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  • RESEARCH HIGHLIGHT

Extreme epidemics are more likely than expected

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Spanish flu. American Red Cross nurses tend to flu patients in temporary wards set up inside Oakland Municipal Auditorium, 1918. Credit: GL Archive/Alamy Stock Photo.

We may tend to see the COVID-19 pandemic as an extremely unlikely event that could not have been predicted. But according to a study led by Marco Marani, at the Università degli Studi di Padova, each of us had an almost 40% probability of witnessing such an epidemic during their lifetime1.

Working with colleagues in the US, Marani, a hydrologist and civil engineer, gathered historical information on hundreds of past epidemics, and analysed it with a statistical method recently devised for hydrological events. The method is used when designing dams, bridges and costal defences, to quantify the probability of heavy rainfalls, floods or waves. When COVID-19 hit Northern Italy last year, Marani and his collaborators wondered if they could employ the same approach to study the probability of similar epidemics. For decades, epidemiologists have been warning that large epidemics would become more and more frequent, because of the growing size of the human population, urbanization, the disruption of habitats and the increased global mobility.

The first step was to build a global historical dataset of almost 500 infectious disease outbreaks since 1600, consulting multiple sources. Epidemics were classified according to their intensity, i.e. the number of victims divided by the duration of the epidemic and the size of the world population. Intensity, the authors say, is a better indicator than the absolute number of deaths of an epidemics’ impact. The intensities in the database span four orders of magnitude, ranging from the 1804-1828 Yellow Fever epidemic in Gibraltar, that killed 2 people per million per year, up to the 1918-1920 Spanish Flu pandemic, with 17 thousand deaths per million people per year. The authors wanted to capture the evolution of diseases based only on the properties of the pathogen and the transmission dynamics, so they excluded all epidemics after 1945 from the analysis, as these were more influenced by vaccines, drugs and other public health interventions.

The scientists found that the number of epidemics per year was extremely variable. This proves that there is no stationary process governing the emergence of infectious diseases, says Marani. In contrast, the probability that an epidemic reaches a given intensity once it starts has remained quite constant in time. And the bad news, says Marani, is that “extremely intense epidemics are not as unlikely as one would have expected”.

By combining the yearly rate of outbreaks with the probability distribution of intensities, the authors could estimate that the probability of having an epidemic with intensity equal to or larger than the one of the Spanish flu within one year was 1% in 1918 (when that epidemic actually happened) and peaked at nearly 2% in 1959. Currently it is nearly 0.25%, which corresponds to a return period (the average time between two consecutive occurrences of the extreme event) of about 400 years.

As for COVID-19, the current return period of an epidemic with equal to higher intensity value is 209 years. This corresponds to slightly less than 0.5% chances per year, or to a 38% probability of experiencing one such epidemics during a person’s lifetime.

“The authors did a wonderful job in collecting historical data on epidemics, building a time series which was not available before”, says Marino Gatto, an expert in quantitative ecology at the Politecnico di Milano. “Their unique experience with statistical analysis of extreme events allowed the extraction of relevant information with broad implications for epidemiology”.

doi: https://doi.org/10.1038/d43978-021-00105-7

References

  1. 1.

    Marani, M. et al. Proceedings of the National Academy of Sciences August 118: 35 (2021).

    PubMed  Article  Google Scholar 

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