The Influenza Epidemic of 1918–1919 in San Francisco

Hrenoff, Arseny K.

doi:10.1093/milmed/89.5.805

Cited by 2 publications

(8 citation statements)

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“…In 1918, the city of San Francisco had an approximate population of 550 000 (Crosby 2003), which is about 74% of today's population. As judged from an analysis of the records of the San Francisco hospital (Hrenoff 1941), the 1918 pandemic affected all ages, sexes and races. Clinical symptoms included severe headache, prostration, muscle and joint pain, rapidly rising fever and chills, and general malaise.…”

Section: Historical Backgroundmentioning

confidence: 99%

“…Clinical symptoms included severe headache, prostration, muscle and joint pain, rapidly rising fever and chills, and general malaise. Other less characteristic manifestations of influenza included epistaxis, sore throat, cough, rhinitis, laryngitis, gastro-enteric upsets and leucopenia (Hrenoff 1941). When followed by pneumonia, influenza was potentially more lethal (Vaughn 1921).…”

Section: Historical Backgroundmentioning

confidence: 99%

“…The effectiveness of these campaigns was publicly debated at the time as, for example, 78% of the nurses at the San Francisco Hospital contracted influenza, although this facility was considered to have one of the best isolation services in this city. Consequently, public announcements were run in local newspapers calling for volunteers to help in overburdened hospitals (Hrenoff 1941), which may have increased transmission opportunities. Neither an influenza vaccine nor antiviral medications were available at the time.…”

Section: Historical Backgroundmentioning

confidence: 99%

“…Daily epidemic data for the autumn influenza wave (September-November) in the city of San Francisco, California were obtained from public records as reported to the city health department (Department of Hygiene 1922; figure 1). Since the health department was aware of the rapidly spreading pandemic influenza in the United States before the autumn wave started in San Francisco, epidemic data were critically inspected and are believed to have been recorded rather precisely (Hrenoff 1941). Nevertheless, levels of underreporting (or overreporting once the epidemic was well publicized) are unknown quantitatively.…”

Section: Data Sourcesmentioning

confidence: 99%

See 3 more Smart Citations

Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

Chowell

Nishiura

Bettencourt

2006

J. R. Soc. Interface.

314

293

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The reproduction number, R, defined as the average number of secondary cases generated by a primary case, is a crucial quantity for identifying the intensity of interventions required to control an epidemic. Current estimates of the reproduction number for seasonal influenza show wide variation and, in particular, uncertainty bounds for R for the pandemic strain from 1918 to 1919 have been obtained only in a few recent studies and are yet to be fully clarified. Here, we estimate R using daily case notifications during the autumn wave of the influenza pandemic (Spanish flu) in the city of San Francisco, California, from 1918 to 1919. In order to elucidate the effects from adopting different estimation approaches, four different methods are used: estimation of R using the early exponential-growth rate (Method 1), a simple susceptible-exposed-infectious-recovered (SEIR) model (Method 2), a more complex SEIR-type model that accounts for asymptomatic and hospitalized cases (Method 3), and a stochastic susceptible-infectious-removed (SIR) with Bayesian estimation (Method 4) that determines the effective reproduction number Rt at a given time t. The first three methods fit the initial exponential-growth phase of the epidemic, which was explicitly determined by the goodness-of-fit test. Moreover, Method 3 was also fitted to the whole epidemic curve. Whereas the values of R obtained using the first three methods based on the initial growth phase were estimated to be 2.98 (95% confidence interval (CI): 2.73, 3.25), 2.38 (2.16, 2.60) and 2.20 (1.55, 2.84), the third method with the entire epidemic curve yielded a value of 3.53 (3.45, 3.62). This larger value could be an overestimate since the goodness-of-fit to the initial exponential phase worsened when we fitted the model to the entire epidemic curve, and because the model is established as an autonomous system without time-varying assumptions. These estimates were shown to be robust to parameter uncertainties, but the theoretical exponential-growth approximation (Method 1) shows wide uncertainty. Method 4 provided a maximum-likelihood effective reproduction number 2.10 (1.21, 2.95) using the first 17 epidemic days, which is consistent with estimates obtained from the other methods and an estimate of 2.36 (2.07, 2.65) for the entire autumn wave. We conclude that the reproduction number for pandemic influenza (Spanish flu) at the city level can be robustly assessed to lie in the range of 2.0-3.0, in broad agreement with previous estimates using distinct data.

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Section: Historical Backgroundmentioning

confidence: 99%

Section: Historical Backgroundmentioning

confidence: 99%

Section: Historical Backgroundmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

See 2 more Smart Citations

Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

Chowell

Nishiura

Bettencourt

2006

J. R. Soc. Interface.

314

293

View full text Add to dashboard Cite

show abstract

“…Since there is no explicit evidence estimating and proving the effectiveness of public health interventions, and because a high burden was placed upon the sanitary and medical sectors, diagnosed/hospitalized individuals (J ) are assumed equally infectious. Although it is difficult to explicitly evaluate the difference in infectiousness between general community and hospital, we roughly made this assumption since 78 percent of the nurses of the San Francisco Hospital contracted influenza [75]. A more rigorous assumption requires either statistical analysis of more detailed time-series data [76] or an epidemiological comparison of specific groups by contact frequency [77].…”

Section: Estimating R 0 Using a Structured Epidemic Modelmentioning

confidence: 99%

Quantifying the transmission potential of pandemic influenza

Chowell

Nishiura

2008

Physics of Life Reviews

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This article reviews quantitative methods to estimate the basic reproduction number of pandemic influenza, a key threshold quantity to help determine the intensity of interventions required to control the disease. Although it is difficult to assess the transmission potential of a probable future pandemic, historical epidemiologic data is readily available from previous pandemics, and as a reference quantity for future pandemic planning, mathematical and statistical analyses of historical data are crucial. In particular, because many historical records tend to document only the temporal distribution of cases or deaths (i.e. epidemic curve), our review focuses on methods to maximize the utility of time-evolution data and to clarify the detailed mechanisms of the spread of influenza.First, we highlight structured epidemic models and their parameter estimation method which can quantify the detailed disease dynamics including those we cannot observe directly. Duration-structured epidemic systems are subsequently presented, offering firm understanding of the definition of the basic and effective reproduction numbers. When the initial growth phase of an epidemic is investigated, the distribution of the generation time is key statistical information to appropriately estimate the transmission potential using the intrinsic growth rate. Applications of stochastic processes are also highlighted to estimate the transmission potential using similar data. Critically important characteristics of influenza data are subsequently summarized, followed by our conclusions to suggest potential future methodological improvements.2

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The Influenza Epidemic of 1918–1919 in San Francisco

Cited by 2 publications

References 0 publications

Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

Quantifying the transmission potential of pandemic influenza

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