DeepAI
Log In Sign Up

Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US

05/01/2020
by   Erfaneh Gharavi, et al.
0

During a disease outbreak, timely non-medical interventions are critical in preventing the disease from growing into an epidemic and ultimately a pandemic. However, taking quick measures requires the capability to detect the early warning signs of the outbreak. This work collects Twitter posts surrounding the 2020 COVID-19 pandemic expressing the most common symptoms of COVID-19 including cough and fever, geolocated to the United States. Through examining the variation in Twitter activities at the state level, we observed a temporal lag between the rises in the number of symptom reporting tweets and officially reported positive cases which varies between 5 to 19 days.

READ FULL TEXT
06/10/2020

Tracking the Twitter attention around the research efforts on the COVID-19 pandemic

The outbreak of the COVID-19 pandemic has been accompanied by a bulk of ...
04/16/2020

BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic

The coronavirus disease 2019 (COVID-19) has quickly grown from a regiona...
12/15/2021

Crowdsourcing County-Level Data on Early COVID-19 Policy Interventions in the United States: Technical Report

Beginning in April 2020, we gathered partial county-level data on non-ph...
04/21/2020

Privacy in Crisis: A study of self-disclosure during the Coronavirus pandemic

We study observed incidence of self-disclosure in a large dataset of Twe...
10/20/2015

A latent shared-component generative model for real-time disease surveillance using Twitter data

Exploiting the large amount of available data for addressing relevant so...
11/08/2020

Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings

In this paper, we present an iterative graph-based approach for the dete...