The Global Infectious Disease Threat and Its Implications for the United States

2000 ◽  
Vol 79 (4) ◽  
pp. 154 ◽  
Author(s):  
Philip Zelikow
Keyword(s):  
Infectious Disease ◽  
United States ◽  
The United States ◽  
Infectious Disease Threat

scholarly journals Estimating the infectious disease risks associated with drinking water in the United States

2006 ◽  
Vol 4 (S2) ◽  
pp. 1-2 ◽  
Author(s):  
Rebecca L. Calderon ◽  
Gunther Craun ◽  
Deborah A. Levy
Keyword(s):  
Infectious Disease ◽  
United States ◽  
Drinking Water ◽  
The United States ◽  
Disease Risks

scholarly journals Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation

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 ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
United States ◽  
Text Processing ◽  
The United States ◽  
Language Models ◽  
Travel History ◽  
Patient Travel ◽  
Electronic Health ◽  
Automated Text Processing

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.

scholarly journals Modeling and analysis of COVID-19 new deaths using tree-based ensemble

2021 ◽  
Author(s):  
Ibrahim Abaker Targio Hashem ◽  
Raja Sher Afgun Usmani ◽  
Asad Ali Shah ◽  
Abdulwahab Ali Almazroi ◽  
Muhammad Bilal
Keyword(s):  
Infectious Disease ◽  
United States ◽  
Random Forest ◽  
Economic Activity ◽  
The United States ◽  
Gradient Boosting ◽  
Health Crisis ◽  
Modeling And Analysis ◽  
Extreme Gradient Boosting ◽  
The World

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>

Revisiting the Need for an Infectious Disease Care Network in the United States

2021 ◽  
Vol 19 (2) ◽  
pp. 240-241
Author(s):  
Aurora B. Le ◽  
Jocelyn J. Herstein
Keyword(s):  
Infectious Disease ◽  
United States ◽  
The United States ◽  
Care Network

scholarly journals Infectious Disease Prevalence, Not Race Exposure, Predicts Both Implicit and Explicit Racial Prejudice Across the United States

2019 ◽  
Vol 11 (3) ◽  
pp. 345-355 ◽  
Author(s):  
Brian A. O’Shea ◽  
Derrick G. Watson ◽  
Gordon D. A. Brown ◽  
Corey L. Fincher
Keyword(s):  
Infectious Disease ◽  
United States ◽  
Black Americans ◽  
State Level ◽  
The United States ◽  
Racial Prejudice ◽  
White Americans ◽  
Disease Rates ◽  
Prejudicial Attitudes ◽  
Implicit And Explicit

What factors increase racial prejudice? Across the United States, increased exposure to Black Americans has been hypothesized to increase White Americans’ prejudicial attitudes toward Black Americans. Here we test an alternative explanation: People living in regions with higher infectious disease rates have a greater tendency to avoid out-groups because such avoidance reduces their perceived likelihood of contracting illnesses. Consistent with this parasite-stress hypothesis, we show that both White and Black individuals ( N > 77,000) living in U.S. states in which disease rates are higher display increased implicit (automatic) and explicit (conscious) racial prejudice. These results survived the inclusion of several individual- and state-level controls previously used to explain variability in prejudice. Furthermore, showing disease-related primes to White individuals with strong germ aversion increased their explicit, but not implicit, anti-Black/pro-White prejudice. Domestic out-groups, not just foreigners, may therefore experience increased overt forms of prejudice when disease rates are high.

Klebsiella Rhinoscleromatis: A Clinical and Pathogenic Enigma

1979 ◽  
Vol 87 (2) ◽  
pp. 212-221 ◽  
Author(s):  
Robert H. Miller ◽  
Joel B. Shulman ◽  
Rinaldo F. Canalis ◽  
Paul H. Ward
Keyword(s):  
Infectious Disease ◽  
United States ◽  
Respiratory Tract ◽  
The United States ◽  
Comprehensive Review ◽  
Endemic Areas ◽  
Clinical Experiment

Rhinoscleroma is a chronic, slowly progressive, infectious disease of the respiratory tract that can produce disability and death, if untreated. Once considered an anomaly in the United States, the disease is now seen more frequently in this country, owing to increased travel from endemic areas. This report presents a comprehensive review of the several types of therapy that have been published in the literature. The results of a clinical experiment are also presented.

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