scholarly journals Evaluation of TellYourContacts.org, a Patient-Initiated Digital COVID-19 Contact Notification Tool (Preprint)

2020 ◽  
Author(s):  
Kelechi Stephanie Okpara ◽  
Jen Hecht ◽  
Daniel Wohlfeiler ◽  
Matthew Prior ◽  
Jeffrey Klausner
Keyword(s):  
Infectious Disease ◽  
United States ◽  
Descriptive Analysis ◽  
The United States ◽  
Microsoft Excel ◽  
Results Disclosure ◽  
Close Contacts

BACKGROUND Contact notification is a method used to control the spread of infectious disease by alerting close contacts of possible exposure. Digital contact notification allows for notification via SMS and email. OBJECTIVE This study evaluated TellYourContacts.org, a COVID-19 digital contact notification website that can be used either confidentially or anonymously. METHODS We performed a descriptive analysis of the feasibility and use of TellYourContacts.org from May 18th to August 17th, 2020. A testing lab and results disclosure service encouraged patients who tested positive for COVID-19 to use TellYourContacts.org through notifications. We collected website data through Google Analytics and used Microsoft Excel to record the data of website users, types of messages sent, and location of the senders. RESULTS Over the course of three months, 9,130 users accessed the website and sent a total of 1,952 messages. Of those messages, users sent 1,815 (93%) SMS messages and 137 (7%) emails. Users sent messages from 40 states, with the majority of United States senders from California (49%). CONCLUSIONS Our findings suggest that TellYourContacts.org is a promising and accessible patient-led digital contact notification method for those who test positive for COVID-19 in the United States. TellYourContacts.org allows for an additional means to assist in contact notification, thereby increasing the overall number of contacts notified of COVID-19 exposure.

You’re Fired! Donald Trump and the 2020 Congressional Elections

The Forum ◽  
2020 ◽  
Vol 18 (4) ◽  
pp. 627-650
Author(s):  
Jamie L. Carson ◽  
Spencer Hardin ◽  
Aaron A. Hitefield
Keyword(s):  
United States ◽  
Presidential Election ◽  
Descriptive Analysis ◽  
Vice President ◽  
The United States ◽  
Congressional Elections ◽  
General Election ◽  
Donald Trump ◽  
Modern Era

Abstract The 2020 elections brought to an end one of the most divisive and historic campaigns in the modern era. Former Vice President Joe Biden was elected the 46th President of the United States with the largest number of votes ever cast in a presidential election, defeating incumbent President Donald Trump in the process. The record turnout was especially remarkable in light of the ongoing pandemic surrounding COVID-19 and the roughly 236,000 Americans who had died of the virus prior to the election. This article examines the electoral context of the 2020 elections focusing on elections in both the House and Senate. More specifically, this article examines the candidates, electoral conditions, trends, and outcomes in the primaries as well as the general election. In doing so, we provide a comprehensive descriptive analysis of the climate and outcome of the 2020 congressional elections. Finally, the article closes with a discussion of the broader implications of the election outcomes on both the incoming 117th Congress as well as the upcoming 2022 midterm election.

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 Lack of Transmission among Close Contacts of Patient with Case of Middle East Respiratory Syndrome Imported into the United States, 2014

2015 ◽  
Vol 21 (7) ◽  
pp. 1128-1134 ◽  
Author(s):  
Lucy Breakwell ◽  
Kimberly Pringle ◽  
Nora Chea ◽  
Donna Allen ◽  
Steve Allen ◽  
...  
Keyword(s):  
United States ◽  
Middle East ◽  
The United States ◽  
Middle East Respiratory Syndrome ◽  
Close Contacts

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>

scholarly journals Introductory Models of COVID-19 in the United States

2021 ◽  
Vol 2 (3) ◽  
pp. 74-98
Author(s):  
Peter Hugo Nelson
Keyword(s):  
United States ◽  
The United States ◽  
Transition Function ◽  
Model Parameters ◽  
Microsoft Excel ◽  
New Model ◽  
Social Distancing ◽  
Unlimited Growth ◽  
Least Squares Techniques

ABSTRACT Students develop and test simple kinetic models of the spread of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Microsoft Excel is used as the modeling platform because it is nonthreatening to students and it is widely available. Students develop finite difference models and implement them in the cells of preformatted spreadsheets following a guided inquiry pedagogy that introduces new model parameters in a scaffolded step-by-step manner. That approach allows students to investigate the implications of new model parameters in a systematic way. Students fit the resulting models to reported cases per day data for the United States using least squares techniques with Excel's Solver. Using their own spreadsheets, students discover for themselves that the initial exponential growth of COVID-19 can be explained by a simplified unlimited growth model and by the susceptible-infected-recovered (SIR) model. They also discover that the effects of social distancing can be modeled using a Gaussian transition function for the infection rate coefficient and that the summer surge was caused by prematurely relaxing social distancing and then reimposing stricter social distancing. Students then model the effect of vaccinations and validate the resulting susceptible-infected-recovered-vaccinated (SIRV) model by showing that it successfully predicts the reported cases per day data from Thanksgiving through the holiday period up to 14 February 2021. The same SIRV model is then extended and successfully fits the fourth peak up to 1 June 2021, caused by further relaxation of social distancing measures. Finally, students extend the model up to the present day (27 August 2021) and successfully account for the appearance of the delta variant of the SARS-CoV-2 virus. The fitted model also predicts that the delta variant peak will be comparatively short, and the cases per day data should begin to fall off in early September 2021, counter to current expectations. This case study makes an excellent capstone experience for students interested in scientific modeling.

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
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