in southeast Iowa using BG-Pro light traps (Biogents AG, Regensburg, Germany). Traps were placed both outdoors, near water bodies (lagoons and ponds) and vegetation (crop fields, trees, shrubs, and thick grasses), and indoors (barns), trapping a total of 11,343 female mosquitoes. Overall, 55.8% were trapped on sow farms and 44.2% on WTM farms. Overall female abundance varied significantly across time periods ( P  < 0.01), peaking in July and August. Outdoor female abundance was significantly higher than indoors on sow farms ( P  < 0.01), whereas no statistically significant difference was observed on WTM farms. Nineteen mosquito species were identified, predominantly belonging to three genera: Culex , Aedes , and Anopheles , with significant differences in community composition across habitats, as indicated by alpha and beta diversity metrics ( P  < 0.05). These findings provide a critical baseline for understanding vector dynamics in swine production systems and highlight the need for sustained mosquito surveillance to inform arbovirus risk mitigation strategies.

The inability to distinguish between species can be a serious problem in groups responsible for pathogen transmission. Culicoides biting midges transmit many pathogenic agents infecting wildlife and livestock. In North America, the C. variipennis species complex contains three currently recognized species, only one of which is a known vector, but limited species-specific characters have hindered vector surveillance. Here, genomic data were used to investigate population structure and genetic differentiation within this species complex. Single nucleotide polymorphism data were generated for 206 individuals originating from 17 locations throughout the United States and Canada. Clustering analyses suggest the occurrence of two additional cryptic species within this complex. All five species were significantly differentiated in both sympatry and allopatry. Evidence of hybridization was detected in three different species pairings indicating incomplete reproductive isolation. Additionally, COI sequences were used to identify the hybrid parentage of these individuals, which illuminated discordance between the divergence of the mitochondrial and nuclear datasets.

West Nile virus (WNV)-a mosquito-borne arbovirus-entered the USA through New York City in 1999 and spread to the contiguous USA within three years while transitioning from epidemic outbreaks to endemic transmission. The virus is transmitted by vector competent mosquitoes and maintained in the avian populations. WNV spatial distribution is mainly determined by the movement of residential and migratory avian populations. We developed an individual-level heterogeneous network framework across the USA with the goal of understanding the long-range spatial distribution of WNV. To this end, we proposed three distance dispersal kernels model: 1) exponential-short-range dispersal, 2) power-law-long-range dispersal in all directions, and 3) power-law biased by flyway direction -long-range dispersal only along established migratory routes. To select the appropriate dispersal kernel we used the human case data and adopted a model selection framework based on approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). From estimated parameters, we find that the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypothesis of long-range WNV transmission is mainly along the migratory bird flyways. Through extensive simulation from 2014 to 2016, we proposed and tested hypothetical mitigation strategies and found that mosquito population reduction in the infected states and neighboring states is potentially cost-effective.

Background Japanese encephalitis is an emerging zoonotic disease caused by the Japanese encephalitis virus (JEV), transmitted primarily by mosquitoes of the Culex species. Amid the recent geographical expansion of JEV into Mainland Australia and the dramatic increase in research output, here we provide an update to our 2018 systematic review and meta-analysis, by appraising the scientific literature published from 2016 through 2023 and quantitatively summarizing the data from this update and the 2018 systematic review meta-analysis on vector competence for JEV. Methods A systematic review of the literature on JEV vector and host competence, published from 2016 through 2023, was performed. Bibliographic databases, PubMed, Scopus, Web of Science, and the Armed Forces Pest Management Board website were searched for relevant literature. Records were screened for relevance for vector competence, specifically: infection rate, dissemination rate, and transmission rate. To estimate the overall and subgroup effect sizes for each mosquito species, random-effects meta-analysis models were utilized. Meta-regression models were fit to evaluate the association between a priori variables—such as mosquito subfamily/tribe, routes of JEV administration for mosquito infection, incubation length, incubation temperatures, and diagnostic methods for JEV detection—and the outcomes of interest. Results This study update includes 74 new reports, identifying 9–12 additional mosquito species as competent for JEV, depending on the specific outcome assessed. The overall JEV infection, dissemination, and transmission rates across all species and studies were 45.4% (95% confidence interval (CI) 35.9–55.2%), 41.2% (95% CI 29.7–53.7%), and 22.7% (95% CI 14.6–33.4%), respectively. Among the subfamilies/tribes, Culicini had the highest infection (51.9%; 95% CI 39.2–64.4%) and transmission (27.8%; 95% CI 16.5–43.1%) rates. Meta-regressions showed mosquito subfamily/tribe was consistently associated with all the outcomes of interest, although the heterogeneity ( I 2 ) between studies remained consistently high ( I 2  > 83.47). Conclusions The information presented in this study provides a quantitative summary update on vector competence for JEV. Vector competence data are necessary for risk assessment models, the development of mosquito and virus surveillance programs, and effective prevention and control strategies in regions currently affected by JEV and those at risk of incursion. Graphical abstract

Biting midges of the genus Culicoides transmit disease-causing agents resulting in a significant economic impact on livestock industries in many parts of the world. Localized control efforts, such as removal of larval habitat or pesticide application, can be logistically difficult, expensive and ineffective if not instituted and maintained properly. With these limitations, a population-level approach to the management of Culicoides midges should be investigated as a means to replace or supplement existing control strategies. Next-generation control methods such as Wolbachia - and genetic-based population suppression and replacement are being investigated in several vector species. Here we assess the feasibility and applicability of these approaches for use against biting midges. We also discuss the technical and logistical hurdles needing to be addressed for each method to be successful, as well as emphasize the importance of addressing community engagement and involving stakeholders in the investigation and development of these approaches. Graphical Abstract

During the last 20 years, the epidemiology of Japanese encephalitis virus (JEV) has changed significantly in its endemic regions due to the gradual displacement of the previously dominant genotype III (GIII) with clade b of GI (GI-b). Whilst there is only limited genetic difference distinguishing the two GI clades (GI-a and GI-b), GI-b has shown a significantly wider and more rapid dispersal pattern in several regions in Asia than the GI-a clade, which remains restricted in its geographic distribution since its emergence. Although previously published molecular epidemiological evidence has shown distinct phylodynamic patterns, characterization of the two GI clades has only been limited to in vitro studies. In this study, Culex quinquefasciatus, a known competent JEV mosquito vector species, was orally challenged with three JEV strains each representing GI-a, GI-b, and GIII, respectively. Infection and dissemination were determined based on the detection of infectious viruses in homogenized mosquitoes. Detection of JEV RNA in mosquito saliva at 14 days post infection indicated that Cx. quinquefasciatus can be a competent vector species for both GI and GIII strains. Significantly higher infection rates in mosquitoes exposed to the GI-b and GIII strains than the GI-a strain suggest infectivity in arthropod vectors may lead to the selective advantage of previously and currently dominant genotypes. It could thus play a role in enzootic transmission cycles for the maintenance of JEV if this virus were ever to be introduced into North America.

Epizootic hemorrhagic disease (EHD) is an insect-transmitted viral disease of wild and domestic ruminants. It was first described following a 1955 epizootic in North American white-tailed deer (Odocoileus virginianus), a species which is highly susceptible to the causative agent of EHD, epizootic hemorrhagic disease virus (EHDV). EHDV has been detected globally across tropical and temperate regions, largely corresponding to the presence of Culicoides spp. biting midges which transmit the virus between ruminant hosts. It regularly causes high morbidity and mortality in wild and captive deer populations in endemic areas during epizootics. Although cattle historically have been less susceptible to EHDV, reports of clinical disease in cattle have increased in the past two decades. There is a pressing need to identify new methods to prevent and mitigate outbreaks and reduce the considerable impacts of EHDV on livestock and wildlife. This review discusses recent research advancements towards the control of EHDV, including the development of new investigative tools and progress in basic and applied research focused on virus detection, disease mitigation, and vector control. The potential impacts and implications of these advancements on EHD management are also discussed.

West Nile virus (WNV) is a mosquito-borne arbovirus that remains a persistent public health challenge in the USA, with seasonal outbreaks that can lead to severe cases. In this study, we detail a real-time prediction system for WNV that incorporates an adapted compartment model to account for the transmission dynamics among birds, mosquitoes and humans, including asymptomatic cases and the influence of weather factors. Using data assimilation techniques, we generate weekly WNV case forecasts for Colorado in 2023, providing valuable insights for public health planning. Comparative analyses underscore the enhanced forecast accuracy achieved by integrating weather variables into our models.

Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

Dengue fever remains a significant public health concern in many tropical and subtropical countries, and there is still a need for a system that can effectively combine global risk assessment with timely incidence forecasting. This research describes an integrated application called PICTUREE—Aedes, which can collect and analyze dengue-related data, display simulation results, and forecast outbreak incidence. PICTUREE—Aedes automatically updates global temperature and precipitation data and contains historical records of dengue incidence (1960–2012) and Aedes mosquito occurrences (1960–2014) in its database. The application utilizes a mosquito population model to estimate mosquito abundance, dengue reproduction number, and dengue risk. To predict future dengue outbreak incidence, PICTUREE—Aedes applies various forecasting techniques, including the ensemble Kalman filter, recurrent neural network, particle filter, and super ensemble forecast, which are all based on user-entered case data. The PICTUREE—Aedes’ risk estimation identifies favorable conditions for potential dengue outbreaks, and its forecasting accuracy is validated by available outbreak data from Cambodia.