Data shows how SARS-CoV-2 spreads globally and locally. Credit: Orbon Alija/iStock
Even as many European and East Asian countries cautiously reopen after months of lockdown, a surge of new cases in the Middle East, Latin America, Africa, and South Asia means that the spread of the novel coronavirus is far from over. Places previously spared from the worst of the pandemic — Egypt, Peru, and Bangladesh, to name a few — are now grappling with crowded hospitals and soaring infection rates. Worldwide, the number of confirmed cases continues to increase at a staggering pace of over 100,000 per day.
The COVID-19 pandemic is a growing, shapeshifting global crisis that requires ongoing study. The World Health Organization (WHO), Johns Hopkins University, and other institutions have created online dashboards that aggregate data from multiple sources to track the virus’s spread. Epidemiologists make sense of the numbers, patterns, and trends to identify risk factors and targets for prevention. The news media then interpret those findings for easy-to-digest reports to keep the general public informed.
But sometimes, the science can get lost in translation and lead to the spread of misinformation. In other instances, terms can be confused with one another or used interchangeably despite having distinct meanings. For instance, what is the difference between the prevalence and incidence of a disease? What is the reproduction number of COVID-19, and why is it so important? How does the choice of epidemiological model affect predictions?
We will answer those questions and more as we cover some of the basic statistics and epidemiology behind the news reports we read daily about the COVID-19 pandemic.
Prevalence vs. Incidence
While the frontline work of doctors and nurses is undoubtedly essential during a pandemic, epidemiologists also play a critical role by trying to understand the big picture of a disease and how it spreads. Epidemiological research takes a population-based view in order to shape an appropriate response that would minimise hospitalizations and deaths.
Incidence and prevalence are two fundamentally different ways of measuring disease occurrence. The prevalence of a disease, usually reported as a percentage, is defined as the proportion of a population that is infected at a specific point in time. For instance, Germany had 9,096 active cases on June 1, calculated as the total number of cases minus those who have died or recovered. It represents the current number of people detected and confirmed to be infected with the virus. For Germany on June 1, the prevalence of COVID-19 was 9,096 / 83,783,942 x 100 = 0.01%. (83,783,942 is the number of people living in Germany in June 2020.)
Whereas prevalence gives a snapshot of how many people are ill and is useful to understand the burden of disease, incidence serves as a measure of how quickly people are becoming infected. The incidence of a disease is defined as the number of new cases in a given period divided by the population at risk. In Germany, the first week of June saw 2,338 new cases of COVID-19. The incidence can then be given as 2,338 / 83,783,942 x 1,000,000 = 27.9 new cases per 1 million people per week.
Diseases can have high prevalence but low incidence, and vice versa. As an example, diabetes has low incidence but high prevalence, since it is essentially a lifelong condition. The common cold, on the other hand, has high incidence but low prevalence.
Another key concept in epidemiology is the reproduction number, which is the average number of secondary infections resulting from an initially infected person. The basic reproduction number is the maximum epidemic potential of a pathogen in the absence of any interventions like hand washing, mask wearing, etc. For the novel coronavirus, some experts estimate a basic reproduction number of 5.7, which means that for each infected person, you can expect 5.7 additional infections. If this value is correct, COVID-19 may be more contagious than the common cold and SARS.
The effective reproduction number takes interventions to control transmission into account and is always lower than the basic reproduction number. For COVID-19, the effective reproduction number has dropped as a result of social distancing, stay-at-home measures, and other actions. At the end of April, for instance, London was estimated to have an effective reproduction number around 0.5 to 0.7.
If the effective reproduction number is greater than one, the epidemic will spread quickly. If it is less than one, the epidemic will spread slowly before tapering off and disappearing. The effective reproduction number cannot be calculated directly and must be estimated with an epidemiological model. The website rt.live uses a model to track the effective reproduction number on a daily basis for each U.S. state to see which lockdown strategies are working best and when to loosen restrictions.
Flatten the Curve
This brings us to our last topic of the epidemiological models used to generate the “curve” referred to in the often-repeated phrase “flatten the curve”. It depicts the projected number of COVID-19 cases over time, and the idea is to employ protective measures to slow the infection rate and reduce the amount of overall cases. Our health systems won’t be overwhelmed beyond their capacity to treat patients, and researchers have time to find vaccines and treatment options.
Throughout the pandemic, modelling has helped countries shape policy responses in an attempt to reduce COVID-19 transmission. This technique has the power to predict what may occur in different scenarios in terms of spread, mortality, and health system impact. The COVID-19 Response Team at Imperial College London created an incredibly detailed model that takes into account census data, like typical household sizes and age distributions, in addition to average class sizes of schools and the number of people in offices. It also includes key epidemiological parameters determining spread and severity, healthcare capacity, and the impact of treatment.
From there, the researchers can vary parameters, such as the implementation of non-pharmaceutical interventions, to observe their predicted effects on the number of cases and deaths. For example, the group estimated that mitigation strategies focusing on shielding the elderly and social distancing could save 20 million lives. Another analysis found that the weekly screening of healthcare workers and other at-risk groups – regardless of whether they are showing symptoms – would reduce their contribution to transmission by 25 to 33 percent.
An Ever-Clearer Picture
What we know about COVID-19 changes from day to day, as researchers in fields like virology, medicine, and epidemiology continue to uncover new information about the disease. The models are constantly re-run as the data improve, and the latest predictions may tell a different story.
And as the number of new cases slows down in places like Europe and East Asia, scientists can take what they’ve learned from those outbreaks and apply them to countries experiencing outbreaks in Latin America, Africa, South Asia, and the Middle East. Hopefully the growing knowledge base and ever-clearer picture of COVID-19 will mean better preparation, earlier interventions and more lives saved.