Pandemic waves

Q: How do scientists find out when which wave of corona will come?

Krishna: Scientists take several parameters and prepare models (5). 

Annual epidemics, like of influenza , may occur because of climate or patterns of social mixing – often driven by the school year or people staying inside more during the winter. It’s possible that SARS-CoV-2, the coronavirus that causes COVID-19, spreads more efficiently under certain weather conditions. But recent outbreaks suggest that warm or humid weather is not sufficient to stop the spread of the disease. Some scientists model that SARS-CoV-2 will eventually become seasonal like other coronaviruses.

Waves and seasonal dynamics are also affected by levels of immunity in the human population. As more individuals become immune to a pathogen, its spread slows and eventually stops as the virus runs out of new people to infect.

Some of the current talk of coronavirus waves likely stems from comparisons with past epidemics that did show these peaks and troughs of infections. “Spanish Flu” directs much of the modelling responses to pandemics. The term “wave” comes from the 1889-92 outbreak that had different phases supposed to have occurred over multiple years (2). Most of our thinking on second-wave theory arises from the 1918-20 “Spanish Flu” that infected 500 million people worldwide and reportedly killed an estimated 20 million to 50 million. The true number of deaths from “Spanish flu” is highly uncertain; it is not clear, for instance, if they were actually caused by influenza, which was not a reportable disease at the time and what role bacterial superinfection played – estimates are, therefore, educated guesses.

But situations and circumstances change. For instance, people travel more now than in earlier times. Now we have a global village. 

We also have more vaccines and drugs to control epidemics efficiently. 

Take for instance, the current COVID-19 pandemic is often compared to the 1918 H1N1 influenza pandemic, which had three distinct waves over the course of a year. The proportion of influenza patients who were severely ill or died was much higher in the last two waves compared to the first. It’s unclear whether being infected earlier on protected individuals during later waves.

It has been suggested that previous pandemics are characterized by waves of activity spread over months. However, there is inconsistent evidence of such patterns across all of these  influenza epidemics and pandemics. As they go on to quickly exhibit seasonality in the ensuing years and along with other acute respiratory viruses tend to favour seasonal cold-weather patterns of circulation (2).

But we have a different scenario now. 

This covid-19 isn't the 1918 flu, and it's not behaving like it either. When we talk about waves of pandemics of influenza, it meant something fairly specific (1).

First and second waves of covid-19 lead to more deaths now than third and fourth, unlike the flu pandemic.

More recently, the 2009 H1N1 influenza pandemic, though mild, had two distinct waves; this virus still commonly shows up in seasonal influenza outbreaks. A study of H1N1 influenza in 2009-2010 found that the second wave affected more older people, with underlying conditions.

Insight from the past suggests that discrete waves result as a disease spreads into and out of a population. Different waves can have different features, too, regarding factors like disease severity or which populations are most affected.

Different geographic scenarios too differ. Like what you get in the West is not true to what you get in the East. Africa has a different scenario to Europe.

Because of all these reasons, we often wonder  whether there are clear cut COVID – ‘waves’ or sporadic outbreaks?

We do not know for certain whether COVID will recur in phases, or sporadic outbreaks.

Making absolute statements of certainty about ‘ waves’ is unwise, given the current substantial uncertainties and novelty of the evidence. Models need not always predict the right outcomes.

Scientists have been working to better understand the factors governing the wave and plateau dynamics of the spread of COVID-19, to be able to better forecast future outbreaks in this pandemic and future epidemics. The earliest epidemiological models developed a century ago represented the spread of disease in very simple terms, not accounting for any variability in a population, whether physiological or social. Over the last two decades, epidemiology has incorporated these kinds of variables, while still assuming that each variable remained constant in time(3).

Recently, however,  a team of scientists has developed an epidemiological model that encompasses the randomness and dynamic variability of individual social interactions, as well as individual differences in the size of social networks. The team reports that this newly accounted-for random dynamic factor will always produce waves or plateaus of infections—like those seen throughout the pandemic—whether or not the model also accounts for individuals’ changing their social behaviour based on knowledge of current infection rates. The new model, which builds on the team’s earlier findings published in April of last year in the Proceedings of the National ... is validated against empirical data taken from four U.S. regions prior to the introduction of COVID-19 vaccines. The model further tells us that COVID-19 may be here to stay—it shows a clear path for it to becoming endemic in the global population, much like the common cold or the flu.

These results are published online in the December 14, 2021 issue of the journal eLife. 

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