Animation: Understanding COVID-19 transmission, informing control | University of Oxford
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Still from animation 'Understanding COVID-19 transmission, informing control'

Animation: Understanding COVID-19 transmission, informing control

By Dr Fiona Jones, Digital Editor Oxford Sparks

We’ve all heard of – and indeed been affected by – COVID-19, the disease caused by infection with the novel SARS-CoV-2 virus. We’ve also become familiar with a plethora of new terminology, with “social distancing”, “lockdown”, “flattening the curve” and “R number” regularly and effortlessly winding their way into our conversations. Something we might not be so familiar with, however, is the scientific process undertaken when we find ourselves faced with a new and unknown pathogen (whether that be a virus, bacterium or prion).

The latest Oxford Sparks animation, created with Professor of Applied Statistics Christl Donnelly, “Understanding COVID-19 transmission, informing control”, sheds some light on this process, likening the challenge to “piecing together a puzzle”.

Using knowledge acquired from previous outbreaks, statistical modellers combine data and equations to estimate the growth of the epidemic, in addition to other parameters, such as the ‘case fatality ratio’ – or proportion of cases who die of the disease. Such estimations are not straightforward, not least because of the huge diversity in pathogen characteristics – even amongst a single group, such as the coronaviruses.

For instance, the virus that caused Sudden Acute Respiratory Syndrome (SARS-CoV, identified in 2003) led to serious clinical disease in all cases of infection. In contrast, many infections with SARS-CoV-2 (the virus responsible for the current pandemic) are asymptomatic. Importantly, it appears that individuals can transmit the virus to others whilst asymptomatic (or in a pre-symptomatic stage), making it much harder to control. Furthermore, this makes it more challenging to estimate parameters such as the infection fatality ratio, as the number of asymptomatic cases – a tricky statistic to discern – must also be taken into account during analyses.

As shown in the animation, there are many important parameters that describe how infections progress within individuals and spread through a population; knowledge of each can help to inform policymakers’ decisions. Examples include the incubation period – used to determine how long periods of isolation should last – and R0, the basic reproduction number. This metric, which represents the average number of infections likely to arise owing to transmission from a single infected individual in a fully susceptible population, has been used to assess infection risk and inform policy throughout the current pandemic – as anyone who’s tuned into one of the UK government’s daily briefings will know.

Estimating these parameters is further complicated when scientists are faced with conflicting data, such as those from other countries. As discussed in the animation, “it’s crucial to understand if that’s due to other diseases, varying access to healthcare, or approaches to detection.”

Alongside hospital staff, carers, delivery drivers and many more, scientists have been on the front-line in the fight again coronavirus – not only searching for a vaccine and potential treatments, but analysing data, estimating parameters and running the models which allow us to make informed decisions on how best to control the pandemic.

The new Oxford Sparks animation, launched on 6th August 2020, was created with Prof. Christl Donnelly, Professor of Applied Statistics at the University of Oxford and Professor of Statistical Epidemiology at Imperial College London. She is Associate Director of the MRC Centre for Global Infectious Disease Analysis.