Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

A tree-based intelligence ensemble approach for spatial prediction of potential groundwater

The objective of this research is to propose and confirm a new machine learning approach of Best-First tree (BFtree), AdaBoost (AB), MultiBoosting (MB), and Bagging (Bag) ensembles for potential groundwater mapping and assessing role of influencing factors. The Yasuj-Dena area (Iran) is selected as a case study. For this regard, a Yasuj-Dena database was established with 362 springs locations and 12 groundwater-influencing factors (slope, aspect, elevation, stream power index (SPI), length of slope (LS), topographic wetness index (TWI), topographic position index (TPI), land use, lithology, distance from fault, distance from river, and rainfall). The database was employed to train and validate the proposed groundwater models. The area under the curve (AUC) and statistical metrics were employed to check and confirm the quality of the models. The result shows that the BFTree-Bag model (AUC = 0.810, kappa = 0.495) has the highest prediction performance, followed by the BFTree-MB model (AUC = 0.785, kappa = 0.477), and the BFTree-MB model (AUC = 0.745, kappa = 0.422). Compared to the benchmark of Random Forests, the BFTree-Bag model performs better; therefore, we conclude that the BFtree-Bag is a new tool should be used for modeling of groundwater potential

Prevalence and Associated Factors of <i>optrA</i>-Positive-<i>Enterococcus faecalis</i> in Different Reservoirs around Farms in Vietnam

Linezolid is an antibiotic of last resort for the treatment of infections caused by Gram-positive bacteria, including vancomycin-resistant enterococci. Enterococcus faecalis, a member of enterococci, is a significant pathogen in nosocomial infections. E. faecalis resistance to linezolid is frequently related to the presence of optrA, which is often co-carried with fex, phenicol exporter genes, and erm genes encoding macrolide resistance. Therefore, the common use of antibiotics in veterinary might promote the occurrence of optrA in livestock settings. This is a cross-sectional study aiming to investigate the prevalence of optrA positive E. faecalis (OPEfs) in 6 reservoirs in farms in Ha Nam province, Vietnam, and its associated factors and to explore genetic relationships of OPEfs isolates. Among 639 collected samples, the prevalence of OPEfs was highest in flies, 46.8% (51/109), followed by chickens 37.3% (72/193), dogs 33.3% (17/51), humans 18.7% (26/139), wastewater 16.4% (11/67) and pigs 11.3%, (14/80). The total feeding area and total livestock unit of the farm were associated with the presence of OPEfs in chickens, flies, and wastewater. Among 186 OPEfs strains, 86% were resistant to linezolid. The presence of optrA was also related to the resistant phenotype against linezolid and levofloxacin of E. faecalis isolates. Close genotypic relationships identified by Pulsed Field Gel Electrophoresis between OPEfs isolates recovered from flies and other reservoirs including chickens, pigs, dogs, and wastewater suggested the role of flies in the transmission of antibiotic-resistant pathogens. These results provided warnings of linezolid resistance although it is not used in livestock