Countries controlling Covid-19: Headed for a Coronavirus recovery or a second wave?

It is crucial for economies and companies to recover from the first wave of the Coronavirus and to prevent a second wave. In this article, we discuss the impact of the first wave of the Coronavirus and use Data Science analysis to derive a complete Coronavirus recovery ranking. It shows how well a country controlling Covid-19 and how far away a second wave might be. We then interpret the data for Coronavirus recovery to find out what actions the leading countries took to control Covid-19.

A man with a faceshield, facemask and a bag protecting himself and others from the second Coronavirus wave.
A country’s control of Covid-19 can help it recover from the Coronavirus and avoid a second wave. Such control can be calculated to determine if a country is likely to recover or to re-experience the horrors of the first wave.

The economy needs to recover from the consequence of Covid-19 response

Governments worldwide enforced nationwide lockdowns to flatten the curve. In other words, to buy more time to respond to the pandemic using limited medical capacity (as in healthcare staff, personal protective equipment, test kits, hospital beds, ventilators, hospital space, and so on).

While requiring people to stay at home has been effective in minimising the risk of a health apocalypse happening, the social distancing strategy has forced governments to enforce travel restrictions and the closure of ‘non-essential’ services, industries and events including cinemas, tourism, film shoots, nightclubs, concerts, festivals, arcades, sports, religious venues, farmers’ markets, schools, colleges and most offices.

A picture of Syed Rashad Imam Tanmoy shows the domino effect of the first and second coronavirus wave.
A lot of people would’ve been in danger if it weren’t for social distancing.
Image Source: Syed Rashad Imam Tanmoy

Almost everyone, whose workplaces and educational institutions have closed during the lockdown, had to bring their daily activities online while at home. While some were able to work or attend online classes using their devices, others were not as lucky due to retrenchment and the shutting down of businesses that couldn’t cope with the lockdown conditions.

And because companies have to minimise costs due to lower business activity, they’re also delaying their recruitment drives, so job seekers have to wait longer to get hired or to even get shortlisted.

Consequently, lacking a source of income will result in lower levels of consumption and spending because they’re trying to save money and spread whatever savings they have over a longer period of time. Even stimulus packages are not enough to avoid a recession. The World Bank’s Global Economic Prospects of June 2020 estimates a 5.2% contraction in global GDP this year.

When the Covid-19 is eradicated, more people will be able to earn a living and industries affected by this pandemic can resume operations. The economy will have a chance to get back up after a fall.


Nobody wants a second wave

Another reason for controlling Covid-19 is to avoid a second wave. A second wave would mean a second lockdown. And that would mean a repeat or extension of the economic problems that happened in the first wave (as mentioned above). In this scenario, global growth could shrink by almost 8% in 2020.

And of course, people really miss having fun, right? Well, the impact of Covid-19 goes beyond a receding economy and the lack of out-of-home entertainment. The obvious victims of Covid-19 are those who tested positive for the Coronavirus and the people around them. At the time of writing, a total of over 13.1 million people around the world have tested positive, of which 7.23 million have recovered while 572,411 have passed away.

Covid-19 patients receiving treatment at the Imam Khomeini Hospital in Tehran, Iran.
Covid-19 patients at the Imam Khomeini Hospital in Tehran, Iran.
Image Source: Mohsen Atai, Fars News

Another health impact of this pandemic is the disruption of prevention and treatment services of non-communicable diseases like diabetes, hypertension, cancer and cardiovascular diseases. A survey by the World Health Organisation (WHO) found that such health services have been partially or completely disrupted in many countries mainly because most medical staff have been reassigned to test and treat Covid-19 patients.

Not to forget, this pandemic has also lead to negative mental health outcomes, not only due to social isolation, but also job loss and income insecurity, worries about physical health, and burnout of frontline workers.

None of us would want to experience these issues again, do we? Let’s analyse the data to see which countries are more likely to escape a second Coronavirus wave and return to normalcy.


Using control scoring to rate how well countries are doing

In order to determine which countries are doing a good job in controlling Covid-19, we use Data Science and calculate a control score to rate each country’s performance. The formula to compute a control score rating for each country is:

Control Score = activepercent × normalisedStability ÷ activeToPopulation1000

As we can see from the formula of the control score, the variables are activepercent, normalisedStability and activeToPopulation1000.

Active percent

active cases = Total Infected – Cured – Deaths

activepercent = current active cases ÷ maximum number of active cases

  • The activepercent shows the number of current active cases compared to the highest number of active cases ever.
  • Active cases exclude those who have recovered and the victims who have passed away.

Normalised stability

normalisedStability = log [(activepercent 28 days ago) – (current activepercent)]

  • Normalised stability shows how stable the changes in the number of cases are. A higher normalised stability figure (which is absolute) shows that the changes are more stable.
  • A trend of 28 days was used because measuring over 14 days does not show enough change in some countries. However, there are other countries in the data set where the changes are huge.
  • Normalisation of data is used when there are extremely large changes in values over time in certain countries, and hence, the changes need to be reorganised in a way that they can all be analysed more easily.
  • Based on the data available, using logarithms is a suitable method for normalising data.

Active to Population1000

activeToPopulation1000 = current active cases ÷ 1000 of the population

  • The active to population1000 figure reflects the infection risk. That is, the risk of each person in every 1000 people in a country getting infected.
  • 1000 is used to standardise the population size. Comparing the number of active cases to a million or billion people will result in a very small number.

Control Score

Control Score = activepercent × normalisedStability ÷ activeToPopulation1000

  • The control score shows the control of cases with respect to infection risk (again, infection risk is the active cases per population).
  • The control score has a positive relationship with activepercent and normalised stability, and a negative relationship with infection risk. That’s why activepercent and normalised stability are multiplied and then divided by the infection risk.
A cat is typing on the keyboard, an instrument which allows us to obtain, analyse and explain data.
Computing the control scores… Image Source: Slava

Voilà! A complete ranking of Coronavirus control

We measured the data in the following table on the 13th of June 2020. The figures might be updated at another time.

The highest control score indicates that the country is best at controlling Covid-19. At the time of writing, Vietnam got the trophy for scoring the highest with a control score of 19,549, while the rest of the top 10 were occupied by other Asian and African countries.

Cuba scored the highest in the Americas with a score of 577 at 12th place while Malta scored the highest in Europe with a score of 280 at 16th place. However, the highest in Oceania is… Australia, with a low score of 33 at 45th place.

If you want to see which countries scored the lowest and are likely to experience a second wave of infections, click on the Index column to rearrange the order of the table.

IndexCountryControl Scoreactivecases
1Viet Nam1954920
2Taiwan179536
3China11845554
4Chad715210
5Thailand436270
6Mauritius29052
7Niger262439
8Cambodia234423
9Uganda214861
10Malaysia195667
11Myanmar191664
12Cuba57779
13Burkina Faso533111
14Tunisia382119
15Mongolia36828
16Malta2807
17Comoros21814
18Korea, Republic of216950
19Saint Lucia2163
20Uruguay21460
21Barbados1976
22San Marino1461
23Finland144162
24Japan1402954
25Bahamas12411
26Yemen116382
27Estonia11550
28Georgia109115
29Jordan102175
30Denmark94260
31Iceland8518
32Sri Lanka83520
33Liechtenstein792
34Jamaica69133
35Bhutan6025
36Germany556373
37Latvia55124
38Lithuania48207
39Hungary44660
40Ireland44501
41Slovakia41372
42Norway39587
43Gambia3727
44Togo33201
45Australia331962
46Zambia30505
47Austria291213
48Cyprus27156
49Switzerland271349
50Turkey2413420
51Slovenia23287
52Eritrea22125
53Antigua and Barbuda2114
54Mali19602
55Italy1813303
56Monaco189
57Sierra Leone17414
58Ghana154282
59Djibouti15223
60Guinea141205
61Nicaragua14762
62Cameroon132886
63Tajikistan111275
64Morocco103232
65Benin8795
66Poland69318
67Somalia51695
68Singapore53731
69Afghanistan512237
70Belize515
71Sudan54259
72Serbia53911
73Iran (Islamic Republic of)424816
74Maldives4383
75Zimbabwe4644
76Luxembourg3646
77Guyana3126
78Nepal38239
79Haiti33961
80Qatar24048
81Suriname2228
82Belarus29389
83Pakistan286975
84Guinea-Bissau21043
85Chile224034
86United Arab Emirates29474
87Czechia24536
88Canada127548
89Croatia11088
90Portugal113912
91South Sudan11650
92Kazakhstan124175
93Kuwait19711
94Spain175129
95Congo15559
96Russian Federation0.0211667
97Bahrain0.04538
98Moldova, Republic of0.06112
99Sao Tome and Principe0.0429
100Gabon0.02892
101Peru0.096876
102Brazil0.0524293
103North Macedonia0.03519
104Armenia0.011414
105Ukraine0.026039
106Saudi Arabia0.061903


However, control scoring has its limitations

There are certain challenges that stand in the way of comparing the performance of different countries with complete certainty.

Some countries were not included in the ranking because they have small population sizes, nearly no cases or no reported recovery numbers, leading to skewed figures of cases growth trend. For example, Ireland has a small population of 4.9 million people and reported a total of 25,670 cases. So it would look as if Ireland is doing badly although the number of cases have dropped significantly since May.

Country/RegionReported DeathsCases growth trend
Timor-Leste01
Lao People’s Democratic Republic01
Holy See01
Tanzania, United Republic of211
New Zealand221.0265957446808511
Belgium97821.0425748522981408
Netherlands61581.0468519731455708
United Kingdom of Great Britain and Northern Ireland448831.0678331189119648
France300071.0718444700935734
Western Sahara11.1111111111111112
Saint Kitts and Nevis01.1333333333333333
Trinidad and Tobago81.1367521367521367
Greece1931.212082262210797
Papua New Guinea01.375
Fiji01.4444444444444444
Ecuador50311.4498446803002847
Sweden55261.470577840607881
Romania18711.479726924673647
Lebanon361.5034674063800277
United States of America1347771.5646465581318674
Senegal1451.6040832666132907
World5649911.638961217530631
Algeria10041.7309898242368178
Egypt37691.8882736156351791
Dominican Republic8801.9100655679603047
Israel3541.9746995572422517
Indonesia35351.9780331373597009
Nigeria7242.0397270756281083
Mozambique92.0524412296564196
Mexico347302.0692970775807695
Central African Republic532.08458920758386
Philippines13722.135396975425331
Bangladesh23052.1466123087498072
Bulgaria2672.196876913655848
Panama8932.2100802632234906
Cabo Verde192.2355371900826446
Paraguay212.2363203806502776
Liberia472.2376681614349776
Burundi12.2470588235294113
Albania892.3025956284153
Syrian Arab Republic162.3176470588235296
Bosnia and Herzegovina2192.3225025924645695
Ethiopia1242.337965887555275
Equatorial Guinea512.3514548238897395
Rwanda42.401109057301294
Azerbaijan2982.4577847439916405
Oman2482.4775558273316123
Uzbekistan572.5197341925090617
Côte d’Ivoire822.566625412541254
El Salvador2542.606439078545656
Bolivia (Plurinational State of)17542.645443335948885
India226732.647129208966665
Eswatini182.697530864197531
Kenya1842.81342204223315
Colombia52022.995616461675959
Guatemala11723.0131703719313028
Mauritania1473.1361474435196195
Venezuela (Bolivarian Republic of)853.1604683195592287
Argentina18103.218649942234692
Honduras7713.262329982259018
Libya383.322966507177034
Angola233.347826086956522
Montenegro233.5925925925925926
Madagascar343.656549520766773
Iraq30553.96802110817942
South Africa39714.018863332116344
Malawi334.274102079395085
Costa Rica284.350782190132371
Kyrgyzstan1294.716810149524242
Botswana15.233333333333333
Seychelles09.090909090909092
Namibia120.874999999999996
Lesotho146

Another possible limitation that could affect the control scores is the selective testing of symptomatic people, which may have influenced the daily reports of cases. This means that people who display the symptoms of Covid-19 or are likely to be exposed to the Coronavirus have been tested, due to the scarcity of testing kits and medical capacity, instead of a random testing method or testing the whole country.

Examples of groups of people with a higher risk of exposure are the homeless and the foreign blue-collar workers living in cramped spaces. Their circumstances didn’t allow them to take the necessary precautions to prevent transmission. Meanwhile, people who stayed at home were less likely to show up at a testing facility, so the asymptomatic cases were probably not taken into account.


Nevertheless, we can learn something from the high ranking countries

There may be imperfections in data collection and control score computation but the control scores still give us a rough idea of how well (or not well) the countries are doing.

Over the past few months, we’ve seen some of the high ranking countries being praised for their efforts in handling the pandemic situation. They implemented some strategies that seem similar to one another. Here’s a summary of the steps taken:

  1. They quickly cancelled outgoing flights, closed the borders, implemented health checks at airports and quarantined everyone flying into these countries upon hearing initial reports of infections in China.
  2. Contact tracing has been conducted in an attempt to break the chain of transmission. Anyone suspected of exposure to Covid-19 would have to report themselves through the respective health ministry’s online reporting system.
  3. Strict lockdown conditions were enacted to reduce exposure to Covid-19 and make social distancing, contact tracing and testing much easier to handle.
  4. Awareness campaigns were held to communicate the seriousness of the Coronavirus and the importance of measures like social distancing and handwashing to the public.
  5. They were quick in restocking their test kits to enable more tests to be done.

To avoid undoing every effort put into controlling the virus, we should continue taking the necessary measures. We know that some governments are starting to allow people to go out (equipped with masks and hand sanitisers) in light of a Coronavirus recovery.

But the loosening of movement restrictions doesn’t mean that the Covid-19 has been defeated. Recovery doesn’t mean recovered, so continue the social distancing, handwashing and public mask-wearing, okay?


Summary

Why is it so crucial to control the Coronavirus?

– The lockdowns enforced to flatten the curve have resulted in a receding economy.
– Avoiding a second wave would help the economy get back up and minimise the health issues resulting from the first wave.

How can we compute the control score to rate how well countries are controlling Covid-19?

Control Score = activepercent × normalisedStability ÷ activeToPopulation1000

Whereby the variables are:

active cases = Total Infected – Cured – Deaths

activepercent = current active cases ÷ maximum number of cases

normalisedStability = log [(activepercent 28 days ago) – (current activepercent)]

activeToPopulation1000 = current active cases ÷ 1000 of the population

Why does our Coronavirus control scoring not work perfectly?

– small population sizes
– selective testing

Which actions did high scoring countries take?

– Cancelled outgoing flights
– Closed borders
– Implemented health checks at airports
– Contact tracing
– Strict lockdown conditions
– Awareness campaigns
– Quickly restocking and upscaling their test kits

2 Responses

  1. FDD says:

    TAIWAN IS NOT A PART OF CHINA

    • Tim says:

      Thanks for the feedback. Our goal is data analysis and not to do political statements. We adjusted the labeling to purely Taiwan and removed the province of China to allow pure data focus. Please excuse this political mistake, it appeared through the merging of data sets and not of any political intention.

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