scholarly journals Current status and future recommendations for feral swine disease surveillance in the United States

2019 ◽  
Vol 97 (6) ◽  
pp. 2279-2282 ◽  
Author(s):  
Vienna R Brown ◽  
Michael C Marlow ◽  
Rachel M Maison ◽  
Thomas Gidlewski ◽  
Richard Bowen ◽  
...  
2020 ◽  
Vol 98 (8) ◽  
Author(s):  
Vienna R Brown ◽  
Michael C Marlow ◽  
Thomas Gidlewski ◽  
Richard Bowen ◽  
Angela Bosco-Lauth

2019 ◽  
Vol 02 (03) ◽  
Author(s):  
Sherif Aly ◽  
Allan Stolarski ◽  
Patrick O’Neal ◽  
Edward Whang ◽  
Gentian Kristo

Harmful Algae ◽  
2021 ◽  
pp. 101975
Author(s):  
Donald M. Anderson ◽  
Elizabeth Fensin ◽  
Christopher J. Gobler ◽  
Alicia E. Hoeglund ◽  
Katherine A. Hubbard ◽  
...  

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S195-S195
Author(s):  
Naeemah Z Logan ◽  
Beth E Karp ◽  
Kaitlin A Tagg ◽  
Claire Burns-Lynch ◽  
Jessica Chen ◽  
...  

Abstract Background Multidrug-resistant (MDR) Shigella sonnei infections are a serious public health threat, and outbreaks are common among men who have sex with men (MSM). In February 2020, Australia’s Department of Health notified CDC of extensively drug-resistant (XDR) S. sonnei in 2 Australian residents linked to a cruise that departed from Florida. We describe an international outbreak of XDR S. sonnei and report on trends in MDR among S. sonnei in the United States. Methods Health departments (HDs) submit every 20th Shigella isolate to CDC’s National Antimicrobial Resistance Monitoring System (NARMS) laboratory for susceptibility testing. We defined MDR as decreased susceptibility to azithromycin (MIC ≥32 µg/mL) with resistance to ampicillin, ciprofloxacin, and cotrimoxazole, and XDR as MDR with additional resistance to ceftriaxone. We used PulseNet, the national subtyping network for enteric disease surveillance, to identify US isolates related to the Australian XDR isolates by short-read whole genome sequencing. We screened these isolates for resistance determinants (ResFinder v3.0) and plasmid replicons (PlasmidFinder) and obtained patient histories from HDs. We used long-read sequencing to generate closed plasmid sequences for 2 XDR isolates. Results NARMS tested 2,781 S. sonnei surveillance isolates during 2011–2018; 80 (2.9%) were MDR, including 1 (0.04%) that was XDR. MDR isolates were from men (87%), women (9%), and children (4%). MDR increased from 0% in 2011 to 15.3% in 2018 (Figure). In 2020, we identified XDR isolates from 3 US residents on the same cruise as the Australians. The US residents were 41–42 year-old men; 2 with available information were MSM. The US and Australian isolates were highly related (0–1 alleles). Short-read sequence data from all 3 US isolates mapped to the blaCTX-M-27 harboring IncFII plasmids from the 2 Australian isolates with >99% nucleotide identity. blaCTX-M-27 genes confer ceftriaxone resistance. Increase in Percentage of Shigella sonnei Isolates with Multidrug Resistance* in the United States, 2011–2018† Conclusion MDR S. sonnei is increasing and is most often identified among men. XDR S. sonnei infections are emerging and are resistant to all recommended antibiotics, making them difficult to treat without IV antibiotics. This outbreak illustrates the alarming capacity for XDR S. sonnei to disseminate globally among at-risk populations, such as MSM. Disclosures All Authors: No reported disclosures


Author(s):  
Mohammad Reza Davahli ◽  
Krzysztof Fiok ◽  
Waldemar Karwowski ◽  
Awad M. Aljuaid ◽  
Redha Taiar

The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the US states. While the GTNN model’s edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with Rt numbers collected over the previous four days and asked them to predict the following day for all states in the USA. The performance of these models was evaluated with the datasets that included Rt values reflecting conditions from 22 January through 26 November 2020 (before the start of COVID-19 vaccination in the USA). To determine the efficiency, we compared the results of two models with each other and with those generated by a baseline Long short-term memory (LSTM) model. The results indicated that the GTNN model outperformed both the NGNN and LSTM models for predicting Rt.


2016 ◽  
Vol 214 (1) ◽  
pp. S339-S340
Author(s):  
Dotun Ogunyemi ◽  
Alma Aurioles ◽  
Rob Olson ◽  
Nathaniel Sugiyama ◽  
Ray Bahado-Singh

1993 ◽  
Vol 57 (2) ◽  
pp. 424
Author(s):  
H. Lee Stribling ◽  
John J. Mayer ◽  
I. Lehr Brisbin

2014 ◽  
Vol 143 (10) ◽  
pp. 2131-2136 ◽  
Author(s):  
K. PEDERSEN ◽  
K. L. PABILONIA ◽  
T. D. ANDERSON ◽  
S. N. BEVINS ◽  
C. R. HICKS ◽  
...  

SUMMARYAs feral swine continue to expand their geographical range and distribution across the United States, their involvement in crop damage, livestock predation, and pathogen transmission is likely to increase. Despite the relatively recent discovery of feral swine involvement in the aetiology of a variety of pathogens, their propensity to transmit and carry a wide variety of pathogens is disconcerting. We examined sera from 2055 feral swine for antibody presence to six serovars of Leptospira that can also infect humans, livestock or domestic animals. About 13% of all samples tested positive for at least one serovar, suggesting that Leptospira infection is common in feral swine. Further studies to identify the proportion of actively infected animals are needed to more fully understand the risk they pose.


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