The term wave is often used to describe a peak or a downfall in infections or cases resembling the shape of a wave. But the word ‘wave’ has triggered panic lately. So we asked an expert to explain what a wave is. We also verified with him whether or not it can be predicted and is it too early to make any speculations about its severity.

There is no universal definition of a ‘wave’ in a pandemic. In fact, when coronavirus started in early 2020, experts were figuring out if there will be waves or patterns since not all infectious diseases follow the same sketch and drawing on historical analogies may not be accurate. In the current scenario, it is being used to define the rising and declining trend.

Here’s the edited excerpt from an email interview with Professor Gautam Menon, Professor of Physics and Biology, Ashoka University on predicting waves of diseases, speculating about vulnerable age groups, the role of immunity and vaccination status, and more:

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What makes a wave of a disease and what defines a wave?

The idea of a wave here is somewhat different from how this term was used earlier. In the waves of measles that used to be seen earlier, cases would drop to very low levels before rising again in some rough periodic manner, typically tied to seasons or school openings. Nowadays, the term is used to denote the broad peaks and troughs in the numbers of daily infections, even though the numbers at the trough might not be too small. These ups and downs can be seen at both national and regional levels.

How would one identify a wave and how are speculations and predictions made about a wave?

There’s no strict definition, really, unless one can make a case that a fresh and sustained rise in cases after a period in which they were decreasing can be tied to something new, such as a more infectious variant of the virus.

How likely is a third wave, can the severity of the third wave be predicted?

There is no inevitability to a third wave and historical comparisons and analogies may not be accurate here. It depends on how long immunity derived from a previous infection might be expected to last as well as the effects of vaccinations in warding off severe disease. With some idea of how many people have been infected so far and more information about the protection that is derived from a previous bout of illness or vaccination, we can attempt to model this possibility.

However, any rise in cases can always be preempted through mask-wearing and distancing, so social norms around COVID-19 appropriate behaviour may be the dominant feature that control epidemic spread. As for severity, much will depend on whether a new mutant that is better at moving between people emerges. Usually, viruses are expected to evolve towards greater transmissibility and lesser virulence, so perhaps a less severe third wave may be in store for us, if at all.

Is it true that children might be vulnerable during the third wave? If yes, what are the factors that might be responsible?

With current vaccination drives, older people, by that time, will have hopefully been vaccinated and thus at less risk from severe disease. There is nothing that we know of now that suggests that the virus might preferentially affect children or that outcomes in them might be worse. From all we know, the risk of severe disease and mortality is overwhelmingly in the older age brackets. Children typically have very mild symptoms.

Do historical outbreaks of infectious diseases offer some models for COVID-19 and how it may unfold over time?

There is a rough 32-week periodicity for waves in the influenza pandemic of 1918-1919 and other influenza pandemics, but as I said, drawing on historical analogies might not be accurate here. We now understand much more about how diseases spread and are able to protect ourselves better. With better surveillance, we should be able to catch early warning signals of an anomalous increase in cases in time to do something about it.

Do different waves have varied features and do mathematical modelers of immunity impact the dynamics?

The features a wave might exhibit depend on many aspects: prior immunity, vaccination status, variants and so on. We can use models to figure out how the combination of these aspects might influence disease spread. However, the central difficulty of modeling will remain the fact that social behaviour largely controls how diseases spread between people. This is the hardest aspect to model.