Covid‐19 Control and the Economy: Test, Test, Test*
| Published date | 01 October 2021 |
| Author | Abderrahim Taamouti |
| Date | 01 October 2021 |
| DOI | http://doi.org/10.1111/obes.12442 |
Covid-19 Control and the Economy: Test, Test,
Test*
ABDERRAHIM TAAMOUTI
Department of Economics and Finance, Durham University Business School, Mill Hill Lane,
Durham, DH1 3LB, UK (e-mail: abderrahim.taamouti@durham.ac.uk)
Abstract
Hard lockdowns have left policymakers to face the ethical dilemma of choosing
between saving lives and saving the economy. However, massive testing could have
helped to respond more effectively to Covid-19 crisis. In this paper, we study the
trade-off between infection control, lockdown and testing. The aim is to understand
how these policies can be effectively combined to contain Covid-19 without damaging
the economy. An extended SIR epidemic model is developed to identify the set of
testing and lockdown levels that lead to a reproduction number below one, thus to
infection control and saving lives. Depending on whether the testing policy is static or
dynamic, the model suggests that testing 4% to 7% of the population is the way to
safely reopen the economy and the society.
I. Introduction
Covid-19 has created an unprecedented global health and economic crisis, which
caused thousands of deaths, threatened business and wiped out millions of jobs.
Almost every country in the world had to take extreme measures to control the spread
of the virus and save lives. One of these measures is lockdown that halts all but
essential businesses. IMF, however, predicted that hard lockdowns are likely to cause
the worst recession in a century. Another measure, namely testing, was introduced by
officials to help control the infection, but the majority of the countries used it at a
small scale. The World Health Organization (WHO) believes that large-scale testing is
necessary to stop the virus, and its Director-General once said ‘We have a simple
message for all countries: test, test, test.’In this paper, we develop an extension of
SIR epidemic model to study the trade-off between infection control, lockdown and
testing. The objective is to understand how these measures can be effectively
combined to contain Covid-19 without damaging the economy too much.
JEL Classification numbers:C02, C61, H00, I10, I30.
*The author thanks very much the Editor Climent Quintana-Domeque and an anonymous referee for their
very constructive comments. This paper also benefited from comments from Debraj Ray and discussions with
Majid Al-Sadoon, Nejat Anbarci, Bo Zhou and Weidong Lin. All errors remain mine.
1011
©2021 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivsLicense, which permits use and
distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 83, 5 (2021) 0305-9049
doi: 10.1111/obes.12442
The lockdown policy has contributed significantly to reducing the transmission of
Covid-19 in all affected countries, which has saved thousands of lives. However, this
was at the cost of bringing the economy to a standstill. To minimize Covid-19’s
economic impact, countries of the world were invited to find ways out of their
lockdowns without causing another epidemic outbreak. But without a vaccine, most
countries were struggling to provide an exit roadmap that balances between reopening
the economy and controlling the infection. Testing is another measure that has been
used by countries to reduce the infection. The WHO views large-scale testing as an
effective way to stop the virus, and called on countries around the world for urgent
action to join forces to identify, isolate and contact trace people with Covid-19.
Moreover, we know for a fact that the countries –including Iceland, China, Germany
and South Korea –that built a high-testing capacity (they tested a greater proportion of
their population than the rest of the world) fought the pandemic in an effective way by
finding out those who have Covid-19 and quickly isolate or/and treat them. These
countries that have managed to keep their case counts and deaths tolls low have also
reopened their economies earlier than most other countries in the world.
Given the high importance of the debate on how countries can contain the virus
and save their economies, in this paper we are interested in studying the trade-off
between Covid-19 infection control and the two policies discussed previously. Our
main motivation is simply to understand how to effectively combine these policies so
that the infection remains under control and the lockdown is lifted at least partially. To
achieve our objectives, we use a key measure of the contagiousness of an infectious
disease, namely the reproduction number R, which tells us how many people will
contract the disease from a person with this disease. This number is extremely valuable
for scientists and policymakers as it can tell if a disease, like Covid-19, can or not
cause a proper epidemic outbreak. Our analysis can help determine the levels of
lockdown and testing that are needed to push Rbelow 1 –the level at which the
disease starts to decline –without risking too much the economy, which should guide
countries in need of defining a roadmap for easing the lockdown.
To obtain our results, we first develop a simple extension of SIR epidemic model
that links the reproduction number to lockdown and testing policies. We then examine
two versions of the extended model, one in which the testing policy is static and the
other one in which the testing policy is dynamic. Using the model with the static
testing policy (constant testing capacity), we identify the set of testing levels (fractions
of the population tested each day) and lockdown levels (fractions of the economic
activity shut down during the lockdown) that lead to a reproduction number below
one, thus to infection control and saving lives. We show that this set is not empty as
long as the false negative rate of the used test is strictly below one and people who
contract the virus are self-isolated. A numerical illustration of this model indicates that
when a soft lockdown is imposed, say 10% shut down of the economic activity, then
high testing capacity of as much as testing 4% of the population each day is needed to
have the reproduction number below 1. When the lockdown is slightly higher, say
30% shut down of the economic activity, then testing about 1.5% of the population
daily will be enough to get the infection under control. We provide the sets of all
levels of lockdown and testing capacity that produce different levels of R.
©2021 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd.
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