## Counts

A count is the number of cases, events, or items that occur in a time period and/or a geographic area.

In its dashboard, the WHO reports the daily counts of new cases, confirmed cases and deaths by country and WHO region. The daily distribution shows the shape of the curve of new infections and of deaths. These graphs of counts show how the epidemic is changing with time and its response to prevention strategies.

Other COVID-19 counts include: the number of hospitalized patients, the number of confirmed cases who have recovered, the number of laboratory tests and so on.

Counts may be also be accumulated across time periods or geographic areas, for example, in its dashboard, WHO provides the accumulated totals of confirmed case and deaths since the start of the epidemic.

### Questions

- How was the event defined? For COVID-19, who classified and confirmed the case/death as COVID-19 and what guidelines did they adhere to?
- How was the event counted? For COVID-19, were the cases simply those that reported to hospital or were did the health authorities seek out and test people in the community?
- Have the definitions changed over time or how do they differ across geographies?

## Rates

Counting cases and deaths as they occur is an important way of monitoring an epidemic or pandemic but as numbers get larger, it is also useful to relate them to population size ie the number of cases during a time period divided by the population size. This is the incidence rate or per capita rate. For COVID-19, statisticians usually express this figure per million population.

Health officials commonly use the case fatality rate when reporting COVID-19. This is the number of confirmed deaths divided by the number of confirmed cases in a period of time and for a defined area.

The figures below compare: 1) the cumulative total numbers of COVID-19 deaths for the United States, the United Kingdom and Spain with 2) the cumulative total numbers of COVID-19 deaths per capita population for the United States, the United Kingdom and Spain. The three countries may have counted deaths differently but the two figures show the difference between cumulative graphs of numbers of deaths and deaths per capita

### Additional resources

This article in *OurWorldInData.org *explains COVID-19 indicators

The Johns Hopkins website explains how to interpret cumulative graphs.

This video explains measures of mortality

## The reproductive number

The *reproductive number* (or ratio) is an indicator epidemiologists have developed to describe the expected transmission dynamics of a disease. It is an estimate of the number of people that one infected person will infect, on average. If the reproductive number is greater than one, the outbreak of the infection will escalate and if it is less than one, the outbreak will die out. The larger the value of the reproductive number, the more serious the outbreak will become as the number of cases will multiply exponentially.

- The
*basic reproductive number*R0 describes the situation at the start of an epidemic in which the entire population is susceptible to catching the infection. On 6th March 2020, for example, the World Health Organization estimated R0 for COVID-19 to be between 2 and 2.5. But others have set the value higher. - The
*effective reproductive number*(R) varies with time and will change as officials implement interventions and some of the population have had the virus or obtained vaccination, for example. The goal of intervention is to lower the value of R below one.

Epidemiologists do not calculate the reproductive number from raw data but estimate it through modelling. The estimated value of the reproductive number will depend on how the model is formulated. Models will usually take into account the known biological characteristics of the pathogen, the likely behaviour of the population and other environmental factors. The model will also measure or predict the impact of any intervention strategies such as social distancing, wearing masks or sheltering at home.

The basic reproductive number (R0) remains unaffected by public health strategies. The effective reproductive number (R) may decrease but it will only retain its value as long as officials keep strategies in place or when a proportion of the population becomes immune to the virus, for example through vaccination.

### Additional resources

This report by the Imperial College modelling team describes predictions of R with time after intervention in eleven European countries.

Heffernan et al. explains the derivation of the basic reproductive ratio and its use in assessing specific emerging diseases.

The statistical estimation page of this website describes modelling

## Disaggregating and comparing indicators

Some segments of the population appear to be more susceptible to the virus. Some become more seriously ill once infected. Segments of the population, for example health care workers, are more exposed to the virus. At the start of the epidemic, we learned that case fatality rates rose with age and that people over 70 years of age with underlying conditions were more vulnerable. As data have become available, it is possible to explore how different groups of people have been affected. That is by disaggregating the indicators by population groups.

For example, the United Kingdom Office of National Statistics analysed COVID-19 deaths in England and Wales by ethnic group. A simple breakdown by ethnic group showed marked differences in the risk of dying between the ethnic groups. Once they adjusted for region, rural and urban classification, area deprivation, household composition, socio-economic position, highest qualification held, household tenure, and health or disability they obtained the results in the figure below. These results showed that adjusting for these factors substantially reduced the odds of a death involving COVID-19 relative to those of white ethnicity for all ethnic groups. However, black males and females, for example, were still 1.9 times more likely to die from COVID-19 than the white ethnic group.

### Notes

- It is informative to disaggregate cases and deaths by age, sex and other factors. For example: ethnicity, occupation, socio-economic group and geographic area. It is important when looking at one factor to adjust for the others. If socio-economic status differs between ethnic groups, the comparison may simply reflect that difference.
- The indicator,
*relative risk*, measure the relative risk of dying between two groups eg that black males and females were 1.9 times as likely to die from COVID-19 than the white group. - The horizontal bars in the figure are measures of uncertainty derived from the analytical model used (logistic regression). When this horizontal line excludes the value representing the white group, this indicates strong evidence of a difference.

**Risk of COVID-19 death by ethnic group and sex, England and Wales, 2 March to 10 April 2020, fully adjusted model**