Covid-19 FAQs


The coronavirus pandemic has led to many different statistics being presented to us from the media, official sources and perhaps more unofficial sources. But what do they all mean, how should we interpret them, and in what ways do they affect us personally?

Members of our Covid-19 Task Force have been working hard to answer the common questions around the numbers that are commonly used to discuss the pandemic. Click on each question individually to be taken to the relevant answers.

What is R, and how is it determined?
How can I find my personalised risk?
What can we tell from international comparisons?


 

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What is R, and how is it determined?

The reproduction number is the average number of direct infections from one case. This is over the whole time while people are infectious.

If the reproduction number is 2, we expect 100 infected people to infect 200 more people. If the reproduction number is 0.5, the average group of 100 infected people infects 50 more.

The reproduction number can change over time. If people reduce contacts, the virus has fewer transmissions.

The basic reproduction number (R₀) is for when the population has no immunity. This is not a biological constant. The same virus may spread in different populations at different paces. By itself, this number does not determine how fast a virus spreads. Initial ‘seed’ cases and infectious periods are important.

Suppose people can recover, conferring immunity. Over time, more infected people will recover, die, or get vaccinated. The effective reproduction number is for the remaining susceptible population.

Reproduction numbers are averages.

One person could pass on the virus to 100 people, and 99 others do not pass it on. In that population, the average new infections is 1. If every infected person infects one more, that would be the same reproduction number. The implications for health policy differ.

How is the reproduction number estimated?

Researchers estimate this number, through mathematical models.<br /> Model inputs could include:

  • Data on confirmed infections, hospital admissions, critical care, and/or deaths.
  • Social contact surveys, with self-reported data on contacts.
  • Household infection surveys, which can estimate current prevalence of infections.

The MRC Biostatistics Unit (Cambridge) uses transmission models. Researchers stratify these models by age and region. Their work takes death figures, mortality risks, and time from infection to death. Researchers estimate new infections over time and reproduction numbers. Different groups of researchers use a variety of models to estimate R, each with their own estimated uncertainty, and then a pooled judgement is reached by SPI-M-O (Scientific Pandemic Influenza Group on Modelling, Operational sub-group), which feeds its conclusions into Sage.

Reproduction number estimates are uncertain, and can be hard to interpret.
There are several sources of uncertainty:

  • Accuracy of input data.
  • Model choice, as different approaches give different estimates.
  • How sensitive underlying assumptions in each model are.

The reproduction number refers to the average infected person. That average person changes. At lower infection levels, reproduction number estimates are more volatile.

A national reproduction number may be less useful than those of groups and areas. On the other hand, estimates relating to small groups will be more uncertain.

We should look at the reproduction number alongside other key statistics. There is a plausible range around reproduction number estimates, and these are generally provided. For example, the Gov.uk website provides a good discussion of the current estimates of R in different regions of the country, with uncertainty intervals. 

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How can I find my personalised risk?

In this pandemic, there are several risks to consider, including:

  • The risk of getting a SARS-CoV-2 infection;
  • The risk of hospitalisation or dying after catching COVID-19.
  • The risk of infecting others;

Analysis by the Office for National Statistics suggests age and sex are important influences on the risk of  both being infected and dying from  COVID-19: older people are far more likely to die with the disease. In each age group, estimated mortality rates are higher for men than women. That analysis covers England and Wales, using death certificates registered up to 4th July 2020.

The QCovid risk calculator features a more personalised model, although it is says it is not intended for clinical use.  After entering your age, sex, ethnicity, BMI, postcode and any clinical conditions, QCovid gives an abolute risk of being infected and then being hospitalised or dying from COVID-19 in the first wave, as well as a relative risk compared with similar person with no clinical conditions.  It is based on the analysis of over 10 million GP records, but could underestimate the risk for individuals who were shielding in the first wave.

The University of Exeter produced a tool for calculating an individual risk score for catching and then  hospitalisation with COVID-19 and dying due to the disease.  The British Medical Association adopted this tool for healthcare workers. This simplified score uses age, sex, ethnicity, and health conditions to produce a score that measures a worker’s risk relative to a healthy younger white female.  

Another calculator designed for occupational health is the ‘Covid Age’ site hosted by the Association for Local Authority Medical Advisors, which is based on analysis of 17 million patients in the Open Safely study.  This estimates the age of a healthy white man with a similar risk as the person providing the information, and uses that ‘Covid age’ to estimate vulnerability - that is the chance of dying from COVID-19 if infected.  This is one of the only calculators that provides this vital information.

Calculators could miss major factors to your personal risk. The calculations often rely on statistical associations. There may be confounding factors. Such tools provide simplified estimates based on historical outcomes for a group of people who match you in the details entered: your personal risk could be somewhat higher or lower.

The results do not override government guidance or advice from doctors. Scientific understanding of personal risks develops with further research.

As this is a pandemic: this is not only about personal risk, but the risk of spreading infections  to others, some of whom are particularly vulnerable.

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What can we tell from international comparisons?

International comparisons are challenging.

First, there is no standard definition of a ‘COVID-19 death’. Before mid-August, there were different definitions in the United Kingdom used for the daily counts:

  • England (Public Health England): confirmed deaths in all settings after aperson has a positive test result for SARS-CoV-2.
  • Wales (Public Health Wales): suspected deaths from COVID-19 in hospitals and care homes. The deceased person must have tested positive for the virus.
  • Scotland and Northern Ireland (Public Health Scotland and Public Health Agency in Northern Ireland): confirmed deaths in all places, if the person has died within 28 days of their first positive test result. 

After a review by Public Health England, the changed measure uses the 28-day cut-off for the UK for the daily reports on the Coronavirus dashboard, although the more reliable, those less timely, weekly death registrations from the Office for National Statistics are also now provided.

Other countries have different criteria.  RIVM in the Netherlands counts “overleden COVID-19 patiënten” (deceased COVID-19 patients). This measure counts deaths in hospital with a positive test result. Sciensano in Belgium includes deaths where doctors suspect the deceased has COVID-19.

Countries can change definitions too. Interpretations are also difficult. Countries have different testing regimes. Processed tests affect the number of confirmed deaths. Tests can differ: false negative results reduce lab-confirmed deaths.

Travel influences seeds and outbreaks. How many people live close together affects how this virus spreads. Demography, cultures, and health policies differ.

Despite these challenges, countries can learn from each other during this pandemic. 

We should avoid precise league tables, and think more in broad tiers of countries.

Our World in Data highlights three ‘success stories’: Vietnam, Germany, and South Korea. The broad conclusion is effective responses need strong action in four areas. These actions cover: prevention, detection, containment, and treatment.

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