Raw Milk: A Continuing Vehicle for the Transmission of Infectious Disease Agents in the United States

1982 ◽  
Vol 146 (3) ◽  
pp. 440-441 ◽  
Author(s):  
J. Chin
2006 ◽  
Vol 4 (S2) ◽  
pp. 1-2 ◽  
Author(s):  
Rebecca L. Calderon ◽  
Gunther Craun ◽  
Deborah A. Levy

1987 ◽  
Vol 50 (3) ◽  
pp. 188-192 ◽  
Author(s):  
J. LOVETT ◽  
D. W. FRANCIS ◽  
J. M. HUNT

To determine the incidence of Listeria monocytogenes in raw milk, an isolation method was evaluated and used to analyze milk from three areas of the United States. The incidence varied by area from 0% in California to 7% in Massachusetts, with an overall incidence of 4.2%. The highest incidence found in any area during a single sampling period was 12% in Massachusetts in March 1985. During that same sampling, the incidence for all Listeria species was 26%. Of the 27 L. monocytogenes strains isolated during the survey, 25 were pathogenic in adult mice. One of three Listeria ivanovii isolated was pathogenic. No other isolates demonstrated pathogenicity.


10.2196/26719 ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. e26719
Author(s):  
Kelly S Peterson ◽  
Julia Lewis ◽  
Olga V Patterson ◽  
Alec B Chapman ◽  
Daniel W Denhalter ◽  
...  

Background Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text. Objective This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. Methods Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy. Results Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events. Conclusions Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.


2021 ◽  
Author(s):  
Ibrahim Abaker Targio Hashem ◽  
Raja Sher Afgun Usmani ◽  
Asad Ali Shah ◽  
Abdulwahab Ali Almazroi ◽  
Muhammad Bilal

The COVID-19 pandemic has emerged as the world's most serious health crisis, affecting millions of people all over the world. The majority of nations have imposed nationwide curfews and reduced economic activity to combat the spread of this infectious disease. Governments are monitoring the situation and making critical decisions based on the daily number of new cases and deaths reported. Therefore, this study aims to predict the daily new deaths using four tree-based ensemble models i.e., Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Voting Regressor (VR) for the three most affected countries, which are the United States, Brazil, and India. The results showed that VR outperformed other models in predicting daily new deaths for all three countries. The predictions of daily new deaths made using VR for Brazil and India are very close to the actual new deaths, whereas the prediction of daily new deaths for the United States still needs to be improved.<br>


2021 ◽  
Vol 19 (2) ◽  
pp. 240-241
Author(s):  
Aurora B. Le ◽  
Jocelyn J. Herstein

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