Influenza poses a continuing public health threat in epidemic and pandemic seasons. The 1951 influenza epidemic (A/H1N1) caused an unusually high death toll in England; in particular, weekly deaths in Liverpool even surpassed those of the 1918 pandemic. We further quantified the death rate of the 1951 epidemic in 3 countries. In England and Canada, we found that excess death rates from pneumonia and influenza and all causes were substantially higher for the 1951 epidemic than for the 1957 and 1968 pandemics (by > or =50%). The age-specific pattern of deaths in 1951 was consistent with that of other interpandemic seasons; no age shift to younger age groups, reminiscent of pandemics, occurred in the death rate. In contrast to England and Canada, the 1951 epidemic was not particularly severe in the United States. Why this epidemic was so severe in some areas but not others remains unknown and highlights major gaps in our understanding of interpandemic influenza.
Previous attempts to study the 1918-1919 flu in three small communities in central Manitoba have used both three-community population-based and single-community agent-based models. These studies identified critical factors influencing epidemic spread, but they also left important questions unanswered. The objective of this project was to design a more realistic agent-based model that would overcome limitations of earlier models and provide new insights into these outstanding questions.
The new model extends the previous agent-based model to three communities so that results can be compared to those from the population-based model. Sensitivity testing was conducted, and the new model was used to investigate the influence of seasonal settlement and mobility patterns, the geographic heterogeneity of the observed 1918-1919 epidemic in Manitoba, and other questions addressed previously.
Results confirm outcomes from the population-based model that suggest that (a) social organization and mobility strongly influence the timing and severity of epidemics and (b) the impact of the epidemic would have been greater if it had arrived in the summer rather than the winter. New insights from the model suggest that the observed heterogeneity among communities in epidemic impact was not unusual and would have been the expected outcome given settlement structure and levels of interaction among communities.
Application of an agent-based computer simulation has helped to better explain observed patterns of spread of the 1918-1919 flu epidemic in central Manitoba. Contrasts between agent-based and population-based models illustrate the advantages of agent-based models for the study of small populations.
When pandemic influenza arrived from the United States in 1918-1920 to strike Canada with devastating force, the health system was overwhelmed. Although emergency hospitals were established in public buildings including schools and universities, many sick remained in their homes. Because of the war, many physicians and nurses were overseas. Many of those who were in Canada became flu victims. The result was a massive call for volunteers. Although a few men responded, most volunteers were women. These women, many of whom had little or no training, risked their lives by acting as nurses in existing and emergency hospitals and by assisting sick families in their homes. Many became ill and some died. The result is an incredible portrait of volunteer response to a major medical emergency.
In the fall of 1918 when war-weary New Brunswickers were hit by the influenza pandemic, theirs was the only Canadian province with a Minister of Health, the first to be appointed anywhere in the British Empire. But it was a new position and a controversial one. This paper traces the growth of a public health movement in New Brunswick in the late 19th and early 20th centuries, the campaign for the establishment of a provincial Department of Health, and the role played by the 1918 influenza epidemic in legitimizing and consolidating the newly minted Department, masthead of the public health movement.
Unlike occurrences of other contagious diseases such as cholera and smallpox, the 1918-19 influenza pandemic did not lead to anti-immigrant backlash, the stigmatization of newcomers as disease carriers, or aggressive quarantine measures focused against immigrant groups. During influenza outbreaks in several major Canadian cities, quarantine was either rejected or was a low-priority containment measure, reluctantly and sceptically employed. Blaming immigrants during the epidemic was not considered enlightened public health practice or good disease containment strategy. Retrospective evaluation of the successes and failures of the fight against influenza concluded that coercive measures such as quarantine did more harm than good. The experience with influenza contributed to new notions of immigrant inclusion in the social body.
Agent-based modeling provides a new approach to the study of virgin soil epidemics like the 1918 flu. In this bottom-up simulation approach, a landscape can be created and populated with a heterogeneous group of agents who move and interact in ways that more closely resemble human behavior than is usually seen in other modeling techniques. In this project, an agent-based model was constructed to simulate the spread of the 1918 influenza pandemic through the Norway House community in Manitoba, Canada. Archival, ethnographic, epidemiological, and biological information were used to aid in designing the structure of the model and to estimate values for model parameters. During the epidemic, Norway House was a Hudson's Bay Company post and a Swampy Cree-Métis settlement with an economy based on hunting, fishing, and the fur trade. The community followed a traditional, seasonal travel pattern of summer aggregation and winter dispersal. The model was used to examine how seasonal community structures and associated population movement patterns may have influenced disease transmission and epidemic spread. Simulations of the model clearly demonstrate that human behavior can significantly influence epidemic outcomes.