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Trichostrongylid nematodes can cause serious loss in the livestock economy; nevertheless, infections with these ubiquitous nematodes in animals from developing countries are largely neglected. The present paper provides a systematic review and meta-analysis to evaluate the status of trichostrongyle infections in domestic ruminants from Egypt. Out of 626 collected publications, 118 were defined suitable for inclusion in this review and represented trichostrongyle infections in 5 ruminant species (sheep, goats, cattle, buffaloes and camels) from Egypt. Published surveys have mostly focused on sheep, which had the highest (44.8%) pooled prevalence of trichostrongyle infections based on 95% confidence interval (35.9-53.6%) with no significant variations among sheep in different Egyptian regions. Goats had lower infection prevalence (31.2%, 21.5-40.8%) in comparison to sheep, and no significant regional differences were also found. The management and marketing practices likely account for the wide distribution of infection among small ruminants across Egypt. Variable trichostrongyle infection rates were estimated for camels (38.8%, 28.9-48.7%), cattle (27.4%, 14.4-40.3%) and buffaloes (12.2%, 8.0-16.4%). The prevalence of infection was significantly high during winter (52.1%, 32.2-72.1%), which provide favorable conditions for development and survival of larvae on pastures. The most common trichostrongyle identified in infected animals was Haemonchus contortus. The parasite was detected in 38.8% (29.9-47.6%) of infected sheep, 35.3% (24.8-45.7%) of infected goats and in 40.6% (18.6-62.5%) of infected camels. Clinical infections have been reported in a limited number of studies. Analysis of fecal egg counts (FECs) revealed that more than half (52.7%, 30.4-75.0%) of the tested sheep had low counts (<500 eggs per gram EPG), whereas only a few sheep (6.1%, 3.5-8.7%) had high counts >2000 EPG, implying that subclinical infections are common; however, the clinical infections cannot be completely ruled out. Anthelmintic resistance does not appear to be serious in trichostrongyle populations infecting ruminants from Egypt; nonetheless there have been a few cases of albendazole resistance in trichostrongyles infecting sheep. This paper contributes to a better understanding of the epidemiology and economic implications of trichostrongyle infections in ruminants from Egypt, which is crucial for establishing effective control strategies against these ubiquitous nematodes., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: West Nile virus (WNV), a global arbovirus, is the most prevalent mosquito-transmitted infection in the United States. Forecasts of WNV risk during the upcoming transmission season could provide the basis for targeted mosquito control and disease prevention efforts. We developed the Arbovirus Mapping and Prediction (ArboMAP) WNV forecasting system and used it in South Dakota from 2016 to 2019. This study reports a post hoc forecast validation and model comparison., Objectives: Our objective was to validate historical predictions of WNV cases with independent data that were not used for model calibration. We tested the hypothesis that predictive models based on mosquito surveillance data combined with meteorological variables were more accurate than models based on mosquito or meteorological data alone., Methods: The ArboMAP system incorporated models that predicted the weekly probability of observing one or more human WNV cases in each county. We compared alternative models with different predictors including a ) a baseline model based only on historical WNV cases, b ) mosquito models based on seasonal patterns of infection rates, c ) environmental models based on lagged meteorological variables, including temperature and vapor pressure deficit, d ) combined models with mosquito infection rates and lagged meteorological variables, and e ) ensembles of two or more combined models. During the WNV season, models were calibrated using data from previous years and weekly predictions were made using data from the current year. Forecasts were compared with observed cases to calculate the area under the receiver operating characteristic curve (AUC) and other metrics of spatial and temporal prediction error., Results: Mosquito and environmental models outperformed the baseline model that included county-level averages and seasonal trends of WNV cases. Combined models were more accurate than models based only on meteorological or mosquito infection variables. The most accurate model was a simple ensemble mean of the two best combined models. Forecast accuracy increased rapidly from early June through early July and was stable thereafter, with a maximum AUC of 0.85. The model predictions captured the seasonal pattern of WNV as well as year-to-year variation in case numbers and the geographic pattern of cases., Discussion: The predictions reached maximum accuracy early enough in the WNV season to allow public health responses before the peak of human cases in August. This early warning is necessary because other indicators of WNV risk, including early reports of human cases and mosquito abundance, are poor predictors of case numbers later in the season. https://doi.org/10.1289/EHP10287.