We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




Events

27 Jan 2025 - 30 Jan 2025
15 Feb 2025 - 17 Feb 2025

Social Media Helps Forecast Outbreak Transmission Patterns

By HospiMedica International staff writers
Posted on 02 Feb 2017
Print article
Image: An infected cell engulfed by Ebola (Photo courtesy of the National Institute of Allergy and Infectious Diseases, National Institutes of Health).
Image: An infected cell engulfed by Ebola (Photo courtesy of the National Institute of Allergy and Infectious Diseases, National Institutes of Health).
A new study suggests then when epidemiological data are scarce, social media and Internet reports can be reliable tools for forecasting infectious disease outbreaks.

Researchers at Georgia State University and the U.S. National Institutes of Health tracked and analyzed reports from public health authorities and reputable media outlets posted via social media or their websites during the 2014-2015 Ebola epidemic in West Africa and the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea, in order to study and collect data on the viruses' exposure patterns and transmission chains, and to test the reliability of alternative data streams.

The researchers found they were able to use internet reports describing Ebola cases in the three hardest hit countries (Guinea, Sierra Leone, and Liberia) to provide detailed accounts of epidemiological clusters, particularly useful to characterize time trends. They also found that exposure patterns based on the internet reports aligned with those derived from epidemiological surveillance data on MERS and Ebola, underscoring the importance of disease amplification in hospitals and during funeral rituals prior to the implementation of control interventions. The study was published in the December 2016 supplemental issue of The Journal of Infectious Diseases.

“Mathematical models forecasting disease transmission are often used to guide public health control strategies, but they can be difficult to formulate during the early stages of an outbreak when accurate data are scarce,” concluded lead author GSU associate professor of epidemiology and biostatistics Gerardo Chowell, PhD, and colleagues. “In the absence of detailed epidemiological information rapidly available from traditional surveillance systems, alternative data streams are worth exploring to gain a reliable understanding of disease dynamics in the early stages of an outbreak.”

“Our study offers proof of concept that publicly available online reports released in real-time by ministries of health, local surveillance systems, the World Health Organization, and authoritative media outlets are useful to identify key information on exposure and transmission patterns during epidemic emergencies,” added Dr. Chowell. “Our Internet-based findings on exposure patterns are in good agreement with those derived from traditional epidemiological surveillance data, which can be available after considerable delays.”

Gold Member
12-Channel ECG
CM1200B
Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
New
Hospital Bed
Alphalite
New
Cannulating Sphincterotome
TRUEtome

Print article

Channels

Surgical Techniques

view channel
Image: The surgical team and the Edge Multi-Port Endoscopic Surgical Robot MP1000 surgical system (Photo courtesy of Wei Zhang)

Endoscopic Surgical System Enables Remote Robot-Assisted Laparoscopic Hysterectomy

Telemedicine enables patients in remote areas to access consultations and treatments, overcoming challenges related to the uneven distribution and availability of medical resources. However, the execution... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.