DeepAI
Log In Sign Up

A Note on Early Epidemiological Analysis of Coronavirus Disease 2019 Outbreak using Crowdsourced Data

03/13/2020
by   Giuseppe Arbia, et al.
0

Crowdsourcing data can prove of paramount importance in monitoring and controlling the spread of infectious diseases. The recent paper by Sun, Chen and Viboud (2020) is important because it contributes to the understanding of the epidemiology and of the spreading of Covid-19 in a period when most of the epidemic characteristics are still unknown. However, the use of crowdsourcing data raises a number of problems from the statistical point of view which run the risk of invalidating the results and of biasing estimation and hypothesis testing. While the work by Sun, Chen and Viboud (2020) has to be commended, given the importance of the topic for worldwide health security, in this paper we deem important to remark the presence of the possible sources of statistical biases and to point out possible solutions to them

READ FULL TEXT

page 1

page 2

page 3

page 4

04/01/2020

An ARIMA model to forecast the spread of COVID-2019 epidemic in Italy

Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that...
12/14/2021

A study on the Morris Worm

The Morris worm was one of the first worms spread via the internet. It w...
06/01/2021

Predicting COVID-19 Spread from Large-Scale Mobility Data

To manage the COVID-19 epidemic effectively, decision-makers in public h...
09/14/2019

Identifying Malicious Players in GWAP-based Disaster Monitoring Crowdsourcing System

Disaster monitoring is challenging due to the lake of infrastructures in...
03/05/2018

Estimation in emerging epidemics: biases and remedies

When analysing new emerging infectious disease outbreaks one typically h...