Calendar effects to forecast influenza seasonality: A case study in Milwaukee, WI

Influenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality3.

June 18, 2019

Forecasting hospital pneumonia admissions using influenza surveillance, climate and community data

Influenza peaks around June and December in Singapore every year. Facing an ageing population, hospitals in Singapore have been constantly reaching maximum bed occupancy. The ability to be able to make early decisions during peak periods is important. Tan Tock Seng Hospital is the second largest adult acute care general hospital in Singapore. Pneumonia-related emergency department (ED) admissions are a huge burden to the hospital's resources. The number of cases vary year on year as it depends on seasonal vaccine effectiveness and the population's immunity to the circulating strain.

June 18, 2019

Epi Evident: Biosurveillance to Monitor, Compare, and Forecast Disease Case Counts

The Epi Evident application was designed for clear and comprehensive visualization for monitoring, comparing, and forecasting notifiable diseases simultaneously across chosen countries. Epi Evident addresses the taxing analytical evaluation of how diseases behave differently across countries. This application provides a user-friendly platform with easily interpretable analytics which allows analysts to conduct biosurveillance with minimal user tasks.

January 25, 2018

PLOS Disease Surveillance and Forecasting Channel

The PLOS Disease Forecasting and Surveillance Channel debuted on November 28, bringing together two related but distinct research communities – disease forecasting and syndromic surveillance. The Channel features research and commentary from PLOS journals and the broader literature. Explore recent research, projects and related content and follow the Channel for article updates.

March 29, 2018

Estimating spatial patterning of dietary behaviors using grocery transaction data

Unhealthy diet is becoming the most important preventable cause of chronic disease burden. Dietary patterns vary across neighborhoods as a function of policy, marketing, social support, economy, and the commercial food environment. Assessment of community-specific response to these socio-ecological factors is critical for the development and evaluation policy interventions and identification of nutrition inequality.

July 27, 2017

Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston

This paper continues an initiative conducted by the International Society for Disease Surveillance with funding from the Defense Threat Reduction Agency to connect near-term analytical needs of public health practice with technical expertise from the global research community.  The goal is to enhance investigation capabilities of day-to-day population health monitors.

March 24, 2017

Models for Forecasting Asthma Exacerbations in Urban Environments

Materials associated with the Analytic Solutions for Real-Time Biosurveillance: Models for Forecasting Asthma Exacerbations in Urban Environments consultancy held March 30-31, 2016 at the Boston Public Health Commission (BPHC).

Problem Summary

March 23, 2017

Models for Forecasting Asthma Exacerbations in Urban Environments

Use case for the Analytic Solutions for Real-Time Biosurveillance: Models for Forecasting Asthma Exacerbations in Urban Environments consultancy held March 30-31, 2016 at the Boston Public Health Commission (BPHC).

Problem Summary

March 24, 2017

A Bayesian Hierarchical Model for Estimating Influenza Epidemic Severity

Timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients with influenza. The CDC’s ILINet system collects data on influenza-like illnesses from over 3,300 health care providers, and uses this data to produce accurate indicators of current influenza epidemic severity. However, ILINet indicators are typically reported at a lag of 1-2 weeks. Another source of severity data, Google Flu Trends, is calculated by aggregating Google searches for certain influenza related terms.

August 07, 2017

Data Blindspots: High-Tech Disease Surveillance Misses the Poor

Evidence from over 100 years of epidemiological study demonstrates a consistent, negative association between health and economic prosperity. In many settings, it is clear that causal links exist between lower socioeconomic status and both reduced access to healthcare and increased disease burden. However, our study is the first to demonstrate that the increased disease burden in at-risk populations interacts with their reduced access to healthcare to hinder surveillance.


August 31, 2017


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