---
author:
- contributor_roles: []
  family: Edmunds
  given: Scott
  url: https://orcid.org/0000-0001-6444-1436
blog:
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  description: Data driven blogging from the GigaScience editors
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  slug: gigablog
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  subfield: '1311'
  title: GigaBlog
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container: GigaBlog
date: '2020-04-12T00:00:00+00:00'
date_updated: '2025-12-06T10:19:46+00:00'
guid: http://gigasciencejournal.com/blog/?p=3279
identifier: https://doi.org/10.59350/mdrhs-5yk16
image: http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Coronavirus-P2-300x221.jpeg
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issn: null
keywords:
- Health
- Coronavirus
- Guest Post
- Infectious Disease
- Michael Dean
lang: en
license: https://creativecommons.org/licenses/by/4.0/legalcode
rid: zg6a2-y8275
rights: https://creativecommons.org/licenses/by/4.0/legalcode
summary: '***With ongoing the 2020 COVID-19 pandemic providing us with unprecedented
  insight into the progression of a disease outbreak, and unprecedented time in the
  lock down to turn us all into armchair epidemiologists. This includes near real-time
  sharing and analysis of genomics data through platforms like nextstrain, and of
  ways to view the infection, mortality and testing statistics via a growing number
  of online dashboards.'
title: 'Guest Blog: Data in the time of Coronavirus, Pt. 2'
url: https://wayback.archive-it.org/22098/2025-05-01T17:13:42Z/http://gigasciencejournal.com/blog/covid-19-data-2
version: v1
---

\*\*\*![COVID-19
data](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Coronavirus-P2-300x221.jpeg){.alignright
.wp-image-3285 decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Coronavirus-P2-300x221.jpeg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Coronavirus-P2-768x565.jpeg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/Coronavirus-P2-1024x753.jpeg 1024w"
sizes="(max-width: 329px) 100vw, 329px" width="329" height="242"}With
ongoing the 2020 COVID-19 pandemic providing us with unprecedented
insight into the progression of a disease outbreak, and unprecedented
time in the lock down to turn us all into armchair epidemiologists. This
includes near real-time sharing and analysis of genomics data through
platforms like [nextstrain](https://nextstrain.org/ncov/global), and of
ways to view the
[infection](https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html?fbclid=IwAR1b5ariHBW6H_TmYQfQ0Vh-DzmNDiQkwYhLPJwAQ1274ymFP3O-J96xSR4#/bda7594740fd40299423467b48e9ecf6),
[mortality](https://www.worldometers.info/coronavirus/) and
[testing](https://a4iot.maps.arcgis.com/apps/opsdashboard/index.html#/50410c128647406ab8ba006dfe1bb6da)
statistics via a [growing number of online
dashboards](https://storymaps.arcgis.com/stories/a1746ada9bff48c09ef76e5a788b5910).
Trying to digest and sift through these COVID-19 data streams, and
following [previous
blogs](http://gigasciencejournal.com/blog/guest-blog-the-2014-ebola-epidemic-approaches-and-resources-to-slow-the-spread-of-infection/)
[on the
Ebola](http://gigasciencejournal.com/blog/guest-blog-the-ebola-epidemic-revisited-where-are-we-in-2015/)
and
[Zika](http://gigasciencejournal.com/blog/guest-blog-mind-zika-data-gap/)
epidemics, in early February we [presented a data oriented guest
post](http://gigasciencejournal.com/blog/guest-blog-coronavirus-data/)
from Michael Dean who pooled together these various public sources to
present a non-specialist view of the Coronavirus crisis. Two months on
he provides a short update, and we have the foresight to look back if
some of his predictions on how the crisis would unfold in China have
come true. Michael is a researcher in the areas of human genetics and
cancer.  He has worked on the understanding of the role of host genetics
in the response to HIV as well as cervical cancer and HPV.

------------------------------------------------------------------------

Just over two months after the [last blog
post](http://gigasciencejournal.com/blog/guest-blog-coronavirus-data/)
was written, on 7th April, China reported a day with no new deaths from
COVID-19. However, the daily trend in cases and deaths around the world,
shows that there are 4800-7100 new deaths per day reported worldwide in
each of the last 7 days.

Following the timeline of the pandemic in China and South Korea is
instructive for understanding the COVID-19 data in other countries.
Plotting the daily increase in new cases, and the percentage of total
cases that represents, shows that the peak of new cases occurred in
China on 4th February and declined at a steady rate for the next month.
A new case definition was introduced in 16th February to include cases
with positive clinical signs.

On 15th February an extrapolation of that declining rate (a negative
slope of \~15%/day) projected that China would drop to 100 new cases per
day on 7th March and 10/day by 21st March. Despite the new case
definition and jump in cases, China did drop to 99 new cases on 6th
March and on 13th March reached 11 cases/day. However, after that, new
cases rebounded somewhat and have hovered at an average of 70-80 new
cases/day.

![COVID-19
data](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/AllNewCases.png){.aligncenter
.wp-image-3280 loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/AllNewCases.png 974w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/AllNewCases-300x174.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/AllNewCases-768x446.png 768w"
sizes="(max-width: 790px) 100vw, 790px" width="790" height="459"}

Subtracting the data from Hubei Province from the totals gives a picture
of what happened in the rest of China. A very similar rate of decline
can be observed, hitting a plateau with an average of about 20 new cases
per day on 22nd February.

![COVID19
data](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/OutsideofHubei.png){.aligncenter
.size-full .wp-image-3281 loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/OutsideofHubei.png 747w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/OutsideofHubei-300x178.png 300w"
sizes="(max-width: 747px) 100vw, 747px" width="747" height="443"}

South Korea, Iran, and Italy were the next three countries to experience
a dramatic increase in cases. By observing the progress from the day
each country passed 1000 cases, they can be compared. In South Korea the
infection was rapidly contained and kept to just over 10,000 cases. The
decline in the percentage of new cases was rapid and constant in South
Korea, very similar to that seen in China. On the first day that Iran
had over 1000 cases, new cases were growing at 37%. That rate declined
steadily to 10%, but then stayed at that level or increasing for the
next 17 days, before declining again to the current rate of 3%. Italy
reached 1000 cases growing at a rate of 25%, and the growth rate
declined erratically until day 23. After that point the growth rate
declined steadily to the current rate of 3%.

![COVID-19
cases](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/globalcasesdecline.png){.aligncenter
.wp-image-3282 loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/globalcasesdecline.png 974w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/globalcasesdecline-300x181.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/globalcasesdecline-768x463.png 768w"
sizes="(max-width: 851px) 100vw, 851px" width="851" height="513"}

The United States, Germany, and Spain each reached over 1000 reported
cases with a growth rate of 25-30% and experienced erratic fluctuations
in the growth rate until day 15-21. After that, all three countries
followed a pattern very similar to Italy. If these trends continue all
three of these countries would reach a growth rate of 2-4% in the next
10 days.

It seems likely the combination of the lock-down, testing and quarantine
of symptomatic cases, and contract tracing brought things under control
in Mainland China, Hong Kong, Taiwan and South Korea. But all this has
to be implemented early.

![COVID-19
decline](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/percentnewcasesdecline.png){.aligncenter
.wp-image-3283 loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/percentnewcasesdecline.png 974w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/percentnewcasesdecline-300x176.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/percentnewcasesdecline-768x449.png 768w"
sizes="(max-width: 850px) 100vw, 850px" width="850" height="498"}

It is tempting to speculate that the voluntary and mandatory imposition
of shelter-in-place, closure of most businesses and social distance
measures, once enacted, lead to a regular decline in the growth rate of
new cases. Of course, the accuracy of the data is limited by the extent
of testing and reporting of cases.

***\[Note: the material presented here is from Michael\'s own
perspective on the public Coronavirus data and does not represent the
viewpoint of his employer\].***

\*![](http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/BGI-MedRxiv-300x156.png){.alignright
.size-medium .wp-image-3284 loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/BGI-MedRxiv-300x156.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/BGI-MedRxiv-768x399.png 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/BGI-MedRxiv-1024x532.png 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/04/BGI-MedRxiv.png 1170w"
sizes="(max-width: 300px) 100vw, 300px" width="300" height="156"}As
Michael stresses the importance of testing, our publisher BGI has been
heavily involved in these efforts and has witnessed first hand the power
of this data to fight the COVID-19 pandemic. Distributing over 10
million PCR based diagnostic tests, after realizing one of the
bottlenecks was a shortage of P2 biosafety labs they [built a lab able
to process 10,000 sample in Wuhan in 5
days](https://www.genengnews.com/insights/bgis-coronavirus-response-build-a-lab-in-wuhan-in-a-week/).
Named Huo-Yan or \"Fire eye\" after the ability of the Monkey God
ability to detect hidden demons. Opening the day after Michael\'s blog
was posted, being able to track and trace cases in Hubei province
greatly assisted the fight to get the outbreak under control. In a [new
preprint](https://www.medrxiv.org/content/10.1101/2020.03.15.20036624v2),
BGI\'s data estimates that without the establishment of the lab the
suspected cases would have increased by 47%, and the corresponding cost
of the quarantine would have doubled. These Huo-Yan testing labs have
now rolled out across China, and are now starting to open up in the new
front lines of the crisis in the [Middle
East](https://www.gulftoday.ae/news/2020/03/31/uae-sets-up-covid-19-detection-lab-in-just-14-days),
[SE
Asia](https://www.bgi.com/global/company/news/first-bgi-huo-yan-laboratory-in-asean-starts-operation/)
and [Europe](https://en.mgitech.cn/news/158/). While wealthy countries
may have the resources to build and run these labs, it\'s essential to
fill in the testing gaps in places that may not necessarily have testing
infrastructure. To address that issue BGI has [launched the \"Huo-Yan
Air
Lab\"](https://www.genengnews.com/news/bgi-unveils-inflatable-p2-lab-for-emergency-covid-19-testing/),
an easily transportable inflatable structure is intended to help ramp up
virus testing and screening infrastructure. [With the aim of being a
BSL-2 lab that can fit in standard air freight, having airlocks,
negative pressure and HEPA filtered air between the different modules
(see video of it being set up by GigaScience\'s Shenzhen Office).
]{.css-901oao .css-16my406 .r-1qd0xha .r-ad9z0x .r-bcqeeo .r-qvutc0}\
\*

The post [Guest Blog: Data in the time of Coronavirus, Pt.
2](http://gigasciencejournal.com/blog/covid-19-data-2/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.