31st March 2020
for the Conversation
The University of Liverpool
Infectious disease epidemiologist & Pharmacotoxicologist, Postdoc in One Health at Institute of Infectious Diseases and Global Health, University of Liverpool
Because it takes time to get data about what is happening in reality, infectious disease models can be a useful guide to help authorities prepare and respond. They provide insights into what might happen.
Essentially, models can help to interrupt the transmission of viruses by predicting where transmission is likely to happen and public communication on the risks. Then controls can be put into place.
It’s important that these are quickly established. COVID-19 is estimated to have a crude mortality ratio (the number of reported deaths divided by the reported cases) of between 3% and 4%. This has been shown to be different in some countries. Discrepancies may arise because cases haven’t been reported or due to a lack of testing.
For Kenya, this implies that – in the worst case scenario – 40 people may die for every 1,000 confirmed cases if no intervention is put in place.
To my knowledge, it’s not clear which models are being used by the Kenyan government, their data sharing protocols lack transparency and therefore there is also uncertainty over the status of the disease within the country.
There are various infectious disease models, one of which is the SEIR model. The model – first described by mathematicians in 1969 – looks at four factors, people that are: Susceptible, Exposed, Infectious, Recovered. It is an extension of the classical SIR (Susceptible, Infected, Recovered) model, originally developed in 1927. The SEIR model adds a fourth compartment: exposed persons which are infected but are not yet infectious.