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bayesian-evidence-synthesis-for-influenza-burden

Use a Bayesian evidence synthesis approach to influenza burden estimation, using hospital surveillance data.

cdccovidview

😷Weekly Surveillance Summary of U.S. COVID-19 Activity

coarsedatatools

An R package for analyzing censored and under-reported surveillance data.

covid-19

covid-19-maps

Maps of Covid-19 cases

covid-19-surveillance-dashboard

Worked with Dr. Timothy Wiemken and Dr. Chris Prener from Saint Louis University to develop a Shiny application for COVID-19. Highlights include: anomaly and breakout detection, GIS tabs displaying health data in Missouri, and the ability to upload your own data to be analyzed.

covid-19_ml

Predictive model for the COVID-19 outbreak

covid19_surveillance_sensitivity

covid_law

COVID-19 mortality and demographic mortality laws

covid_spatial

A Bayesian Approach to Improving Spatial Estimates After Accounting for Misclassification Bias in Surveillance Data for COVID-19 in Philadelphia, PA.

covid_surveillance_strategy

cultural-evolution-covid-19-next

Code accompanying "Cultural Evolution, COVID-19, and Preparing for What’s Next"

ecdccolors

Development of an R package for using colour palettes following March 2018 ECDC guidelines for presentation of surveillance data

epidemiar

R package with modeling, forecasting, and early detection & early warning alerts code for EPIDEMIA forecasting reports. The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) Forecasting System is a set of tools coded in free, open-access software, that integrate surveillance and environmental data to model and create short-term forecasts for environmentally-mediated diseases. For producing formatted reports, see also the demo project based on malaria in Ethiopia (with demo data): https://github.com/EcoGRAPH/epidemiar-demo

epidemiar-demo

Demo R project to be used with package epidemiar for environmentally mediated disease modeling and forecasting, integrating data from epidemiological surveillance & environmental drivers. Demo is for malaria in Amhara region, Ethiopia. Epidemiological data are artificial and should not be used for research or public health. Find epidemiar here: https://github.com/EcoGRAPH/epidemiar

episignaldetection

ECDC in collaboration with Epiconcept developed an R package for monitoring infectious disease surveillance data

financial-fraud-detection

Machine learning classification models for predicting fraud transaction

flu_shiny_app

R Shiny application to view weekly influenza surveillance data

fluhmm

Hidden Markov Model for influenza sentinel surveillance

fluview

Computational surveillance of pneumonia and influenza mortality in FluView uses epidemic thresholds to identify high mortality rates but is limited by statistical issues such as seasonality and autocorrelation. We utilized time series anomaly detection to improve recognition of high mortality rates. Results suggest anomaly detection may complement mortality reporting. Constructed with assistance from Dr. Timothy Wiemken from Saint Louis University.

forecasting

Supplementary R package for the book chapter "Forecasting Based on Surveillance Data"

forem

For empowering community 🌱

freedom

An R package to calculate probability of freedom from disease in a population based on surveillance data

gev

guatemala

Environmental Surveillance Results for PPLB

identify-fraud-using-machine-learning

Identified which Enron employees are more likely to have committed fraud using machine learning and public Enron financial and email data.

injurymatrix

A R package for the implementation of ICD-10-CM injury matrix

machine-learning-python-enron-employees

identify fraud from Enron financial data set

machinelearninglab

Some experiments about Machine Learning

mass

A shiny-based web application for disease surveillance