A study led by researchers at the University of Oxford and Cardiff University has been used to define waves of Covid-19 that occurred during the pandemic.

Despite the extensive scrutiny of Covid-19 data and frequent discussion of waves amongst the general public, no set criteria to define a wave was ever used. In this study, researchers have developed an algorithm which, when applied to daily cases and fatalities of Covid-19, allows for the identification of substantial, significant, and sustained periods of increase, aptly described as “observed waves”. The ability to define a wave will assist and inform public health experts and policy makers in the management of future epidemics.

Dr Adam Mahdi, founder of the University of Oxford’s OxCOVID19 project and the senior author of the study said: “Our algorithmic approach offers a means to identify and understand observed waves of disease. By revealing the types, drivers, and modulators of Covid-19 waves, our research contributes to the broader analysis of the epidemic’s progression.”

Co-author Prof. Ricardo Aguas, Nuffield Department of Medicine, University of Oxford, notes: “Applying an algorithm to a spectrum of time series data can help address relevant epidemiological questions: How can we best define an epidemiological wave? Intuitively, anyone would guess that a series of rising and falling cases would make a wave. However, by how much does it need to rise and drop for it to be epidemiologically meaningful?”

Periods of higher infection were identified as meaningful “waves” of the disease if they were sufficiently long and severe, rather than more temporary fluctuations. Dips between waves were ignored unless the case rate fell below a certain threshold of the peak value.  The algorithm will help policymakers and public health experts to better comprehend and respond to the dynamics of future epidemics and pandemics. With the ability to uniformly describe and analyse observed waves, decision-makers can refine their strategies and interventions, aiming for a more effective and targeted approach to disease management.

Co-author Dr John Harvey, a UKRI Future Leaders Fellow at Cardiff University, observes that proposing uniform definitions for concepts that cannot be easily defined is a commonly used scientific technique, drawing a similarity with body weight measurement: “Nobody would claim that a person magically becomes underweight the moment their BMI goes below 18.5 – there is nothing special about this particular number and every individual is different. But drawing a line somewhere – anywhere – makes it possible to study the effects of being underweight. We can do the same exercise with a wave. Once something has a definition, you can start to investigate and understand it.”

The researchers employed their algorithm to synthesise Covid-19 data across different countries, adopting a wave-centric view. The analysis of individual countries revealed significant variations in consecutive observed waves, particularly in terms of the case fatality ratio.

Moreover, a more detailed examination revealed diverse geographical ranges for consecutive observed waves, especially in larger countries. The study also confirmed the impact of government interventions on modulating these waves, with early implementation of Non-Pharmaceutical Interventions (NPIs) found to correlate with a reduced number of observed waves and a decreased mortality burden during those waves.

Notes for editors

This research was funded by UKRI via “A multi-modal Covid 19 database for research”.  The full study is published in Heliyon. (https://tinyurl.com/bddwkvjt) Harvey J, Chan B, Srivastava T, Zarebski AE, Dłotko P, Błaszczyk P, Parkinson RH, White LJ, Aguas R, Mahdi A.
Epidemiological waves-types, drivers and modulators in the COVID-19 pandemic.

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