Modelling mitigation strategies for pandemic (H1N1) 2009

Gojovic, Marija Zivkovic; Sander, Beate; Fisman, David N.; Krahn, Murray; Bauch, Chris T.

doi:10.1503/cmaj.091641

Cited by 96 publications

(100 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also assume that some proportion of symptomatic individuals will practice self-isolation; however it is unclear to what extent self-isolation may be adopted by community members as a variety of external factors will likely influence this decision. Furthermore, in the absence of specific data for mobility patterns and time use of individuals in remote and isolated communities, we have structured the model with assumptions consistent with previous work 15,16,17 . Our results generated in this study using an agent-based model of a small community in rural Manitoba support the hypothesis that both age structure and household size play an important role in the transmissibility of influenza throughout a remote and isolated community.…”

Section: Discussionmentioning

confidence: 99%

“…The age-specific probability of hospitalization was estimated from pandemic data collected for the Burntwood health region in the province of Manitoba, Canada, where the majority of remote and isolated communities are located (see Supplementary Information). The probability of self-isolation was assumed to be 0.8 for children below 18; 0.3 for adults between 19 and 49 years of age; and 0.8 for individuals above age of 50 16,17,22 . A key parameter in understanding the nature of an epidemic is the basic reproduction number (R 0 ), defined as the number of secondary cases caused by a single infected case in an entirely susceptible population 23 .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities

Laskowski

Mostaço-Guidolin

Greer

et al. 2011

Sci Rep

View full text Add to dashboard Cite

The role of demographic variables in disease spread in remote and isolated communities is poorly understood. We developed an agent-based model of a small indigenous community to qualitatively study the impact of pre-existing immunity in both young and elderly populations. We observed that in crowded living conditions, the age distribution of the population is a critical factor influencing epidemic spread. As the average age of the population increases, the effect of the pre-existing immunity in older individuals becomes more pronounced in decreasing disease incidence, even when pre-existing immunity levels in young individuals are low. However, in a non-crowded setting with relatively low average persons-per-household, the pre-existing immunity levels of young individuals remains a determining factor, regardless of the age distribution of the population. We suggest that for optimizing public health policies, social and demographic complexities of the remote and vulnerable communities should be carefully evaluated in modeling intervention strategies. In the event of an emerging disease, public health officials are tasked with identifying the geographic spread and time course of the outbreak, and identifying the most effective utilization of available health interventions and resources to mitigate disease outcomes. In addition to the natural history and biology of the disease, the demographic characteristics of the population at risk play an important role in determining the pattern of epidemic spread and identifying the type and intensity of public health intervention measures required for disease control 1,2,3 . Furthermore, the differential prevalence of predisposing health conditions and other types of health disparities increases uncertainty about how a novel disease would affect different populations with distinctly different mobility patterns, social interactions, and health characteristics 4 . The importance of these factors in influencing disease burden was highlighted during outbreaks of the 2009 H1N1 pandemic in several Canadian population settings, including First Nation reserves in northern Manitoba, remote and isolated communities in Nunavut, and Aboriginal communities on Vancouver Island 5,6,7 . Understanding the interplay between demographic, health, infection and control parameters requires the development of a modeling framework that can identify individuals with their assigned information, and describe disease spread in the population in silico (i.e., via computer simulations). Agent-based models, which specifically encapsulate individual-level characteristics, behaviors and population profiles, provide such a framework that is capable of reproducing observed scenarios in epidemics and exploring plausible contingency plans and control measures for curtailing an emerging disease 8,9 . Agent based modeling typically uses a bottom-up approach in which complex phenomena emerge from interactions between autonomous entities (i.e., agents) that perceive, make decisions, and act within an environment.He...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities

Laskowski

Mostaço-Guidolin

Greer

et al. 2011

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Infected individuals are assumed to enter a latent period (with average duration of three days) during which they are symptomfree, followed by an infectious period (with average duration of six days) during which they are both symptomatic and infectious [12] See Additional file 1 for one-way sensitivity analyses on both latent and infectious periods. We considered two levels of pandemic severity: relatively low case fatality rates (CFR) of 0.01% for the school age population and 0.16% for the adult population, based on estimates for the 2009 (H1N1) pandemic [31] and higher CFR's (2% for both of the age groups), as estimated for the 1918 pandemic [32].…”

Section: Modelmentioning

confidence: 99%

“…A number of studies have quantified the impact of school closures on cumulative attack rates, but have not evaluated the social and economic costs of such policies [6,[8][9][10][11][12]. Some of these studies demonstrate school closures can have a significant impact on the basic reproduction number and on the overall spread of disease [4-7, 10, 13, 14].…”

Section: Introductionmentioning

confidence: 99%

“…For other modes of intervention, a number of studies simultaneously assess epidemiological and economic impacts using stochastic agent-based models of pandemic influenza transmission and decisionanalytic approaches [4,12,[20][21][22]. While a few of these consider one or very few school closure options among a larger spectrum of intervention measures [4,12,23], none systematically explore the cost effectiveness and epidemiological impact of a large set of school closure policies nor assess the political, social or economic viability of such policies for mild pandemics such as 2009 H1N1 [24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulating school closure policies for cost effective pandemic decision making

et al. 2012

View full text Add to dashboard Cite

Background Around the globe, school closures were used sporadically to mitigate the 2009 H1N1 influenza pandemic. However, such closures can detrimentally impact economic and social life. Methods Here, we couple a decision analytic approach with a mathematical model of influenza transmission to estimate the impact of school closures in terms of epidemiological and cost effectiveness. Our method assumes that the transmissibility and the severity of the disease are uncertain, and evaluates several closure and reopening strategies that cover a range of thresholds in school-aged prevalence (SAP) and closure durations. Results Assuming a willingness to pay per quality adjusted life-year (QALY) threshold equal to the US per capita GDP ($46,000), we found that the cost effectiveness of these strategies is highly dependent on the severity and on a willingness to pay per QALY. For severe pandemics, the preferred strategy couples the earliest closure trigger (0.5% SAP) with the longest duration closure (24 weeks) considered. For milder pandemics, the preferred strategies also involve the earliest closure trigger, but are shorter duration (12 weeks for low transmission rates and variable length for high transmission rates). Conclusions These findings highlight the importance of obtaining early estimates of pandemic severity and provide guidance to public health decision-makers for effectively tailoring school closures strategies in response to a newly emergent influenza pandemic.

show abstract

Modeling information feedback during H1N1 outbreak using stochastic agent‐based models

Loganathan

Sundaramoorthy

Lakshminarayanan

2011

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

Influenza pandemics have struck thrice in the twentieth century and around 50 million global deaths have occurred due to these pandemics. Traditional methods of modeling intervention planning do not consider people's response in taking the vaccine with respect to the evolving characteristic of an epidemic. Intervention policies derived neglecting such feedback effects can be misleading in gauging the effectiveness of the recommended control strategies. To address this issue, we have developed a stochastic agent-based model by including the information transmission feedback [through word of mouth (WOM)] for vaccine intake. The information flow among the agents regarding vaccine intake was modeled using diffusion theory. The model incorporates parameters from very recent H1N1 2009-related studies. The developed model was then used to analyze how WOM influence is exerted into personal networks (at the micro level) and how it affects the macro evolution of the disease for different scenarios. Our results demonstrate that the disease progression with the inclusion of information transmission is very different from that predicted by models that consider only conventional phenomena. With further improvements, this model can be used to determine the optimal interventions that can form the basis of a public health systems' response to mitigate a future outbreak. 

show abstract

Modelling mitigation strategies for pandemic (H1N1) 2009

Cited by 96 publications

References 25 publications

The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities

The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities

Simulating school closure policies for cost effective pandemic decision making

Modeling information feedback during H1N1 outbreak using stochastic agent‐based models

Contact Info

Product

Resources

About