What Can We Learn from the Travelers Data in Detecting Disease Outbreaks – A Case Study of the COVID-19 Epidemic

08/27/2020
by   Le Bao, et al.
0

Background: Travel is a potent force in the emergence of disease. We discussed how the traveler case reports could aid in a timely detection of a disease outbreak. Methods: Using the traveler data, we estimated a few indicators of the epidemic that affected decision making and policy, including the exponential growth rate, the doubling time, and the probability of severe cases exceeding the hospital capacity, in the initial phase of the COVID-19 epidemic in multiple countries. We imputed the arrival dates when they were missing. We compared the estimates from the traveler data to the ones from domestic data. We quantitatively evaluated the influence of each case report and knowing the arrival date on the estimation. Findings: We estimated the travel origin's daily exponential growth rate and examined the date from which the growth rate was consistently above 0.1 (equivalent to doubling time < 7 days). We found those dates were very close to the dates that critical decisions were made such as city lock-downs and national emergency announcement. Using only the traveler data, if the assumed epidemic start date was relatively accurate and the traveler sample was representative of the general population, the growth rate estimated from the traveler data was consistent with the domestic data. We also discussed situations that the traveler data could lead to biased estimates. From the data influence study, we found more recent travel cases had a larger influence on each day's estimate, and the influence of each case report got smaller as more cases became available. We provided the minimum number of exported cases needed to determine whether the local epidemic growth rate was above a certain level, and developed a user-friendly Shiny App to accommodate various scenarios.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

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...
03/05/2018

Estimation in emerging epidemics: biases and remedies

When analysing new emerging infectious disease outbreaks one typically h...
09/06/2021

Estimating the case fatality rate of a disease during the course of an epidemic with an application to COVID-19 in Argentina

We present an accurate estimator of the case fatality rate that can be c...
07/01/2020

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COV...
05/05/2020

Did COVID-19 infections decline before UK lockdown?

The number of new infections per day is a key quantity for effective epi...
05/07/2020

COVID-19 transmission risk factors

We analyze risk factors correlated with the initial transmission growth ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.