Covid-19 statistics: What medical resources are needed to treat the coronavirus?

A nurse wears a protective facemask as she speaks to an ill patient in an EHPAD (Housing Establishment for Dependant Elderly People) in France, amid a spread of Covid-19. It is unclear if the elderly are more susceptible to the virus because of the virus itself or their weakened immune system. — AFP

Sometimes, statistics can be scary, or at least, made to look more menacing than what these unbiased, risk-adjusted numbers really say.

Conversely, threatening, inconvenient data can be slighted, ignored, or perhaps misrepresented as something harmless, or even beneficial.

Both the above cases apply to the reporting of data regarding the spread of SARS-CoV-2 and the resultant pandemic of Covid-19.

Looking from a statistical point of view, one thing stands out starkly.

The data available about Covid-19 is currently incomplete, hence media speculation about the mortality (death) rate and prevalence of infection is mostly that: Raw, uninformed speculation – and much of it is about as useful as the lies that drinking silver solutions or eating garlic can prevent Covid-19 (note that silver is not even a metal involved in any human bodily processes).

That is not to say the current data available about Covid-19 is useless.

In statistics, where data is incomplete, several techniques can be applied to “clean up” the numbers and extrapolate the likely scenarios needed to manage the scope, extent and damage done by SARS-CoV-2 in various countries.

And we need to know this information as soon as possible, so that the world can know what measures are the most effective in defeating this coronavirus pandemic NOW, and also importantly, to take measures that can prevent recurrences in the future.

Risk factors

Although there is much media attention on the different death rates of older people and people with other health conditions due to Covid-19, the statistics about such risk factors are often not as clear-cut as they appear.

For example, Covid-19 may not necessarily always be the killer pathogen in such patients, but the weakening of their immune system due to their age or other health conditions may allow other normal, low-level pathogens in the body to become virulent and deadly.

Also, it is not clear how statistics are recorded for deaths in people with more than one health condition, e.g. how would a death be recorded for someone aged over 70 with both heart disease and diabetes?

To have better visibility of the real factors involved in reported Covid-19 deaths, all deaths should be recorded with all co-morbidities, so that better risk factors and probabilities can be calculated for people with more than one at-risk condition.


Apart from death, the other extremely undesirable outcomes of contracting Covid-19 would be either intensive medical treatment or hospitalisation.

These events are depicted by measures such as the Case Critical Rate (CCR).

The CCR can be sub-divided into more granular statistics, and this division could, for example, be reflective of the demographics in each country.

For example, CCR-0 may be the percentage of infected people who require medical treatment in a quarantine facility where several treatment drugs may be offered.

CCR-1 may be for the percentage of patients who require intensive care and/or life-support equipment such as ventilators.

CCR-2 may cover the percentage of people who transition from CCR-0 to CCR-1 patients.

These statistics can help determine or manage the treatment capacities in local available facilities or the numbers that would need to be relocated for treatment elsewhere.

A more gruesome use of CCR data in conjunction with risk factors data, may be for the selection and prioritising of treatment for patients requiring rare resources such as ventilators, in the event of overwhelming demand for such facilities.

The CCR data for China is interesting. It indicates that 13.8% of infected people required significant medical care and 4.7% of infections needed intensive medical care.

The facilities in China are probably not equivalent to the facilities in other countries, for example, they build several new hospitals specifically for Covid-19 patients.

Therefore, reliable CCR estimates are important statistics to derive as early as possible so that countries can plan the health resources necessary to treat the likely numbers of afflicted people.

Comparing Covid-19 with influenza

There are often many comparisons made between Covid-19 and influenza, usually with suggestions that they are somehow comparable.

From a disease point of view, both are undoubtedly members of the virus family, and have more or less the same means of infection and roughly similar initial symptoms of infections.

Factually, that is where the similarities end.

SARS-CoV-2 is part of the Coronaviridae family of viruses, in the Nidovirales order with a RNA (ribonucleic acid) genome of 29,903 bases.

Such a large genome is certain to undergo frequent mutations and several new strains have already been discovered.

Influenza viruses come from the Orthomyxoviridae family in the Articulavirales order and have a RNA genome with around 13,500 bases. Currently, there are three strains that can infect humans.

Saying the two viruses are the same is like comparing a Doberman dog with a poodle, though both are undoubtedly dogs and both are capable of damaging the furniture, but it is certain that the Doberman can do rather more damage.

Even the World Health Organization (WHO) describes SARS-CoV-2 as “a unique virus with unique characteristics”.

One important difference may be that SARS-CoV-2 appears to survive longer on inanimate objects (e.g. tables, door handles, clothes, etc) than influenza viruses.

Only 1.38% of influenza patients in the United States require hospitalisation, compared to 4.7% of Covid-19 patients in China who require intensive care.

There is no evidence the health systems in the US can cope with such levels of demand for hospital treatment if the number of Covid-19 patients reach the same number as flu sufferers.

Additionally, 13.8% of people with Covid-19 in China require an unspecified amount of increased medical resources and medications.

In short, Covid-19 can easily and comprehensively overwhelm the medical resources of any affected country.

However, disturbingly, the probability of such widespread contagion by Covid-19 (unless somehow contained) would seem to be almost certain as the R0 for influenza is 1.5, compared to Covid-19’s R0 of 2.6 (estimated in Wuhan).

It appears that the only way Covid-19 can be contained is if the entire world adopts the containment strategies enforced by China.

Some early (and probably inaccurate) CAse Fatality Rate (CFR) data suggests that Covid-19 may be 18 times more fatal than influenza, once symptoms are expressed.

Statistically, it would not be surprising if some countries end up with a final CFR higher than the (roughly) 1.84% indicated by South Korea.

Much would depend on determining the numbers of SARS-CoV-2 carriers who display very mild or no symptoms of Covid-19.

Note that this rough estimate is taken from South Korea, which has done the most testing on its population, and therefore, its statistics for infection is likely to encompass most of the infected people in the country.

Currently, we have no more reliable CFR data than that.

Chris Chan works in advanced statistical and mathematical modelling of risks in large investment banks and often applies his analytical expertise to other fields such as chemistry and biochemistry. He also writes the Curious Cook column for StarLifestyle. For more information, email The information provided is for educational purposes only and should not be considered as medical advice. The Star does not give any warranty on accuracy, completeness, functionality, usefulness or other assurances as to the content appearing in this column. The Star disclaims all responsibility for any losses, damage to property or personal injury suffered directly or indirectly from reliance on such information.

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