An evidence-based review of the coronavirus responsible for COVID-19.
A pandemic is a sudden outbreak of infectious disease that spreads rapidly, affecting vast numbers of people in different countries.
This does not consider disease severity.
SARS-CoV-2 is the name of the virus which causes the disease COVID-19.
About the author
I am a Physics graduate from University College London (UCL) and have several years’ worth of experience as a Senior Research Analyst at a cybersecurity company, where I trained in, applied, and taught skills relating to open-source intelligence gathering and critical analysis.
I believe that through evidence-based thinking methods, combined with open-minded discourse and free speech, we can discern the truth among the murky swathes of disinformation and spin-doctoring on the internet.
About this review
The COVID-19 pandemic has impacted the world at a horrific scale, and people are trying to form their own opinions — rightly so — on topics ranging from disease severity to government policy. However, the general public are not exposed to a consistent flow of reliable information, so many are suffering from fear, confusion, and isolation, exacerbated by extreme differences in opinion on how seriously any aspect of the pandemic should be taken. These are the problems that this report aims to address.
Given my lack of specialist experience regarding COVID-19, this report is not a personal analysis of the pandemic. Rather, it is a collection of information gathered from systematic reviews, peer-reviewed studies, expert analyses, and more, in an attempt to provide a reliable and transparent overview on the pandemic. Attempts were made to minimise the amount of original content, in order to maintain authenticity of information.
It is worth reiterating that the intended audience of this report is the general public, i.e. people with no expertise in any virus-related fields. As such, any original content was carefully considered in order to translate technical information into layman’s terms without sacrificing accuracy. Nonetheless, all references are provided, where more detailed information can be found for those who want to explore deeper.
Please do not consider the information in this report as fact, but hopefully it will stimulate evidence-based conversations on the pandemic and, subsequently, more intelligent critiques of private and public policy.
- PCR (polymerase chain reaction) tests represent the primary method being used to detect the presence of SARS-CoV-2 in patients around the world.
- It is probable that 36% to 82% of positive COVID-19 diagnoses made exclusively using test results are false positives.
- Countries determine cause of death differently. Even within the UK, Public Health England (PHE) and the Office for National Statistics (ONS) have different methods for estimating deaths from COVID-19.
- While the infection fatality rate (IFR) of COVID-19 is estimated to be between 0.1% and 0.35%, this does not accurately reflect the mortality risk, which is heavily dependent on age and comorbidities. In many countries, the average age of death from COVID-19 is over 80.
- In the UK, COVID-19 deaths in care homes account for roughly 40–50% of total COVID-19 deaths. However, in some regions, care home deaths may account for up to 80% of total COVID-19 deaths.
Symptoms and transmission
- Cases of COVID-19 have reported a wide range of symptoms, if any at all. However, cough and sore throat symptoms in combination with high temperature, muscle or joint pain, fatigue, and headache, substantially increase the likelihood of COVID-19 as the cause.
- Current estimates of the proportion of asymptomatic COVID-19 infections are around 15–30%.
- It is thought that the primary transmission mode for COVID-19 is person-to-person contact through respiratory droplets generated by coughing, sneezing, singing, speaking, or breathing.
- Transmission by asymptomatic cases may be significantly lower than presymptomatic and symptomatic cases, but most people who become infected do not remain asymptomatic throughout the infection.
- Evidence based on mathematical models suggests that quarantine was important in reducing the number of people infected and the number of deaths.
- However, evidence is limited regarding the need for strict national or regional lockdown measures in order to garner similar results as advisory quarantine measures. Furthermore, the collateral damage caused by strict responses to COVID-19 may outweigh any benefits gained.
- There is strong evidence to show that hygiene measures, such as hand-washing, are effective at reducing the spread of respiratory viruses.
- There is insufficient evidence to support social distancing (spatial separation of at least one metre) as a method to reduce the spread of disease during epidemics.
- There seems to be little to no evidence supporting restrictions on the sizes of social gatherings, as well as curfew laws for bars and restaurants, as measures for reducing the spread of a virus.
What they are and how they work
PCR (polymerase chain reaction) tests, or assays, represent the primary method being used to detect the presence of SARS-CoV-2 in patients around the world. In this context, they work as follows:
A swab, or sample, is taken from the back of a patient’s throat. This sample is processed such that genetic material specifically associated with the SARS-CoV-2 virus is amplified. This amplification process is cyclical, whereby the genetic material is doubled with every cycle. The number of cycles required to amplify the viral material beyond a specified threshold is known as the cycle threshold (Ct) value. If the Ct value is reached within a certain number of cycles, the sample is considered a positive result. In other words, samples containing more viral material will result in a lower Ct value.
The PCR test was originally designed to amplify genetic material, and while it can be useful in diagnostics, it has limitations which have been highlighted during the current COVID-19 pandemic:
- PCR tests offer the capacity to detect viral material in very small quantities. However, the test cannot explicitly determine whether this viral material is infectious or not.
- PCR tests are not 100% accurate, since results can include false positives (the false detection of viral material) and false negatives (the false omission of viral material).
COVID-19 test data
The accuracy of the COVID-19 test data is reliant on how these limitations have been taken into consideration:
1. How do the test results and Ct values correlate with infectiousness?
2. How do PCR test inaccuracies impact the results?
Reliability of PCR tests in the current pandemic
Various studies have shown that infectivity of SARS-CoV-2-positive individuals was rarely observed for Ct values higher than 24–30 and after eight days of symptom onset. As such, many experts have recommended that the maximum number of allowed cycles should be 30–35, in order to minimise false positives. However, most PCR tests are being conducted with a limit of 37–40, i.e. the amount of SARS-CoV-2 within samples is being amplified to a Ct value that is way beyond the level associated with infectiousness. Furthermore, various studies have suggested that “re-positive” cases are unlikely to be infectious.
Regarding the impact of false positives and false negatives, there are many ways to assess these. One such way would be via estimates from the UK Office for National Statistics (ONS), which was done in two different nuances by Professor Carl Heneghan (Director of the Centre for Evidence Based-Medicine at Oxford University) and Dr Mike Yeadon (former Chief Scientific Officer and VP, Allergy and Respiratory Research Head with Pfizer). The analysis produced by the latter found that 89% of positive PCR test results may be false positives. The same analysis can be done using the most recent ONS estimates…
According to a government report in June, “An attempt has been made to estimate the likely false-positive rate of national COVID-19 testing programmes by examining data from published external quality assessments (EQAs)… carried out between 2004–2019. Results of 43 EQAs were examined, giving a median false positive rate of 2.3% (interquartile range 0.8–4.0%).”
For argument’s sake, the lower quartile false positive rate of 0.8% will be used in the following analysis (the same that was used by Dr Yeadon). As of 7 October, the latest ONS estimates suggest that 116,600 people within the community population in England had COVID-19 during the week from 18 to 24 September. If the entire UK population of 66,650,000 were to be tested during that week, we should expect to get 533,200 false positives alongside the 116,600 actual positives. So out of 649,800 positive results, 82% of them would be false positives.
Essentially, if there is a high proportion of actual-negatives being tested, the false negative rate becomes statistically insignificant and the false positive rate becomes significant (as is the case with COVID-19), and vice versa. In reality, PCR tests are being conducted more selectively…
In total in the UK, as of 7 October, there have been 23,818,928 PCR tests conducted and 530,113 positive results, leaving 23,288,815 negative results. This suggests that, of all tests conducted, we should expect that 190,551 falsely detect the presence of viral material — this accounts for 36% of all positive results.
However, as mentioned earlier, the presence of viral material does not necessarily mean the presence of infectious disease. As such, the actual false positive rate of diagnoses, as opposed to viral detection, is likely to be somewhere between 36% and 82%.
A possible problem
The first PCR test designed to detect SARS-CoV-2 was published in a paper in January by Dr Christian Drosten et al. As explained in the paper, it was developed without access to the SARS-CoV-2 virus. According to the associated letter to the editor, the proposed genome sequence did not match any of the 1,623 SARS-CoV-2 complete genome sequences publicly available in the National Centre for Biotechnology Information (NCBI) database as of 7 May 2020.
What this means
Many positive results are likely to be false, and many others may represent cases where non-infectious viral material is present. Furthermore, not everyone with COVID-19 is being tested. As such, there may be significant inaccuracies regarding the use of PCR test results to gauge the spread and severity of COVID-19 on a macroscopic scale. Any data relating to the number of COVID-19 cases is susceptible to these inaccuracies, throughout this report and elsewhere.
The importance of COVID-19 mortality figures
In general terms, risk is calculated via the consideration of two key components:
- The probability of an event occurring.
- The impact of such an event.
Accordingly, the risk of COVID-19 can be assessed via the probability of infection (i.e. the spread of the virus) and the impact of infection (i.e. long-term effects or death). As such, mortality figures play an important role in monitoring national risk from COVID-19.
Defining a COVID-19 death 
Public Health England (PHE) has been publishing figures on COVID-19 deaths based on whether deaths occurred within 28 days of a positive test, or whether COVID-19 was listed on the death certificate (which is also based on there having been a positive test).
However, as mentioned in the previous section of this report, these statistics lack clinical context and do not include COVID-19 deaths of patients who were not tested. As such, figures from the ONS may provide a more accurate reflection of SARS-CoV-2 deaths due to their consideration of whether the virus was listed as the underlying cause of death on the death certificate:
“Between 1 January and 31 August 2020, 52,327 deaths in England and Wales involved COVID-19. Out of these, 48,168 deaths were due to COVID-19: that is, COVID-19 was the underlying cause.”
This means that roughly 7.9% of deaths with COVID-19 on the death certificate did not have the disease as the underlying cause of death. However, since the beginning of July, this proportion has risen to 29%. This change may be due to a number of possible reasons, such as a change in diagnostics and reporting procedures, or an improvement in the treatment of COVID-19.
Death rate can be considered in two primary ways:
- Case fatality rate (CFR) — refers to the ratio of the number of deaths divided by the number of confirmed cases.
- Infection fatality rate (IFR) — refers to the ratio of deaths divided by the number of actual infections (i.e. confirmed and unconfirmed cases).
Many cases of COVID-19 are going undetected, as symptoms are either mild or non-existent. These people are unlikely to be tested, so will not be considered when calculating the case fatality rate. As such, IFR estimates provide lower and more accurate interpretations of the severity of the virus, although are more difficult to produce.
Case fatality rate (CFR)
Other research on the CFR for COVID-19 has found similarly declining trends across the world. As testing increases, more data is captured regarding less severe illnesses, so CFRs towards the end of the pandemic are thought to provide a more accurate representation of the severity of the virus.
While the CFR in Figure 1 appears to have levelled-off at around 1–2%, an analysis by Oxford’s Centre for Evidence-Based Medicine (CEBM) estimates the IFR for COVID-19:
“Taking account of historical experience, trends in the data, increased number of infections in the population at large, and potential impact of misclassification of deaths give a presumed estimate for the COVID-19 IFR somewhere between 0.1% and 0.35%.”
Furthermore, a recent study sought to estimate IFRs among non-institutionalised populations by age, race, ethnicity, and sex. The study found an overall IFR of 0.26%, although people younger than 40 years old had an IFR of 0.01%.
Death rate by age 
Recent data from the ONS, analysed by the CEBM, has shown that the average age of COVID-19 deaths in England and Wales since the pandemic began is 82.4 years old, further evidencing the COVID-19 risks with age shown in Figure 2. In addition, and perhaps surprisingly, this is higher than the average age of non-COVID-19 deaths during the same period, which is 81.5. The average age of death from people who contracted COVID-19 was also around 80 in Italy, Spain, Ireland, and the US.
Care homes 
In the UK, from 2nd March to 12th June, there were 19,394 deaths involving COVID-19 in care homes, of which 18,509 deaths (95.4%) were due to COVID-19 as the leading cause. This represents 39.6% of all deaths due to COVID-19 during a similar timeframe (from 1st March to 30th June). The UK government’s strategy regarding elderly patients at the start of the pandemic may have exacerbated the risk faced by those in care homes…
Between 19th March and 15th April, over 28,000 elderly patients had been moved from hospitals to care homes in England. This policy was intended to free up beds in advance of a surge in COVID-19 patients into hospitals. The proportion of COVID-19-positive cases among these patients is unknown.
However, in many regions around the world, the proportion of COVID-19 deaths in care homes may have exceeded 70%.
Death rate by comorbidity
Various studies and meta-analyses have been conducted in order to understand if and how different comorbidities are associated with an increased risk from COVID-19.
While results seem to fluctuate due to various factors, including different categorisations of comorbidities, the comorbidities that seem to be most commonly and strongly linked to an increased risk from COVID-19 include cardiovascular diseases and hypertension, respiratory diseases, diabetes, and some other obesity-related issues.
Death rate by race
A growing body of evidence indicates that BAME (black, Asian, and minority ethnic) groups have a higher mortality risk from COVID-19. It is probable that clinical and socioeconomic variables account for substantial amounts of this increased risk, however, adjusted differences in death rates between whites and non-whites are still significant.
Mortality figures not only act as an indicator of current risk, but can also indicate future risk, particularly when assessed in relation to associated factors. However, identifying correlations without evidenced causality to support them can be dangerous, as false conclusions can be drawn.
For example, based on data since the start of the pandemic from “Our World in Data” and PHE, positive PCR test results have a moderate-strong correlation with deaths, whereas number of COVID-19 hospital admissions and COVID-19 patients in mechanical ventilation beds have a much stronger correlation with deaths. Furthermore, SARS-CoV-2-positive hospital admissions also seem to enable more accurate predictions regarding COVID-19 deaths compared to positive test results. Similar results were found by the CEBM, who suggested that “PCR positives lack predictive power in terms of telling whether people will die in the future.”
In addition, trend extrapolation can be used as an indicator of future risk. For example, experts often look for signs of exponential growth regarding the spread of a virus, in order to gauge the impact of future infections and suggest appropriate measures.
Comparisons with other countries
Direct comparisons with other European countries should be taken with a pinch of salt given the many differences in COVID-19-relevant factors such as population density, demographics, lockdown measures, recording methods, and more.
Given the above, perhaps countries such as Germany, Italy, and France can be considered among the more appropriate for comparisons with the UK:
- All three countries are experiencing case numbers comparable to those at the peak of the pandemic between March and May, as is the case with the UK.
- All three countries are not seeing significant or exponential growth regarding deaths from COVID-19, as is the case with the UK.
- As previously discussed, hospital admission rates may provide a more accurate indicator of deaths to come. As seen in Figure 4, neither the UK, France, nor Italy are currently experiencing exponential growth regarding COVID-19 hospital occupancy rate.
Excess mortality is measured during a crisis by comparing deaths from all causes with the average number of deaths over the same period in previous years. It can help to provide an understanding of the total impact of the COVID-19 pandemic on deaths. While random deviations in year-on-year mortality numbers occur, these can be accounted for by considering the standard deviation.
While high excess mortality rates have been seen across the world, older age groups have proportionately suffered the most excess deaths by a significant amount, across many countries.
Excess mortality rates allow for better comparison between countries, as they not only avoid the problem of countries recording COVID-19 deaths in different ways, but also consider the excess deaths indirectly caused by the pandemic. Furthermore, comparing age-standardised excess mortality rates across selected cities can reduce analytical challenges that arise from differences in population densities and age demographics. This provides a better picture to gauge the impact of the pandemic in relation to control measures implemented by different governments. This comparison can be seen in Figure 5.
Comparisons with other infectious diseases
Comparing COVID-19 with other infectious diseases is not simple, due to the differences in recording and diagnostic procedures, demographics affected, healthcare response, and more.
A 2014 systematic review of the H1N1 virus, which spread in 2009, highlighted the difficulty in estimating the seriousness of infection using the case fatality risk, and found that substantial variability in case definitions and age-specific estimates complicated the interpretation of the overall case fatality risk.
While the IFR for seasonal flu is estimated to be below 0.1%, versus IFR estimates for COVID-19 at 0.1–0.35%, this comparison should be taken with a pinch of salt. Furthermore, the average transmissibility of COVID-19 is thought to be significantly higher than even the more severe flu outbreaks.
Symptoms and Transmission
Cases of COVID-19 have reported a wide range of symptoms; a number of studies have shown that the most common symptoms are cough, fever, fatigue, shortness of breath, and muscle or joint pain. Existing data also suggests a prevalence of smell and/or taste loss in 31–85% of COVID-19 patients. On average, symptom onset occurs 4–6 days after infection (i.e. incubation period).
Furthermore, a Cochrane systematic review in July found that, while cough and sore throat are common in people both with and without COVID-19, “high temperature, muscle or joint pain, fatigue, and headache substantially increase the likelihood of COVID-19 when they are present.”
Current estimates of the proportion of asymptomatic COVID-19 infections are around 15–30%, although they range from 5% to 80%. Asymptomatic infection rates are difficult to capture not only due to the lack of widespread testing, but also because, without continued recording of symptoms, differentiations cannot be made between asymptomatic and presymptomatic infections.
It is thought that the primary transmission mode for infectious SARS-CoV-2 is person-to-person contact through respiratory droplets generated by coughing, sneezing, singing, speaking, or breathing. Evidence suggests that fomites (objects contaminated with infectious material) may not play a significant role in the transmission of COVID-19, especially when good hygiene standards are maintained.
Accordingly, several outbreak investigation reports have shown that COVID-19 transmission can be particularly effective in crowded, confined indoor spaces with poor ventilation. Weather conditions may also influence the spread of SARS-CoV-2, whereby higher temperatures, humidity, and wind speed have been associated with lower transmission, although quality of evidence remains inconclusive.
Evidence suggests that viral load increases before symptom onset and peaks around symptom onset, and viral load in severe cases of COVID-19 is significantly higher than in mild cases. However, high viral load does not necessarily indicate high infectivity, which declines after around day eight even among cases with ongoing high viral loads.
Furthermore, transmission by asymptomatic cases may be significantly lower than presymptomatic and symptomatic cases, but most people who become infected do not remain asymptomatic throughout the infection. As such, asymptomatic cases should still adhere to certain preventative policies.
While the above evidence includes several systematic reviews, some individual studies have shown contrasting results regarding viral load, although transmissibility is either measured using small sample sizes or not at all.
Current evidence, while inconclusive, suggests that approximately 20% to 50% of people (dependent on location) have increased immune response to SARS-CoV-2, despite having no known exposure to the virus. The likely source of this phenomenon is the immunological cross-reactivity between common cold coronaviruses and SARS-CoV-2. However, observations may indicate that the presence of this immunological cross-reactivity is more likely to lower the severity of the disease, rather than the spread of the virus.
Defining a pandemic
Upon declaration of a pandemic, an understanding of what a pandemic actually is can help to avoid instinctive feelings of fear and confusion.
Definitions of a pandemic available on Cambridge Dictionary and Oxford Reference are similar, and state that a pandemic is a sudden outbreak of infectious disease that spreads rapidly, affecting vast numbers of people in different countries. These definitions, along with the WHO’s pandemic phase descriptions, include nothing about disease severity.
Quarantine refers to the restriction of asymptomatic healthy people who have had contact with confirmed or suspected cases.
According to a Cochrane systematic review in September, “despite limitations in the evidence, [due to the reliance on mathematical modelling], all the studies found quarantine to be important in reducing the number of people infected and the number of deaths. Results suggest that quarantine was most effective, and cost less, when it started earlier. Combining quarantine with other prevention and control measures may have a greater effect than quarantine alone.”
However, evidence is limited regarding the need for strict national or regional lockdown measures in order to garner similar results as advisory quarantine measures. In terms of intrusiveness of measures versus reduction in virus spread, a more specific case-isolation strategy may be most efficient, followed by quarantine; relatively, the effects of strict restriction of movement may be minimal in reducing the number of deaths.
Suggestions have been made that strict national lockdown measures and mass closures of certain industries had little to no positive effect on the number of COVID-19 deaths in several countries. Furthermore, the collateral damage caused by many national responses to COVID-19 may outweigh any benefits gained from lockdown. The negative psychological impacts of quarantining are well-evidenced.
Face masks and hygiene
In short, there is limited evidence on the effectiveness of facemasks in reducing the spread of a respiratory virus. However, given the aforementioned primary modes of transmission for SARS-CoV-2, the reviews which conclude that masks reduce the spread of the virus seem reasonable. However, the extent to which masks are effective remains unclear, particularly since different viruses have different levels of infectiousness.
There is strong evidence to show that hygiene measures, such as hand-washing, are effective at reducing the spread of respiratory viruses.
Social distancing and the two-metre rule
“A one-size-fits-all two-metre social distancing rule is not consistent with the underlying science of exhalations and indoor air.
“Smaller airborne droplets laden with SARS-CoV-2 may spread up to 8 metres concentrated in exhaled air from infected individuals, even without background ventilation or airflow. Whilst there is limited direct evidence that live SARS-CoV-2 is significantly spread via this route, there is no direct evidence that it is not spread this way.
“The risk of SARS-CoV-2 transmission falls as physical distance between people increases, so relaxing the distancing rules, particularly for indoor settings, might therefore risk an increase in infection rates. In some settings, even two metres may be too close.
“Safe transmission mitigation measures depend on multiple factors related to both the individual and the environment, including: viral load, duration of exposure, number of individuals, indoor versus outdoor settings, level of ventilation, whether face coverings are worn, and where someone is placed in relation to the infected person.”
Given the conditional nature of the success of a two-metre rule (or similar), it may not be surprising that, according to a Cochrane systematic review in July 2011, “there is insufficient evidence to support… social distancing (spatial separation of at least one metre between those infected and those non-infected) as a method to reduce spread during epidemics.”
UK: the rule of six and the 10pm curfew
In mid-September, the UK government implemented a new law prohibiting social gatherings of more than six people. However, the evidence to support the effectiveness of such measures in controlling a pandemic seems limited, with Professor Heneghan and his senior associate writing that “[the ‘rule of six’] is a disturbing decision that has no scientific evidence to back it up, and may well end up having major social consequences.”
In addition, the UK, along with other countries such as France and Germany, introduced a late-night curfew on bars and restaurants, forcing them to close at 10pm. The evidence to support such restrictions also appears to be limited. In Berlin, a court ruling recently overturned their curfew law, reportedly stating that “it was not apparent” the law would help to stop the spread of COVID-19, and that closing bars would be a “disproportionate encroachment on the freedom [of the industry].”
 An assay is an investigative procedure in laboratory medicine, pharmacology, environmental biology and molecular biology for qualitatively assessing or quantitatively measuring the presence, amount, or functional activity of a target entity.
 It is reasonable to expect the prevalence of the virus to be close to the number found by ONS, because they sample randomly, and would pick up symptomatic and asymptomatic people in proportion to their presence in the community.
 Pearson correlation coefficients (where 1 and -1 represent perfectly positive and negative correlations respectively) were calculated for: deaths and positive test results (0.60), deaths and COVID-19 hospital admissions (0.76), and deaths and COVID-19 patients in mechanical ventilation beds (0.92).