COVID-19 Related

COVID-19 Related

Data Sources and Applications

• Data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University
• CDC: COVID-19 Case Surveillance Public Use Data with Geography
• 1Point3Acres 
• Apple: COVID-19 Mobility Trends Reports
• C3.ai COVID-19 Data Lake is a unified, easily accessible data image of critical COVID-19 data publicly available at no cost to the global research community beginning on April 20, 2020.
• SAS COVID-19 Resource Hub
• reichlab/covid19-forecast-hub: Projections of COVID-19, in standardized format
• CORD-19: COVID-19 Open Research Dataset
• US Census: US Census Bureau COVID-19 Site
• CDC: US Influenza Surveillance System, Laboratory-Confirmed Influenza Hospitalizations
• European CDC: EARS-Net
• How severe is the pandemic where you live?
• 新型コロナウイルス感染速報 - a shiny application (in Chinese) (English, Chinese)

Articles

• CDC MMWR: COVID-19 related reports
• Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019
• Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019
• SAS: Using data visualization to track the coronavirus outbreak
• Stata by Chuck Huber
• How to create animated choropleth maps using the COVID-19 data from Johns Hopkins University
• How to create choropleth maps using the COVID-19 data from Johns Hopkins University
• COVID-19 time-series data from Johns Hopkins University
• Brauer (2017): Mathematical epidemiology: Past, present, and future
• Plus magazine: Covid-19 (SIR model)
• software carpentry: Dynamic SIR Models using R! (deSolve)
• Eugene Joh: SIR model with deSolve & ggplot2
• Wikipedia: Compartmental models in epidemiology 
• Lancet: COVID-19 Resource Centre
• NEJM: Coronavirus
• HealthKnowledge: Epidemic theory (R0, R, Herd immunity, etc.)
• Bjørnstad: R package for Epidemics: Models and Data in R
• Frosts: Epi Recipes of Epi Cookbook

Video

• ADA COVID-19 Webinar Series
• Tom Rocks Maths on YouTube
• Part  I: Mathematician explains SIR disease model for COVID-19
• Part II: Mathematician explains SIR disease model for COVID-19
• 3Blue1Brown: Exponential growth and epidemics; Taylor series

Prevalence of Diabetes by Race and Ethnicity in the United States, 2011-2016

Prevalence of Diabetes by Race and Ethnicity in the United States, 2011-2016
YJ Cheng, AM Kanaya, MRG Araneta, et al. JAMA 2019;322(24):2389-2398

Key Points

Question  During 2011-2016, how prevalent was diabetes among major race/ethnicity groups and subgroups of Hispanic and non-Hispanic Asian adults in the United States?
Findings  In this cross-sectional study that included 7575 adults, the age- and sex-adjusted diabetes prevalence was 12.1% for non-Hispanic white, 20.4% for non-Hispanic black, 22.1% for Hispanic, and 19.1% for non-Hispanic Asian groups. The diabetes prevalence also differed significantly among Hispanic or non-Hispanic Asian subgroups.
Meaning  In the United States in 2011-2016, the prevalence of diabetes varied across racial/ethnic groups.
...Full text...

News etc.

• Editorial: K Matsushita, O Tang, E Selvin. Addressing Challenges and Implications of National Surveillance for Racial/Ethnic Disparities in Diabetes
•  CDC Newsroom: CDC Releases First National Estimates on Diabetes within Hispanic and Asian Populations in the US
• U.S. News: CDC Study Breaks Down Diabetes Risk for Hispanic, Asian Subgroups
• abs NEWS: First national estimates on how common diabetes is among Hispanic and Asian Americans: CDC
• DailyMail: Diabetes is TWICE as common in Asian and Hispanic Americans as the disease is among the US population, CDC says
• JAMA Podcast: Diabetes Prevalence in the US, Invasive Nontypeable ..., and more
• Medicine Matters Diabetes: Diabetes prevalence varies between and within US ethnic subgroups
• Altmetric

A brief history of AI, machine learning, artificial neural networks, and deep learning

A brief history of AI, machine learning, artificial neural networks, and deep learning
TheScientist, Jef Akst (2019)
"The term “artificial intelligence” dates back to the mid-1950s, when mathematician John McCarthy, widely recognized as the father of AI, used it to describe machines that do things people might call intelligent. He and Marvin Minsky, whose work was just as influential in the AI field, organized the Dartmouth Summer Research Project on Artificial Intelligence in 1956. A few years later, with McCarthy on the faculty, MIT founded its Artificial Intelligence Project, later the AI Lab. It merged with the Laboratory for Computer Science (LCS) in 2003 and was renamed the Computer Science and Artificial Intelligence Laboratory, or CSAIL"