Prevalence of Neospora caninum Exposure in Wild Pigs (Sus scrofa) from Oklahoma with Implications of Testing Method on Detection.

Neospora caninum is a protozoan parasite, reported as a leading cause of cattle abortions and reproductive failure worldwide, costing the cattle industry approximately $1.3 billion annually. With wild pig (Sus scrofa) populations estimated at over six million in the United States, contact between wild pigs and livestock is inevitable, mainly because of the widespread geographic co-occurrence of the two species. As a known reservoir for numerous fungal, bacterial, viral and parasitic diseases, wild pigs are of particular importance for human and veterinary health relative to the prevention of infectious diseases. The seroprevalence of N. caninum in wild pig populations was previously documented in the United States, raising the question as to their exposure point of prevalence. This research screened 116 individual wild pigs for N. caninum using a variety of available assays. Using two different commercially available ELISA test kits, seroprevalence ranged from 12.5% to 67.8%. The Indirect Fluorescent Antibody Test resulted in our highest percent seroprevalence for these samples, at 84.1%. However, none of our samples showed any presence of N. caninum or associated pathologies via histological evaluation of representative tissues. Importantly, the assays used in this study were not congruent with all duplicate samples or between the test types used. The implications of these non-congruent results demonstrates that currently available testing assays produce variable results, underscoring the need for more reliable testing kits and a standardized methodology when assessing disease prevalence in wildlife, particularly for N. caninum in wild pigs, which impacts prevalence and comparability across studies.

The Influence of New Surveillance Data on Predictive Species Distribution Modeling of Aedes aegypti and Aedes albopictus in the United States.

The recent emergence or reemergence of various vector-borne diseases makes the knowledge of disease vectors' presence and distribution of paramount concern for protecting national human and animal health. While several studies have modeled Aedes aegypti or Aedes albopictus distributions in the past five years, studies at a large scale can miss the complexities that contribute to a species' distribution. Many localities in the United States have lacked or had sporadic surveillance conducted for these two species. To address these gaps in the current knowledge of Ae. aegypti and Ae. albopictus distributions in the United States, surveillance was focused on areas in Texas at the margins of their known ranges and in localities that had little or no surveillance conducted in the past. This information was used with a global database of occurrence records to create a predictive model of these two species' distributions in the United States. Additionally, the surveillance data from Texas was used to determine the influence of new data from the margins of a species' known range on predicted species' suitability maps. This information is critical in determining where to focus resources for the future and continued surveillance for these two species of medical concern.

Predictive Modeling for West Nile Virus and Mosquito Surveillance in Lubbock, Texas.

West Nile virus (WNV) was first detected in North America during 1999, and has since spread throughout the contiguous USA. West Nile virus causes West Nile fever and the more severe West Nile neuroinvasive disease. As part of a WNV vector surveillance program, we collected mosquitoes in Lubbock, Texas, using CO2-baited encephalitic vector survey (EVS) traps. During 219 wk from 2009 through 2017, EVS traps were operated for 1,748 trap nights, resulting in more than 101,000 mosquitoes captured. Weekly, selected female mosquito specimens were pooled by species and trap site, and screened for WNV using reverse transcription-polymerase chain reaction assay. Mosquitoes positive for WNV were detected during 16.9% (37/219) of the weeks. Using this information, we constructed a statistical model to predict the probability of detecting an infection within a mosquito pool as a factor of weather variables. The final model indicated that detection of WNV in mosquitoes was negatively associated with the week of year squared and average wind from 3 wk prior to sampling, and was positively associated with week of year, average visibility, average humidity from 2 wk prior to sampling, and average dew point from 4 wk prior to sampling. The model developed in this study may aid public health and vector control programs in swift and effective decision making relative to city-wide mosquito control efforts by predicting when the chances of mosquitoes having WNV are at their greatest.