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Retrospective #modelling of the #disease and #mortality burden of the 1918–1920 #influenza #pandemic in #Zurich, #Switzerland

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By G.M.

Abstract

Background

Our study aims to enhance future pandemic preparedness by integrating lessons from historical pandemics, focusing on the multidimensional analysis of past outbreaks. It addresses the gap in existing modelling studies by combining various pandemic parameters in a comprehensive setting. Using Zurich as a case study, we seek a deeper understanding of pandemic dynamics to inform future scenarios.

Data and methods

We use newly digitized weekly aggregated epidemic/pandemic time series (incidence, hospitalisations, mortality and sickness absences from work) to retrospectively model the 1918–1920 pandemic in Zurich and investigate how different parameters correspond, how transmissibility changed during the different waves, and how public health interventions were associated with changes in these pandemic parameters.

Results

In general, the various time series show a good temporal correspondence, but differences in their expression can also be observed. The first wave in the summer of 1918 did lead to illness, absence from work and hospitalisations, but to a lesser extent to increased mortality. In contrast, the second, longest and strongest wave in the autumn/winter of 1918 also led to greatly increased (excess) mortality in addition to the burden of illness. The later wave in the first months of 1920 was again associated with an increase in all pandemic parameters. Furthermore, we can see that public health measures such as bans on gatherings and school closures were associated with a decrease in the course of the pandemic, while the lifting or non-compliance with these measures was associated with an increase of reported cases.

Discussion

Our study emphasizes the need to analyse a pandemic's disease burden comprehensively, beyond mortality. It highlights the importance of considering incidence, hospitalizations, and work absences as distinct but related aspects of disease impact. This approach reveals the nuanced dynamics of a pandemic, especially crucial during multi-wave outbreaks.

Source: Epidemics, https://www.sciencedirect.com/science/article/pii/S1755436525000015?via%3Dihub

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