Algorithms using data from antibody signatures in peoples’ blood may enable scientists to assess the size of cholera outbreaks and identify hotspots of cholera transmission more accurately than ever, according to a study led by scientists at the Johns Hopkins Bloomberg School of Public Health.
Scientists who study predictive systems, doubt that it will be possible to predict exactly what will happen next in a disease outbreak, because the most important variables can change so much from one outbreak to another.
Thousands of Zika virus cases went unreported in Cuba in 2017, according to an analysis of data on travelers to the Caribbean island. Veiling them may have led to many other cases that year.
This document summarizes World Health Organization (WHO) recommendations for surveillance for Middle East respiratory syndrome coronavirus (MERS-CoV) infection.
A study in Tennessee found ticks on about one in six cattle and at livestock monitoring locations in all regions of the state, highlighting a “hidden health threat” to the cattle industry.
Researchers examined the usefulness of Google Trends search data for analyzing the 2016 Zika epidemic in Colombia and evaluating their ability to predict its spread.
Security officials are looking at publicly available, open source communications like social media, and intercepted phone and internet communications to track infectious disease outbreaks.
Open Source Intelligence (OSINT) and Signals Intelligence (SIGINT) from the clandestine intelligence sector are increasingly employed in infectious disease outbreaks.
Researchers seek to develop tools, spurred by machine-based learning, to predict zoonotic disease outbreaks before they happen.
Researchers are using machine learning and social media to identify illness outbreaks.