Search for: All records

ABSTRACT Antibiotics are often used to treat severeVibrioinfections, with third-generation cephalosporins and tetracyclines combined or fluoroquinolones alone being recommended by the US Centers for Disease Control and Prevention. Increases in antibiotic resistance of both environmental and clinical vibrios are of concern; however, limited longitudinal data have been generated among environmental isolates to inform how resistance patterns may be changing over time. Hence, we evaluated long-term trends in antibiotic resistance of vibrios isolated from Chesapeake Bay waters (Maryland) across two 3-year sampling periods (2009–2012 and 2019–2022).Vibrio parahaemolyticus(n= 134) andVibrio vulnificus(n= 94) toxR-confirmed isolates were randomly selected from both sampling periods and tested for antimicrobial susceptibility against eight antibiotics using the Kirby-Bauer disk diffusion method. A high percentage (94%–96%) ofV. parahaemolyticusisolates from both sampling periods were resistant to ampicillin and only 2%–6% of these isolates expressed intermediate resistance or resistance to third-generation cephalosporins, amikacin, tetracycline, and trimethoprim-sulfamethoxazole. Even lower percentages of resistantV. vulnificusisolates were observed and those were mostly recovered from 2009 to 2012, however, the presence of multiple virulence factors was observed. The frequency of multi-drug resistance was relatively low (6%–8%) but included resistance against antibiotics used to treat severe vibriosis in adults and children. All isolates were susceptible to ciprofloxacin, a fluoroquinolone, indicating its sustained efficacy as a first-line agent in the treatment of severe vibriosis. Overall, our data indicate that antibiotic resistance patterns amongV. parahaemolyticusandV. vulnificusrecovered from the lower Chesapeake Bay have remained relatively stable since 2009.IMPORTANCEVibriospp. have historically been susceptible to most clinically relevant antibiotics; however, resistance and intermediate-resistance have been increasingly recorded in both environmental and clinical isolates. Our data showed that while the percentage of multi-drug resistance and resistance to antibiotics was relatively low and stable across time, someVibrioisolates displayed resistance and intermediate resistance to antibiotics typically used to treat severe vibriosis (e.g., third-generation cephalosporins, tetracyclines, sulfamethoxazole-trimethoprim, and aminoglycosides). Also, given the high case fatality rates observed withVibrio vulnificusinfections, the presence of multiple virulence factors in the tested isolates is concerning. Nevertheless, the continued susceptibility of all tested isolates against ciprofloxacin, a fluoroquinolone, is indicative of its use as an effective first-line treatment of severeVibriospp. infections stemming from exposure to Chesapeake Bay waters or contaminated seafood ingestion. 

The main focus of this article is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the form of a budget constraint. In addition to the standard (homogeneous) setting where all clients must be within a distance\(R\)of the nearest facility, we provide results for the more general problem where the radius demands may beinhomogeneous(i.e., different for each client). We also explore a number of variants where additional constraints are imposed on the first-stage decisions, specifically matroid and multi-knapsack constraints, and provide results for these settings. We derive results for the most general distributional setting, where there is only black-box access to the underlying distribution. To accomplish this, we first develop algorithms for thepolynomial scenariossetting; we then employ a novelscenario-discardingvariant of the standardSample Average Approximationmethod, which crucially exploits properties of the restricted-case algorithms. We note that the scenario-discarding modification to the SAA method is necessary to optimize over the radius. 

Climate change is having increasingly profound effects on human health, notably those associated with the occurrence, distribution, and transmission of infectious diseases. The number of disparate ecological parameters and pathogens affected by climate change are vast and expansive. Disentangling the complex relationship between these variables is critical for the development of effective countermeasures against its effects. The pathogenVibrio vulnificus, a naturally occurring aquatic bacterium that causes fulminant septicemia, represents a quintessential climate-sensitive organism. In this review, we useV.vulnificusas a model organism to elucidate the intricate network of interactions between climatic factors and pathogens, with the objective of identifying common patterns by which climate change is affecting their disease burden. Recent findings indicate that in regions native toV.vulnificusor related pathogens, climate-driven natural disasters are the chief contributors to their disease outbreaks. Concurrently, climate change is increasing the environmental suitability of areas non-endemic to their diseases, promoting a surge in their natural populations and transmission dynamics, thus elevating the risk of new outbreaks. We highlight potential risk factors and climatic drivers aggravating the threat ofV.vulnificustransmission under both scenarios and propose potential measures for mitigating its impact. By defining the mechanisms by which climate change influencesV.vulnificusdisease burden, we aim to shed light on the transmission dynamics of related disease-causing agents, thereby laying the groundwork for early warning systems and broadly applicable control measures. 

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7–14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.