Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

The pandemic has marked a turning point for an underutilized technology for infectious disease control in buildings.

DOVER, DE (Jan. 3, 2023) – Through the Centers for Disease Control and Prevention’s State Unintentional Drug Overdose Reporting System (SUDORS), Delaware was recently among 47 states to contribute data to the CDC’s Dec. 16, 2022, Morbidity and Mortality Weekly Report, titled “Drug Overdose Deaths Among Persons Aged 10-19 Years – United States, July 2019-December 2021.” The Division of Public Health (DPH) utilizes SUDORS to track available trends and characteristics of […]

The recent Centers for Disease Control and Prevention (CDC) report states that some types of laminate flooring made by the company, Lumber Liquidators

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Montreal : National Film Board of Canada, 2019

Ben Igbakpa, a member of the House of Representatives, representing Ethiope East and Ethiope West Federal Constituency of Delta State, in this interview, speaks on the intendment of the controversial Infectious Disease Control Bill, saying sponsors mean well for Nigeria.

The post Disease Control Bill: Some people are working on voodoo theory — Rep Igbakpa appeared first on Vanguard News.

Innovator & Multi-Genre Composer, Blythe Abigail 黄淑仁 Schulte, Gets Inspiration from Public-Private Partnerships Amid COVID-19 Crisis

Peter J. Diggle, Emanuele Giorgi, Julienne Atsame, Sylvie Ntsame Ella, Kisito Ogoussan, Katherine Gass.

Source: Statistical Science, Volume 35, Number 1, 42--50.

Abstract:
Vector-borne diseases have long presented major challenges to the health of rural communities in the wet tropical regions of the world, but especially in sub-Saharan Africa. In this paper, we describe the contribution that statistical modelling has made to the global elimination programme for one vector-borne disease, onchocerciasis. We explain why information on the spatial distribution of a second vector-borne disease, Loa loa, is needed before communities at high risk of onchocerciasis can be treated safely with mass distribution of ivermectin, an antifiarial medication. We show how a model-based geostatistical analysis of Loa loa prevalence survey data can be used to map the predictive probability that each location in the region of interest meets a WHO policy guideline for safe mass distribution of ivermectin and describe two applications: one is to data from Cameroon that assesses prevalence using traditional blood-smear microscopy; the other is to Africa-wide data that uses a low-cost questionnaire-based method. We describe how a recent technological development in image-based microscopy has resulted in a change of emphasis from prevalence alone to the bivariate spatial distribution of prevalence and the intensity of infection among infected individuals. We discuss how statistical modelling of the kind described here can contribute to health policy guidelines and decision-making in two ways. One is to ensure that, in a resource-limited setting, prevalence surveys are designed, and the resulting data analysed, as efficiently as possible. The other is to provide an honest quantification of the uncertainty attached to any binary decision by reporting predictive probabilities that a policy-defined condition for action is or is not met.

Cynthia L. Ogden
Jan 1, 2002; 109:45-60
ARTICLES

McKesson Corporation has agreed to pay $18 million to resolve allegations that it improperly set temperature monitors used in shipping vaccines under its contract with the Centers for Disease Control and Prevention (CDC), the Justice Department announced today. McKesson is a pharmaceutical distributor with corporate headquarters in San Francisco