Transmissibility and geographic spread of the 1889 influenza pandemic
Until now, mortality and spreading mechanisms of influenza pandemics have been studied only for the 1918, 1957, and 1968 pandemics; none have concerned the 19th century. Herein, we examined the 1889 "Russian" pandemic. Clinical attack rates were retrieved for 408 geographic entities in 14...
Gespeichert in:
Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS 2010-05, Vol.107 (19), p.8778-8781 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Until now, mortality and spreading mechanisms of influenza pandemics have been studied only for the 1918, 1957, and 1968 pandemics; none have concerned the 19th century. Herein, we examined the 1889 "Russian" pandemic. Clinical attack rates were retrieved for 408 geographic entities in 14 European countries and in the United States. Case fatality ratios were estimated from datasets in the French, British and German armies, and morbidity and mortality records of Swiss cities. Weekly all-cause mortality was analyzed in 96 European and American cities. The pandemic spread rapidly, taking only 4 months to circumnavigate the planet, peaking in the United States 70 days after the original peak in St. Petersburg. The median and interquartile range of clinical attack rates was 60% (45-70%). The case fatality ratios ranged from 0.1% to 0.28%, which is comparable to those of 1957 and 1968, and 10-fold lower than in 1918. The median basic reproduction number (R₀ ) was 2.1, which is comparable to the values found for the other pandemics, despite the different viruses and contact networks. R₀ values varied widely from one city to another, and only a small minority of those values was within the range in which modelers' mitigation scenarios predicted effectiveness. The 1889 and 1918 R₀ correlated for the subset of cities for which both values were available. Social and geographic factors probably shape the local R₀ , and they could be identified to design optimal mitigation scenarios tailored to each city. |
---|---|
ISSN: | 0027-8424 1091-6490 |
DOI: | 10.1073/pnas.1000886107 |