First daily surveillance of emerging COVID-19 hotspots
Hotspots have trended towards smaller but more numerous clusters since
the pandemic started

Date:
August 20, 2020
Source:
University of Utah
Summary:
Over the course of the coronavirus epidemic, COVID-19 outbreaks
have hit communities across the United States. As clusters of
infection shift over time, local officials are forced into a
whack-a-mole approach to allocating resources and enacting public
health policies. Geographers hope that timely, localized data will
help inform future decisions, and one day predict where hotpots
will emerge.



FULL STORY ==========================================================================
Over the course of the coronavirus epidemic, COVID-19 outbreaks have hit communities across the United States. As clusters of infection shift
over time, local officials are forced into a whack-a-mole approach
to allocating resources and enacting public health policies. In a new
study led by the University of Utah, geographers published the first
effort to conduct daily surveillance of emerging COVID-19 hotspots for
every county in the contiguous U.S. The researchers hope that timely,
localized data will help inform future decisions.


========================================================================== Using innovative space-time statistics, the researchers detected
geographic areas where the population had an elevated risk of contracting
the virus. They ran the analysis every day using daily COVID-19 case
counts from Jan. 22 to June 5, 2020 to establish regional clusters,
defined as a collection of disease cases closely grouped in time and
space. For the first month, the clusters were very large, especially in
the Midwest. Starting on April 25, the clusters become smaller and more numerous, a trend that persists until the end of the study.

The article published online on June 27, 2020, in the journal Spatial and Spatio-temporal Epidemiology. The study builds on the team's previous
work by evaluating the characteristics of each cluster and how the characteristics change as the pandemic unfolds.

"We applied a clustering method that identifies areas of concern,
and also tracks characteristics of the clusters -- are they growing
or shrinking, what is the population density like, is relative risk
increasing or not?" said Alexander Hohl, lead author and assistant
professor at the Department of Geography at the U. "We hope this can
offer insights into the best strategies for controlling the spread of
COVID-19, and to potentially predict future hotspots." The research
team, including Michael Desjardins of Johns Hopkins Bloomberg School
of Public Health's Spatial Science for Public Health Center and Eric
Delmelle and Yu Lan of the University of North Carolina at Charlotte,
have created a web application of the clusters that the public can check
daily at COVID19scan.net. The app is just a start, Hohl warned. State
officials would need to do smaller scale analysis to identify specific locations for intervention.

"The app is meant to show where officials should prioritize efforts --
it's not telling you where you will or will not contract the virus," Hohl
said. "I see this more as an inspiration, rather than a concrete tool,
to guide authorities to prevent or respond to outbreaks. It also gives
the public a way to see what we're doing." The researchers used daily
case counts reported in the COVID-19 Data Repository from the Center for Systems Science and Engineering at Johns Hopkins University, which lists
cases at the county level in the contiguous U.S. They used the U.S. Census website's 2018 five-year population estimates within each county.

To establish the clusters, they ran a space-time scan statistic that takes
into account the observed number of cases and the underlying population
within a given geographic area and timespan. Using SatScan, a widely
used software, they identified areas of significantly elevated risk of COVID-19. Due to the large variation between counties, evaluating risk
is tricky. Rural areas and small, single counties may not have large populations, therefore just a handful of cases would make risk go up significantly.

This study is the third of the research group's iteration using the
statistical method for detecting and monitoring COVID-19 clusters in the
U.S. Back in May, the group published their first geographical study to
use the tracking method, which was also of the first paper published by geographers analyzing COVID-19.

In June, they published an update.

"May seems like an eternity ago because the pandemic is changing so
rapidly," Hohl said. "We continue to get feedback from the research
community and are always trying to make the method better. This is just
one method to zero in on communities that are at risk." A big limitation
of the analysis is the data itself. COVID-19 reporting is different for
each state. There's no uniform way that information flows from the labs
that confirm the diagnoses, to the state health agencies to the COVID-
19 Data Repository from the Center for Systems Science and Engineering at
Johns Hopkins University, where the study gets its data. Also, the testing efforts are quite different between states, and the team is working to
adjust the number of observed cases to reflect a state's efforts. Hohl is
also working with other U researchers to look at the relationship between social media and COVID-19 to predict the future trajectory of outbreaks.

"We've been working on this since COVID-19 first started and the field
is moving incredibly fast," said Hohl. "It's so important to get the
word out and react to what else is being published so we can take the
next step in the project."

========================================================================== Story Source: Materials provided by University_of_Utah. Original written
by Lisa Potter.

Note: Content may be edited for style and length.


========================================================================== Journal References:
1. Alexander Hohl, Eric M. Delmelle, Michael R. Desjardins, Yu
Lan. Daily
surveillance of COVID-19 using the prospective space-time scan
statistic in the United States. Spatial and Spatio-temporal
Epidemiology, 2020; 34: 100354 DOI: 10.1016/j.sste.2020.100354
2. Alexander Hohl, Eric Delmelle, Michael Desjardins. Rapid detection
of
COVID-19 clusters in the United States using a prospective
space-time scan statistic. SIGSPATIAL Special, 2020; 12 (1):
27 DOI: 10.1145/ 3404820.3404825
3. M.R. Desjardins, A. Hohl, E.M. Delmelle. Rapid surveillance
of COVID-19
in the United States using a prospective space-time scan statistic:
Detecting and evaluating emerging clusters. Applied Geography,
2020; 118: 102202 DOI: 10.1016/j.apgeog.2020.102202 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200820164203.htm

--- up 5 weeks, 1 day, 1 hour, 55 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)