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SOUTH AFRICA

GROUNDUP & SPOTLIGHT


HERE’S WHAT THE MODELS PREDICT ABOUT COVID-19 IN SA

Image by Jeyaratnam Caniceus from Pixabay

By Marcus Low and Nathan Geffen
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22 May 2020
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BUT BEWARE: SOOTHSAYING HAS A CHEQUERED HISTORY.


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BeyondWords

First published by GroundUp

A week ago many were complaining about the lack of published models by
government to explain Covid-19 decision-making. On Thursday the Department of
Health hosted a Zoom call in which more models were presented than could fit on
a Milan catwalk.



Some models are useful. If modellers carefully explain their assumptions and
present multiple scenarios, we can get a better understanding of the epidemic’s
possible trajectories, or the potential of different interventions to reduce the
number of infections.

But models, especially because they are surrounded by fancy equations, can give
a false sense of certainty. No one truly knows how the epidemic will play out.
We humans, in contrast to any other species, have an insatiable desire to know
the future. But we can’t. We can only make educated guesses based on the limited
information at our disposal, and when it comes to Covid-19 that information is
still very limited indeed.



Some common uncertainties that stand out in the models are the rate of
asymptomatic infections, how infectious SARS-CoV-2 is, how effective various
interventions are, and the death rate.

A number of the models suggest that South Africa will not have enough ICU beds
for everyone who will need one. Estimates of Covid-19 deaths range from a few
thousands to several hundreds of thousands.



Here then is a very brief summary of each model and a link to more information
about it.

Government’s primary model

The South African Covid-19 Modelling Consortium mainly consists of researchers
from the universities of Cape Town, Stellenbosch, Wits and the National
Institute of Communicable Diseases (NICD). It is the main model that the health
department is using.

The model makes projections under optimistic and pessimistic scenarios with
epidemic peaks in August and July, respectively. It projects six months into the
future.

Under the two scenarios this model estimates between 34,000 and 50,000 deaths by
November, although the modellers stress the significant uncertainty about these
numbers. “These projections are subject to considerable uncertainty and
variability. Estimates will change and improve as the epidemic progresses and
new data become available.”

The model assumes that after the lifting of hard lockdown measures, level four
remains in place for one month, and after that social distancing measures reduce
transmission by 10 to 20%.

Even in the optimistic scenario ICU capacity is exceeded by many thousands of
beds in August to September. While they do not factor in the additional deaths
that this will cause, it might be considerable given that ICU bed capacity is
about 3,300 and that the need for ICU beds may exceed 20,000 to 30,000 at the
peak of the epidemic.

The modellers estimate that dealing with Covid-19 will require R26-billion to
R32-billion over the next six months, of which R10- to R15-billion will accrue
to the national health department. (South Africa’s health budget for this year
is about R230-billion, and the national department has allocated R20-billion to
Covid-19.)

(Note that government originally was using a very simple, but possibly no less
prescient, model developed by some of the members of this consortium, that we
described here.)

Actuarial Society of South Africa

On 29 April the Actuarial Society of South Africa warned that as many as 48,000
people may die of Covid-19 in the following four months if “government does not
maintain a strict approach to flattening the curve”. The society also projected
that the need for ICU beds may peak at 10,000, significantly above the estimated
3,300 capacity. The society is open about the very significant uncertainty in
its model and has indicated that it is being updated, with new projections to be
shared soon. (This Powerpoint presentation of the ASSA model, dated 21 May, may
indeed be an update, but we’re struggling to read the graphs.)

Van den Heever

A model by Alex van Den Heever of the Wits School of Governance looks at
strategies for keeping the epidemic suppressed, or, to put it technically, to
get R below 1 (see this explanation of R). His model is not concerned with
trying to predict the future. Some of the interventions he considers are testing
and contact tracing, lockdown, closing borders, social distancing and workplace
protocols. He also considers the effect of cold and warm weather (other
coronaviruses transmit a bit less efficiently in warm weather so this is a
reasonable assumption for SARS-CoV-2).

Van den Heever has argued that if government is using a strategy that assumes R
goes above 1 (like the one we described above), then it has effectively given
up, because the epidemic will run out of control. His model on the other hand
envisages the switching off and on of a selection of interventions as the
epidemic is suppressed here and resurges there. Vital to this strategy is good
contact tracing, but Van den Heever has expressed frustration at the poor
quality of government’s test and trace strategy so far.

Van den Heever has described his model in detail here.

SABCoM

Most of the models of the epidemic are what’s called compartmental ones; they
divide the population into compartments (susceptible, exposed, infectious,
recovered, and so on) and then describe how people move between these
compartments with a set of equations.

A different kind of model is the SABCoM one developed by researchers at UCT.
This simulates how people move around and transmit or contract Covid-19. The
creators of this model deserve credit for making the computer code of their
model available online. They have applied their model to each ward in Cape Town,
but, as with the Van den Heever model, rather than predicting the future, their
main aim appears to be to see what the effect of different interventions are on
the epidemic.

Alternatives to lockdown exist, they write, but must be done properly. In
particular they emphasise the importance of effective contact tracing.

Other models

Auditing and consulting firm Deloitte has developed a model for Business for
South Africa (B4SA) to assist B4SA in its engagement with government. The model
projects the need for hospital beds, oxygen, ventilators, N95 masks etcetera.

The London School of Hygiene and Tropical Medicine has produced a model of the
South African epidemic that estimates 310,000 deaths if there are no
interventions (more than died of AIDS in South Africa in 2005, the worst year of
the HIV epidemic). Introducing social distancing as well as “shielding”
high-risk people from infection, can reduce the number of deaths considerably,
to below 100,000 if implemented very effectively. Charles Simkins explains the
model well on Politicsweb.

A group called Pandemic Data Analysis estimates only 20,000 deaths with, as they
write, “a sense that we might bring it down or decide that it is the top of our
range.”

Researchers at the University of East Anglia have used Chinese and Italian data
to estimate the number of deaths in many countries for different infection
rates, corrected for age distribution, including South Africa. We’ve previously
explained their work. DM

Jointly published by GroundUp and Spotlight.






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visits, average time spent on the website, and the what pages have been loaded.
These data are then used to segment audiences based on the geographical
location, demographic, and user interest provide relevant content and for
advertisers for targeted advertising._cc_id8 months 26 daysThe cookie is set by
crwdcntrl.net. The purpose of the cookie is to collect statistical information
in an anonymous form about the visitors of the website. The data collected
include number of visits, average time spent on the website, and the what pages
have been loaded. These data are then used to segment audiences based on the
geographical location, demographic, and user interest provide relevant content
and for advertisers for targeted advertising._kuid_5 months 27 daysThe cookie is
set by Krux Digital under the domain krxd.net. The cookie stores a unique ID to
identify a returning user for the purpose of targeted advertising._rxuuid1
yearThe main purpose of this cookie is targeting, advertesing and effective
marketing. This cookie is used to set a unique ID to the visitors, which allow
third party advertisers to target the visitors with relevant advertisement up to
1 year.ANON_ID_old3 monthsThis cookie helps to categorise the users interest and
to create profiles in terms of resales of targeted marketing. This cookie is
used to collect user information such as what pages have been viewed on the
website for creating profiles.bscookie2 yearsThis cookie is a browser ID cookie
set by Linked share Buttons and ad tags.CMID1 yearThe cookie is set by
CasaleMedia. The cookie is used to collect information about the usage behavior
for targeted advertising.CMPRO3 monthsThis cookie is set by Casalemedia and is
used for targeted advertisement purposes.CMPS3 monthsThis cookie is set by
Casalemedia and is used for targeted advertisement purposes.CMST1 dayThe cookie
is set by CasaleMedia. The cookie is used to collect information about the usage
behavior for targeted advertising.DSID1 hourThis cookie is setup by
doubleclick.net. This cookie is used by Google to make advertising more engaging
to users and are stored under doubleclick.net. It contains an encrypted unique
ID.google_push5 minutesThis cookie is set by the Bidswitch. This cookie is used
to collect statistical data related to the user website visit such as the number
of visits, average time spent on the website and what pages have been loaded.
This collected information is used to sort out the users based on demographics
and geographical locations inorder to serve them with relevant online
advertising.i1 yearThe purpose of the cookie is not known yet.id3 monthsThe main
purpose of this cookie is targeting and advertising. It is used to create a
profile of the user's interest and to show relevant ads on their site. This
Cookie is set by DoubleClick which is owned by Google.IDE1 year 24 daysUsed by
Google DoubleClick and stores information about how the user uses the website
and any other advertisement before visiting the website. This is used to present
users with ads that are relevant to them according to the user profile.IDSYNC1
yearThis cookie is used for advertising purposes.KADUSERCOOKIE3 monthsThe cookie
is set by pubmatic.com for identifying the visitors' website or device from
which they visit PubMatic's partners' website.KTPCACOOKIE1 dayThis cookie is set
by pubmatic.com for the purpose of checking if third-party cookies are enabled
on the user's website.ljt_reader1 yearThis is a Lijit Advertising Platform
cookie. The cookie is used for recognizing the browser or device when users
return to their site or one of their partner's site.loc1 year 1 monthThis cookie
is set by Addthis. This is a geolocation cookie to understand where the users
sharing the information are located.mc1 year 1 monthThis cookie is associated
with Quantserve to track anonymously how a user interact with the
website.mt_mop1 monthStores information about how the user uses the website such
as what pages have been loaded and any other advertisement before visiting the
website for the purpose of targeted advertisements.personalization_id2 yearsThis
cookie is set by twitter.com. It is used integrate the sharing features of this
social media. It also stores information about how the user uses the website for
tracking and targeting.suid_legacy1 yearThis cookie is used to collect
information on user preference and interactioin with the website campaign
content. This cookie is used for promoting events and products by the webiste
owners on CRM-campaign-platform.TDCPM1 yearThe cookie is set by CloudFlare
service to store a unique ID to identify a returning users device which then is
used for targeted advertising.TDID1 yearThe cookie is set by CloudFlare service
to store a unique ID to identify a returning users device which then is used for
targeted advertising.test_cookie15 minutesThis cookie is set by doubleclick.net.
The purpose of the cookie is to determine if the user's browser supports
cookies.tluid3 monthsThis cookie is set by the provider AdRoll.This cookie is
used to identify the visitor and to serve them with relevant ads by collecting
user behaviour from multiple websites.tuuid1 yearThis cookie is set by
.bidswitch.net. The cookies stores a unique ID for the purpose of the
determining what adverts the users have seen if you have visited any of the
advertisers website. The information is used for determining when and how often
users will see a certain banner.tuuid_lu1 yearThis cookie is set by
.bidswitch.net. The cookies stores a unique ID for the purpose of the
determining what adverts the users have seen if you have visited any of the
advertisers website. The information is used for determining when and how often
users will see a certain banner.uid5 months 27 daysThis cookie is used to
measure the number and behavior of the visitors to the website anonymously. The
data includes the number of visits, average duration of the visit on the
website, pages visited, etc. for the purpose of better understanding user
preferences for targeted advertisments.uuid1 year 27 daysTo optimize ad
relevance by collecting visitor data from multiple websites such as what pages
have been loaded.VISITOR_INFO1_LIVE5 months 27 daysThis cookie is set by
Youtube. Used to track the information of the embedded YouTube videos on a
website.wfivefivec1 year 1 monthThe domain of this cookie is owned by Dataxu.
The main business activity of this cookie is targeting and advertising. This
cookie tracks the advertisement report which helps us to improve the marketing
activity.xbc2 yearsThis cookie is used for optmizing the advertisement on the
website more relevant by analysing the user behaviour and interaction with the
website.YSCsessionThis cookies is set by Youtube and is used to track the views
of embedded videos.

Others
Others
Other uncategorized cookies are those that are being analyzed and have not been
classified into a category as yet.

CookieDurationDescription__cflb23 hoursThis cookie is used by Cloudflare for
load balancing.ajs_group_idneverThis cookie is set by Segment.io. The purpose of
the cookie is currently not identified.revengine-browser-tokensessionRevEngine
Data Tool.UserMatchHistory1 monthLinkedin - Used to track visitors on multiple
websites, in order to present relevant advertisement based on the visitor's
preferences.vicinity_id1 year 10 months 24 days 11 hoursVicinity Advertising.

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