Transmissibility of the 2009 H1N1 pandemic in remote and isolated Canadian communities: a modelling study

Abstract

ObjectivesDuring the first wave of the 2009 influenza pH1N1, disease burden was distributed in a geographically heterogeneous fashion. It was particularly high in some remote and isolated Canadian communities when compared with urban centres. We sought to estimate the transmissibility (the basic reproduction number) of pH1N1 strain in some remote and isolated Canadian communities.DesignA discrete time susceptible-exposed-infected transmission model was fit to infection curves simulated from laboratory-confirmed case counts for pH1N1 on each day. The sampling from Poisson distribution was used to estimate the basic reproduction number, R0, of pH1N1 during the spring wave for five different communities in Manitoba and Nunavut, Canada, where remote and isolated communities experienced a high incidence of infection, and high rates of hospitalisation and intensive care unit admission.SettingRemote and isolated communities in Northern Manitoba, Nunavut, and the largest urban centre (Winnipeg) in the province of Manitoba, Canada.ResultsUsing published values of the exposed and infectious periods specific to H1N1 infection, corresponding to the average generation time of 2.78 days, we estimated a mean value of 2.26 for R0 (95% CI 1.57 to 3.75) in a community located in northern Manitoba. Estimates of R0 for other communities in Nunavut varied considerably with higher mean values of 3.91 (95% CI 3.08 to 4.87); 2.03 (95% CI 1.50 to 3.19); and 2.45 (95% CI 1.68 to 3.44). We estimated a lower mean value of 1.57 (95% CI 1.35 to 1.87) for R0 in the Winnipeg health region, as the largest urban centre in Manitoba.ConclusionsInfluenza pH1N1 appears to have been far more transmissible in rural and isolated Canadian communities than other large urban areas. The differential severity of the pandemic in these regions may be explained partly by differential transmissibility, and suggests the need for more nuanced, targeted or population-specific control strategies in Canada.

Nuanced risk assessment for emerging infectious diseases

“…This diff erence might have resulted from diff erential crowding and a younger age distribution (those born before 1957 seem to have been protected against infection) in isolated First Nations reservations. 11 If the mean value of R 0 is fi xed, heterogeneity caused by diff erences between individuals in one setting (eg, superspreaders 5 ) or by diff erences between settings (hospital vs community) both increase variation in cluster size and reduce the probability that any particular individual infection will cause an epidemic. 4 Pandemic risk estimates based on early, scarce information should be interpreted with caution, because the identifi cation of highly infectious individuals and severe cases is more likely, and because of the greater availability of surveillance resources in high-income populations where transmission characteristics of patho gens might be atypical.…”

Estimated impact of aggressive empirical antiviral treatment in containing an outbreak of pandemic influenza H1N1 in an isolated First Nations community

“…Nonetheless, our use of two distinct modeling approaches provides a degree of cross‐validation of our main findings, which were similar in both cases, and regardless of whether influenza‐like illness counts, or respiratory illness visits more generally, were used for model fitting. While the Richards growth model, for a given generation time, produced a far higher estimate of the reproductive number for influenza in this community than was produced by fitting a compartmental model, both approaches produced estimates for reproductive numbers far higher than those reported elsewhere in Canada and echo prior reports suggesting higher reproductive numbers for influenza in Canadian First Nations . To approximate the reproductive number estimates derived in southern Canada, we would have to postulate serial intervals (time between successive generations of cases) that are shorter than those attributed to influenza, and certainly shorter than those estimated in southern Ontario during the 2009 influenza A H1N1 pandemic .…”

Infectious Disease Modeling Methods as Tools for Informing Response to Novel Influenza Viruses of Unknown Pandemic Potential

“…Contact tracing data, surveillance datasets, R 0 population distribution (ie, the probability associated with an individual in the population generating R 0 secondary cases at the start of the epidemic). Case renewal process Initial cases are modeled as a renewal process, which is a generalization of the Poisson process in which the time between cases is random and arbitrary, but independent and identically distributed [8,17].…”

“…The basic reproduction number R 0 is estimated by fitting infection curves to incidence data from early in outbreak [8] using the Richards growth model, which is a generalized logistic function of time.…”

“…These methods were enhanced by newer computational techniques such as bioinformatics and digital surveillance methods [5]. Simultaneously, substantial contributions to the literature were made in the area of infectious disease modeling (IDM) [6][7][8][9][10]. This article is a guide to the way in which (IDM) can contribute to policy discussions and decision-making in preparation for, or during, an influenza pandemic.During an outbreak of influenza with pandemic potential, public health leaders ask a range of questions to inform situational awareness, help assess severity [11] and guide decisions that aim to control the spread and impact of disease.…”