New research emerging from the UK has signified that future disease outbreaks can potentially be predicted by analysing early warning signs.
A study led by experts at the University of Bristol has suggested that early warning signs of disease outbreaks can be detected earlier than any rapid increase in cases. The team believes that the novel findings could aid governments and policymakers in improving the accuracy of their decisions, making interventions more effective.
The research is published in Biology Letters.
Predicting disease outbreaks
The researchers employed a new method of sequential analysis combined with daily COVID-19 case data from 24 countries, discovering that early warning signs can proficiently predict surges in COVID-19 cases. The experts are confident that the method can be utilised to monitor a range of future disease outbreaks.
The team found that early warning signs were regularly detectable prior to exponential rises in cases; however, the reliability of these signals depended on the amount of time between successive waves of infection and the mathematical likelihood of a critical transition.
Furthermore, early warning signs demonstrated exceptional accuracy for waves that experienced a suppressed R number over a long period prior to the outbreak. As the devastating COVID-19 pandemic has shown, identifying rapid increases in cases before they happen is critical for allowing the public to modify their behaviour to mitigate its spread and to inform government decisions.
Duncan O’Brien from Bristol’s School of Biological Sciences said: “We’ve always been aware that any technique that’s able to predict the appearance of disease would be useful in protecting human health. This has never been more apparent with the global COVID-19 pandemic and the many discussions around when governments should put interventions in place.
“Our research found that hotly debated early warning signals were most reliable before the second COVID-19 wave that was experienced by many, and whilst these signals performed less well for the first and third waves, any rapid increase in cases could be identified well in advance.
“There is a lot of conflicting evidence surrounding early warning signs use in epidemiology and ecological monitoring in general, so we hope some of the methodological points we raise in this work helps others disentangle the complicated behaviour of these warnings.”
Limitations
Due to the requirement of specific mathematical conditions, interpreting early warning signs can be especially challenging when using real-world data. Nevertheless, recent conceptual work relaxing some of these requirements is supported in this study but has generally been discounted during the use of early warning signs in epidemiology. Therefore, the team is now looking to investigate how the methodological differences improve the generic assessment of disease dynamics.
