Search

ABSTRACT: Objectives: The contamination of fresh surface waters poses a significant burden on human health and prosperity, especially in marginalized communities with limited resources and inadequate infrastructure. Here, we performed in-depth genomic analyses of multidrug-resistant bacteria (MDR-B) isolated from Al-Oueik river water that is used for irrigation of agricultural fields in a disenfranchised area that also hosts a makeshift Syrian refugee camp. Methods: A composite freshwater sample was filtered. Faecal coliforms were counted and extended spectrum cephalosporins and/or ertapenem resistant bacteria were screened. Isolates were identified using MALDI-TOF-MS and analysed using whole-genome sequencing (WGS) to identify the resistome, sequence types, plasmid types, and virulence genes. Results: Approximately 106 CFU/100 mL of faecal coliforms were detected in the water. Four drug-resistant Gram-negative bacteria were identified, namely Escherichia coli, Klebsiella pneumoniae, Enterobacter hormaechei, and Pseudomonas otitidis. Notably, the E. coli isolate harboured blaNDM-5 and a YRIN-inserted PBP3, representing an emerging public health challenge. The K. pneumoniae isolate carried blaSHV-187 as well as mutations in the gene encoding the OmpK37 porin. Enterobacter hormaechei and P. otitidis harboured blaACT-16 and blaPOM-1, respectively. Conclusion: This report provides comprehensive genomic analyses of MDR-B in irrigation water in Lebanon. Our results further support that irrigation water contaminated with faecal material can be a reservoir of important MDR-B, which can spread to adjacent agricultural fields and other water bodies, posing both public health and food safety issues. Therefore, there is an urgent need to implement effective water quality monitoring and management programs to control the proliferation of antibiotic-resistant pathogens in irrigation water in Lebanon.

Abstract BackgroundSyndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-19–like illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing. ObjectiveThis study aims to assess the temporal relationship between confirmed SARS-CoV-2 infections and self-reported and health care provider–reported CLI in university and county settings, respectively. MethodsWe collected aggregated COVID-19 testing and symptom reporting surveillance data from Cornell University (2020‐2021) and Tompkins County Health Department (2020‐2022). We used negative binomial and linear regression models to correlate confirmed COVID-19 case counts and positive test rates with CLI rate time series, lagged COVID-19 cases or rates, and day of the week as independent variables. Optimal lag periods were identified using Granger causality and likelihood ratio tests. ResultsIn modeling undergraduate student cases, the CLI rate (PPPP ConclusionsThe real-time correlation between syndromic surveillance and COVID-19 cases on a university campus suggests symptom reporting is a viable alternative or supplement to COVID-19 surveillance testing. At the county level, syndromic surveillance is also a leading indicator of COVID-19 cases, enabling quick action to reduce transmission. Further research should investigate COVID-19 risk using syndromic surveillance in other settings, such as low-resource settings like low- and middle-income countries.