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Iran and COVID-19

The Unfolding of a Humanitarian Disaster

Vahid S. Bokharaie, PhD∗

March 24, 2020

Update on March 29, 2020: Chapter 5 added.

Notes

•If you want to understand the mathematical model used in the fol-

lowing simulations, you can look at this manuscript.

•If you have any reliable statistics of the spread of COVID-19 in Iran,

please send your information to

•If you want to run the simulations yourself and try other scenarios

which are not considered in this report, you can download the code

from this GitHub repository. The code is written in Python program-

ming language.

•Please share this report as much as you can. You can follow my

tweets on the latest updates in here.

∗V. S. Bokharaie is with Max Planck Institute for Biological Cybernetics, T¨

ubingen,

Germany (email: vahid.bokharaie@tuebingen.mpg.de)

1

2

Summary of the Report

In the following report, using a model adapted to real-world data of the

spread of COVID-19, it is shown that if COVID-19 is spread in Iran with-

out any containment policy, which is more or less the case at the moment

of writing this text, it can lead to disastrous consequences. Although the

reports show that the current situation in Iran is already very bad, simu-

lations show that the worse is yet to come. The peak number of infected

people in Iran will probably happen in 1.5-2 months from now. Simula-

tions show that in an uncontained human population with the age struc-

ture of the Iranian population, at the peak of the infection, around 11% of

the population will be infected simultaneously. And eventually, 65% of the

population will be infected. For a country as big as Iran, these peak values

might reach in different days in different cities or regions. But all are ex-

pected to reach these peak values days or weeks apart. If we assume only

5% of those infected with COVID-19 need respiratory or intensive care,

the total number of hospital beds needed at the peak of the epidemic in

Iran is around 467,000 beds. Also, in the uncontained scenario, eventu-

ally 65% of the population will be infected with COVID-19 in a matter of

months, which is around 55 million people for a country of around 85 mil-

lion inhabitants. Even with a mortality rate of 0.2%, which admittedly is

optimistic for such a huge number of potential patients, total death would

amount to around 110,000 people.

To avoid such an unprecedented disaster, there is an immediate need

to implement effective containment strategies. I have presented a de-

tailed quantitative analysis of the effectiveness of various containment

policies. The most effective, among the ones humanly possible, is a to-

tal lock-down of the population, closing down all governmental ofﬁces,

companies, banks and schools and forcing people to stay home. A policy

which is already enforced by governments in different countries around

the world. Simulations show that if such a policy is imposed today, it can

bring down the total number of infected to around 7% of the population,

down from 65%. But even in such a case, considering the current level of

the spread of the COVID-19 virus in the Iranian population, eradicating it

would take months. It needs patience and determination and a long-term

strategy to manage the number of infected people in the population until

the vaccine for COVID-19 is publicly available.

Contents

Contents 3

1 Worst case Scenario: Uncontained Population 5

2 Two Technical Notes 9

2.1 Mortalityrates ......................... 9

2.2 Initial Conditions . . . . . . . . . . . . . . . . . . . . . . . . 10

3 The Current Status 12

4 The future Outlook under different Scenarios 14

4.1 Uncontained........................... 14

4.2 Schools remains shut, ofﬁces open . . . . . . . . . . . . . . 14

4.3 Self-imposed Strict Social Distancing, but not a Lock-down . 16

4.4 Self-imposed Social Distancing for only three months . . . . 19

4.5 TotalLock-down ........................ 21

5 Long-term Strategies 23

3

Introduction

This report presents a model for the spread of COVID-19 in Iran. Also,

a detailed analysis of the effectiveness of various possible containment

strategies, and short-term and long-term plans.

4

Chapter 1

Worst case Scenario:

Uncontained Population

In the following simulations, I have assumed people infected with COVID-

19 and then cured, have immunity to the virus. Let’s consider the case in

which COVID-19 is spread uncontained in Iran, i.e. the case that people in

the society interact with each other as in normal times, with no external

or self-imposed restrictions in the interactions. Figure 1.1 shows how the

ratio of Infectious people changes in the 18 months after the introduction

of COVID-19 virus in the population. In case you are not familiar with the

term Infectious, it refers to those who are infected and can also transmit

the disease. For COVID-19, it seems everybody who is infected is almost

immediately Infectious, so in this case they can be used interchangeably.

The model can also give use the progress of the disease in age groups, as

can be seen in 1.2.

Please note that the output of the mathematical model is the ratio of

Infectious and Removed individuals in each age groups. Age groups are

deﬁned as as 0-10, 10-20, ..., 70-80 and 80+. Also, Day 1 in these plots

falls approximately in early January, which means the time to reach the

peak value of simultaneous Infectious, around 11%, is on day 135, which

is 1.5-2 months from now. But this estimate for the time to peak depends

on the initial conditions set for the model, which are discussed in detail

in Section 2.2.

These two ﬁgures, can already provide a picture of the disaster in the

making. The most important conclusion is that, at the peak, around 11%

of the population are simultaneously infected with the virus. In this re-

5

CHAPTER 1. WORST CASE SCENARIO: UNCONTAINED POPULATION 6

Figure 1.1: Aggregate ratio of Infectious in uncontained scenario.

Figure 1.2: Ratio of Infectious in each age group in Uncontained scenario

for Iran

CHAPTER 1. WORST CASE SCENARIO: UNCONTAINED POPULATION 7

port, I mainly talk about the population of Iran as a whole. But for a

country as big as Iran, it is expected that different cities or regions would

reach this peak value with some time-delay with respect to each other.

But the overall trajectory will be more or less the same. Even if 5% of the

infected people might need respiratory or intensive care, there is a time-

period of days or weeks in which around 467,000 people in Iran might

need support care in hospitals.

To get a picture of the total number of people who will be infected with

COVID-19 in the uncontained scenario, we should have a look at the ratio

of the Removed, as shown in Figures 1.3 and 1.4. Removed, also called

Recovered compartment, refers to those who were infected and are not

infected any more. Either because they become healthy, or because they

lost their lives. I will keep using the terms Removed, because term Re-

covered causes confusion for some readers as they assume it includes inly

those who have are cured. As we can see, in the uncontained scenario,

eventually 65.17% of the population will be infected with COVID-19. For

a country of around 85 million inhabitants, that is around 55 million peo-

ple. It should be note that this ratio is signiﬁcantly lower than countries

with relatively older population. For example, in Germany, the eventual

ratio of infected people in the uncontained scenario is around 80%. Ger-

many is now in lock-down but the only ofﬁcial policy imposed in Iran has

been to shut down the schools in late February. But as we will see, this is

far from enough to slow the rate of infections.

CHAPTER 1. WORST CASE SCENARIO: UNCONTAINED POPULATION 8

Figure 1.3: Aggregate ratio of Removed people (those who were infected

and then were cured or lost their lives) in uncontained scenario in Iran.

Figure 1.4: Ratio of Removed in each age group in Uncontained scenario

for Iran

Chapter 2

Two Technical Notes

2.1 Mortality rates

It should be noted that the possible mortality rate in Iran or any other

population is neither part of the model, nor affects the outcome of the

simulations. The compartment of ’Removed’ in this model includes people

who were infected and are not infected any more, either because they are

cured or because they died. But in the interest of brevity, I refer to this

group only as ’Removed’.

But we can still make an intelligent guess and try to come up with a

rough estimate of the mortality rate. It is important to know what ratio of

the 55 million people who, in the worst case scenario, might be infected

with COVID-19 might die.

If we look at the current statistics on the mortality rates in different

country, we get wildly different results. At the time of writing this text, it

varies from 0.38% in Germany to more than 9% in Italy, 4.65% in Hubei

province in China and a current global rate of 4.40%. These values are the

ratio of conﬁrmed death because of COVID-19, to total conﬁrmed cases of

COVID-19 in the population. And there are a few reasons for such a wild

differences in these mortality rates. Ignoring the deliberate tampering

with the statistics by certain government, there are few technical reasons

for such differences. One is attributing death caused by COVID-19 to

other factors. But given the awareness of COVID-19 all around the world,

we can ignore those cases. But more importantly, if the total number of

conﬁrmed cases are less than the actual Infectious people, which is always

9

CHAPTER 2. TWO TECHNICAL NOTES 10

the case, then reported ratio is an overestimation. On top of that, if the

number of patients that need respiratory and intensive cares exceed the

capacity of the healthcare system of a country, which seems to be what is

happening in parts of Italy, then the mortality rate starts to soar.

If we consider the statistics in Hubei province as a benchmark (and

hope that Chinese authorities have not tampered with the data too much),

and if we assume only 1 in 10 of positive cases have become noticeable

and consequently tested for the COVID-19, then the actual death rate

should be close to 0.46%. But the population pyramids in China and Iran

are completely different. For example, 52.6% of the Chinese population

are under 40, while that ratio in Iran is 66.8%. So, if as a rough estimate,

we assume mortality rate in Iran is even less than half of Hubei Province

in China, we can use the value of 0.2% as a rough, and quite possibly

optimistic, estimate for mortality rate in Iran. But we should keep in mind

that this ﬁgure is obtained under the assumption the health-care system is

not overloaded (or at least not more than what it was in Wuhan city) and

the care and assistance required for patients with more severe symptoms

are provided. But as the number of patients in Iran increase, mortality

rate would increase as well, which is a likely scenario if nothing is done

to contain the spread of COVID-19.

But even with the estimate of 0.2% death toll for Iran in the uncon-

tained scenario would be around 110,000. A catastrophic outcome for the

Iranian society.

2.2 Initial Conditions

To run the simulations that has led to ﬁgures such as the ones presented

above, I needed to assume an initial condition for the number of Infectious

in the population. Also, to ﬁgure out where in this curve are we now, we

should know when is day 1, the day that the virus was introduced into

the populations. It should be noted that the model accepts external Infec-

tious as inputs in various times during the spread of the virus. But I have

ignored that possibility and assumed a ﬁxed number of initial Infectious

in the population. The justiﬁcation for that decision is that when the virus

starts to spread and number of Infectious increase, very soon the existing

population of Infectious is of orders of magnitude higher than incoming

infected individuals. So, adding these external inputs does not really af-

CHAPTER 2. TWO TECHNICAL NOTES 11

fect the simulation outcomes. And apart from that, when the numbers of

Infectious are so low, the model, which is based on continuous ordinary

differential equations, becomes unreliable.

To estimate the population of the initial group at the beginning of the

spread of COVID-19 in Iran, given the lack of any reliable information,

I had to rely on circumstantial evidence. For example, a former health

minister has said that he has warned the authorities about the spread

of COVID-19 in late December. Given the fact that the Chinese authori-

ties were still in denial at that stage (as late as January 14th, the ofﬁcial

line was that there is no strong evidence for human to human spread of

COVID-19) and assuming the claims of this former minster are true, then

the number of people infected with COVID-19 and the number of those

who needed intensive care should have been high enough to make it dis-

tinct from the number of the cases of seasonal ﬂu. Based on that and

other reports of citizens in social networks, I have assumed that at the

end of December, 1 in 100,000 of the population have been infected with

COVID-19, which for a country of 85 million population, is 8500 people.

Please note that variations in this initial population does not change

the eventual infected ratio of 65%. It changes the number of days it takes

to reach there, and more importantly, the day in which we reach peak

value of Infectious. With the above mentioned estimate, that day as can

be seen in Figure 1.1 is 135 days from early January, which is mid-May. If

the initial population is of an order of magnitude lower than our estimate,

we reach the peak at day 171, and if it is of an order of magnitude higher

than our estimate, then it will arrive in day 98, which is two days from

now. But given other evidence, I have chose 1 in 100,000 in early January

as the initial conditions for the simulations.

Chapter 3

The Current Status

For all practical purposes, spread of COVID-19 has been uncontained in

Iran. The only ofﬁcial containment policy has been to shut down schools

and universities one months ago. Banks, governmental ofﬁces has been

open, with the exception of the last week of Persian year (3rd week of

march), which they have worked in half capacity, and the ﬁrst few days of

the Persian year which they have been closed. But they are all open now.

Schools, as it is customary in Iran, will stay closed for the ﬁrst two weeks

of Persian new year (end of 1st week of April). At the moment, there is

no ofﬁcial statement on whether ot not they will be reopened then.

I have assumed shutting down schools and universities means 90%

decrease in contacts of the population under the age of 20 and that half-

closing banks and governmental ofﬁces and partial self-imposed social-

distancing by adults aged 20-70 means 50% decrease in the number of

their interactions. And I have assumed no major changes in the lifestyle

of those older than 70. With all that in mind, the model tells us that the

current situation in Iran is as shown in Figures 3.1 and 3.2.

The effect of closing banks and ofﬁces can be seen in the abrupt change

in the number of Infectious. Total number of Removed is also decreased to

2.74% from the 3.49% that it could be in uncontained scenario. To see how

things can develop from now on, we need to simulate various scenarios,

as shown in the following sections.

12

CHAPTER 3. THE CURRENT STATUS 13

Figure 3.1: The estimate for the current number of Infected people in

Iran.

Figure 3.2: The estimate for the total number of people who were in-

fected but are not any more (dead or cured), which will be referred to as

Removed compartment in this text.

Chapter 4

The future Outlook under

different Scenarios

Given what the models tells us about the current status, the natural ques-

tions that arises is the effect of different containment strategies on the

peak number of Infectious and also total number of Infectious in the Ira-

nian population. Let’s start with the worst possible policy to adopt, un-

contained scenario.

4.1 Uncontained

Let’s ﬁrst assume the Iranian authorities decide to open the schools and

universities in the third week of the Persian year. Figures 4.1 and 4.2

show what can happen. Under such a scenario. the effect of closing down

schools for 1.5 month and ofﬁces for a couple of weeks would be neg-

ligible in the long run. The peak percentage of instantaneous Infectious

drops by around 0.7% and time to that peak changes by a few days from

135 days to 144 days. In other words, no major differences in the dynam-

ics of the spread of the virus.

4.2 Schools remains shut, ofﬁces open

The current decision is to open the governmental ofﬁces with no ofﬁcial

restrictions on gatherings, and private sector. Let’s assume Iranian author-

ities decide to keep the ofﬁces open and schools closed. The following two

14

CHAPTER 4. THE FUTURE OUTLOOK UNDER DIFFERENT SCENARIOS15

Figure 4.1:

Figure 4.2:

CHAPTER 4. THE FUTURE OUTLOOK UNDER DIFFERENT SCENARIOS16

Figure 4.3:

ﬁgures show what happens if schools remain closed for a long time. The

peak of Infectious drops from around 11% to 9.2%, and eventual number

of Removed drops to 46% of the population. That means that keeping the

schools closed in the foreseeable future without imposing any other policy

would spare around 19% of the population, equivalent to around 16 mil-

lion people, from the COVID-19 infection. Which seems good, but looking

at the trajectory of the Removed cases (those who were infected and nor

infected any more) for each age group, as shown in Figure 4.5, we can see

that this decrease is mostly because of the decrease in the number of cases

in age groups 0-10 and 10-20, whose mortality rates are very close to 0.

In other words, this policy will spare many children and teenagers from

the infection, but have minimal effect on the number of deaths because of

COVID-19.

4.3 Self-imposed Strict Social Distancing, but

not a Lock-down

Now let’s assume the Iranian authorities do not decide to impose an of-

ﬁcial lock-down, but Iranian people decide to apply social distancing in

CHAPTER 4. THE FUTURE OUTLOOK UNDER DIFFERENT SCENARIOS17

Figure 4.4:

Figure 4.5:

CHAPTER 4. THE FUTURE OUTLOOK UNDER DIFFERENT SCENARIOS18

Figure 4.6: What happens if Iranian people self-impose strict social-

distancing? Disease starts to die out but it will take months.

their everyday lives. I have assumed such a scenario means the number

of interactions among age groups until 70 years old decreases to 20%,

and for people above 70 to 50% of their normal life-styles. The following

ﬁgures show the effects of such a policy.

Good news is that such a policy will make the reproduction number,

R0, less than 1(actual value is R0= 0.68. That means each individual in

the course of the disease on average will infect less than one person, and

the disease starts to die out. Under such a policy, total number of Removed

falls from around 65% to just 6.77%, a signiﬁcant decrease. Which means

thousands of lives would be saved. The bad news is that the time that

such a policy will bring the number of Infectious to a negligible ratio is

of the order of months. In other words, although such a policy is very

effective, but it is not something that can eradicate the disease in a matter

of weeks. This can be seen if we look closely at Figure 4.6.

CHAPTER 4. THE FUTURE OUTLOOK UNDER DIFFERENT SCENARIOS19

Figure 4.7: Eventual total number of infected people will decrease signif-

icantly under strict social distancing.

4.4 Self-imposed Social Distancing for only

three months

Let’s see what happens if we stop imposing social distancing as a way of

life before the virus is eradicated. Let’s assume, people of Iran self-impose

this policy for only three months, and after that, human interactions go

back to the levels that were in their normal lifestyle. As shown in the

ﬁgures, ratio of Infectious starts to increase, although the peak is now

down to 7.54% and happens in Day 290, a few months delay compared

to 135 days in uncontained scenario. But as can be seen in Figure 4.9,

the eventual number of Removed, hence the eventual death toll would be

more or less the same. In other words, to stop this disease to eventually

infect 65% of the population, we should continue effective containments

strategies until there are strong evidence that the disease is completely

eradicated or a vaccine is publicly available. This also applies to the next

scenario.

CHAPTER 4. THE FUTURE OUTLOOK UNDER DIFFERENT SCENARIOS20

Figure 4.8:

Figure 4.9:

CHAPTER 4. THE FUTURE OUTLOOK UNDER DIFFERENT SCENARIOS21

Figure 4.10:

4.5 Total Lock-down

And ﬁnally, if an ofﬁcial Lock-down is imposed on the population, gov-

ernmental ofﬁces closed, big gatherings banned and people asked to stay

home, similar to what is happening in some countries around the world,

the future will look as shown below. In this case, I have assumes a total

lock-down means interactions of all the members of the society on average

has decreased to only 10% of what it used to be.

Again, given the current number of Infectious, eradicating the disease

will take months. But the eventual total number of infected people will

be 5.28% of the population, around 4.5 million. With the mortality rate

of 0.2%, that means the eventual death toll will be around 9,000, a signif-

icant decrease from 110,000 in uncontained case. More than a hundred

thousand lives can be saved in Iran if such a policy is imposed. And even

if merits of such a policy in the long-run can be debated, there is no justi-

ﬁcation of any sort not to impose such a policy with immediate effect for

at least one month.

CHAPTER 4. THE FUTURE OUTLOOK UNDER DIFFERENT SCENARIOS22

Figure 4.11:

Chapter 5

Long-term Strategies

In the previous section, we saw how different strategies can affect the rate

of changes of the number of Infectious people in the population. And we

saw that the total lock-down is a necessary strategy when the number of

infectious people grow fast and we need to immediately lower the growth

rate. Without such policies, Iran, or any other society for that matter, will

face a humanitarian disaster, and the whole purpose of this report is to

highlight this imminent threat.

But let’s assume that there is an immediate containment policy in place

and the number of infected starts to drop. As we saw, bringing down

the ratio of infectious to a level that is close to 0 for practical purposes

can take months. And imposing a strict containment strategy has a huge

economic impact. Apart from that, even if we eradicate the virus in the

population, there is always a possibility that it is re-introduced from other

countries who have not done so. So, the natural question is, what policy is

suitable for a long-term strategy. An obvious answer, is to impose a policy

that would to make Basic Reproduction Number,R0, to be around 1.0(for

technical details, please look at this). What kind of policy is needed to

achieve that goal? To ﬁnd the answer to that, we need to run an optimi-

sation scheme to ﬁnd the coefﬁcients for contact rates in each age groups

which would bring R0from the original estimated 2.28 for the spread of

COVID-19 in an uncontained population, to around 1.00.

In the simplest case, when we decide to impose a uniform policy to all

age groups, the coefﬁcient is 0.4342. It means that if everybody will bring

down their direct contacts with other individuals to 43% of what it was in

the normal times, the ratio of infectious would stay the same or decrease

23

CHAPTER 5. LONG-TERM STRATEGIES 24

at a slow pace. Now let’s see how these values change if we want to

impose different policies for each age groups. To make it realistic, I have

assumed a different policy for people under 20, between 20 and 70, as the

workforce in the society, and above 70. And let’s assume minimum feasi-

ble coefﬁcient is 10% for all age groups. Running the optimisation scheme

again, we get values of [0.1101,0.1954,0.9026] for these three age groups,

0-20, 20-70, and 70+. []Technical note: the optimisation problem is non-

convex, and I use a global optimisation algorithms to solve it. Algorithm,

which is sqp in Global Optimization Toolbox in Matlab c

, ﬁnd differ-

ent local minima in different runs. A more sophisticated algorithm might

be needed]. But bringing down the interactions of the working force to

19.5% of the normal level while asking elderly to bring it down only to

90% does not seem like a sound policy. Let’s set a lower threshold of 40%

to the interactions of the working force and keep it at 10% for other two

age ranges. Also, to protect the elderly, who are the most vulnerable age

group with the highest mortality rates, we set an upper limit of 20% on

their interactions. In other words, we ask them to, until further notice,

lower their interactions with other people considerably. The optimised co-

efﬁcients are now [0.3812,0.5481,0.1998]. So, under such a policy, if kids

and teenagers’ interactions are brought down to 38% and adults to 54%,

number of Infectious would stay more or less the same for a long period

of time.