Choosing pandemic parameters for pandemic preparedness planning: A comparison of pandemic scenarios prior to and following the influenza A(H1N1) 2009 pandemic

Weijden, Charlie P. van der; Stein, Mart L.; Jacobi, André; Kretzschmar, Mirjam; Reintjes, Ralf; Steenbergen, Jim E. van; Timen, Aura

doi:10.1016/j.healthpol.2012.05.007

Cited by 10 publications

(13 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result can be seen in Figure 12, where R L,1 = 1.38 and R L,2 = 1.4. These local reproduction numbers match the results of [4,33,35].…”

Section: Canada-mexicosupporting

confidence: 80%

“…We ignore demography and set parameters Λ j , d r j , and d v j , j ∈ {1, 2}, equal to 0; moreover, we neglect the possibility of disease induced mortality and let δ = 0. Several studies ( [1,4,6,7,12,20,33,35,36] and the references therein) have estimated the local reproduction number and values of key epidemiological parameters for recent influenza pandemics. In Table 3 we give an overview of the ranges of these parameters and choose reasonable values from the ranges for our simulations.…”

Section: Parametrization For Influenzamentioning

confidence: 99%

See 1 more Smart Citation

Epidemic Spread and Variation of Peak Times in Connected Regions Due to Travel-Related Infections---Dynamics of an Antigravity-Type Delay Differential Model

Knipl¹,

Röst²,

Wu³

2013

SIAM J. Appl. Dyn. Syst.

View full text Add to dashboard Cite

Abstract. National boundaries have never prevented infectious diseases from reaching distant territories; however, the speed at which an infectious agent can spread around the world via the global airline transportation network has significantly increased during recent decades. We introduce an SEAIRbased, antigravity model to investigate the spread of an infectious disease in two regions which are connected by transportation. As a submodel, an age-structured system is constructed to incorporate the possibility of disease transmission during travel, where age is the time elapsed since the start of the travel. The model is equivalent to a large system of differential equations with dynamically defined delayed feedback. After describing fundamental but biologically relevant properties of the system, we detail the calculation of the basic reproduction number and obtain disease transmission dynamics results in terms of R0. We parametrize our model for influenza and use real demographic and air travel data for the numerical simulations. To understand the role of the different characteristics of the regions in the propagation of the disease, three distinct origin-destination pairs are considered. The model is also fitted to the first wave of the influenza A(H1N1) 2009 pandemic in Mexico and Canada. Our results highlight the importance of including travel time and disease dynamics during travel in the model: the invasion of disease-free regions is highly expedited by elevated transmission potential during transportation.

show abstract

“…The result can be seen in Figure 12, where R L,1 = 1.38 and R L,2 = 1.4. These local reproduction numbers match the results of [4,33,35].…”

Section: Canada-mexicosupporting

confidence: 80%

Section: Parametrization For Influenzamentioning

confidence: 99%

Epidemic Spread and Variation of Peak Times in Connected Regions Due to Travel-Related Infections---Dynamics of an Antigravity-Type Delay Differential Model

Knipl¹,

Röst²,

Wu³

2013

SIAM J. Appl. Dyn. Syst.

View full text Add to dashboard Cite

show abstract

“…friends, family members and/or colleagues) whom they had met (according to the contact definition described below) in real-life in the past seven days. The time span of seven days was decided as it was a reasonable period for remembering close contacts and a seven days period includes the generation time of influenza [28], [29]. Each participant was provided with an invitation for each of their four contacts.…”

Section: Methodsmentioning

confidence: 99%

Online Respondent-Driven Sampling for Studying Contact Patterns Relevant for the Spread of Close-Contact Pathogens: A Pilot Study in Thailand

et al. 2014

Self Cite

View full text Add to dashboard Cite

BackgroundInformation on social interactions is needed to understand the spread of airborne infections through a population. Previous studies mostly collected egocentric information of independent respondents with self-reported information about contacts. Respondent-driven sampling (RDS) is a sampling technique allowing respondents to recruit contacts from their social network. We explored the feasibility of webRDS for studying contact patterns relevant for the spread of respiratory pathogens.Materials and MethodsWe developed a webRDS system for facilitating and tracking recruitment by Facebook and email. One-day diary surveys were conducted by applying webRDS among a convenience sample of Thai students. Students were asked to record numbers of contacts at different settings and self-reported influenza-like-illness symptoms, and to recruit four contacts whom they had met in the previous week. Contacts were asked to do the same to create a network tree of socially connected individuals. Correlations between linked individuals were analysed to investigate assortativity within networks.ResultsWe reached up to 6 waves of contacts of initial respondents, using only non-material incentives. Forty-four (23.0%) of the initially approached students recruited one or more contacts. In total 257 persons participated, of which 168 (65.4%) were recruited by others. Facebook was the most popular recruitment option (45.1%). Strong assortative mixing was seen by age, gender and education, indicating a tendency of respondents to connect to contacts with similar characteristics. Random mixing was seen by reported number of daily contacts.ConclusionsDespite methodological challenges (e.g. clustering among respondents and their contacts), applying RDS provides new insights in mixing patterns relevant for close-contact infections in real-world networks. Such information increases our knowledge of the transmission of respiratory infections within populations and can be used to improve existing modelling approaches. It is worthwhile to further develop and explore webRDS for the detection of clusters of respiratory symptoms in social networks.

show abstract

“…The AsiaFluCap Simulator contains three pre-defined pandemic influenza scenarios: a mild pandemic scenario (based on Pandemic (H1N1) 2009 parameter values), a severe pandemic scenario (partly based on highly pathogenic avian influenza (HPAI) H5N1 parameter values, assuming a person-to-person transmission rate similar to human viruses) and a moderate pandemic scenario which is based on an average of the mild and severe disease parameter values. The underlying disease-specific parameters describing the transmissibility and clinical severity for these scenarios were based on data reported in the literature [8,[40][41][42][43][44][45], additional tables provide the non-scenario and scenario specific parameter values used in the simulator [see Additional files 2 and 3]. The three scenarios differ in parameter values regarding the proportion of mild and severe cases (i.e.…”

Section: Three Pandemic Influenza Scenariosmentioning

confidence: 99%

Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator

et al. 2012

Self Cite

View full text Add to dashboard Cite

BackgroundHealth care planning for pandemic influenza is a challenging task which requires predictive models by which the impact of different response strategies can be evaluated. However, current preparedness plans and simulations exercises, as well as freely available simulation models previously made for policy makers, do not explicitly address the availability of health care resources or determine the impact of shortages on public health. Nevertheless, the feasibility of health systems to implement response measures or interventions described in plans and trained in exercises depends on the available resource capacity. As part of the AsiaFluCap project, we developed a comprehensive and flexible resource modelling tool to support public health officials in understanding and preparing for surges in resource demand during future pandemics.ResultsThe AsiaFluCap Simulator is a combination of a resource model containing 28 health care resources and an epidemiological model. The tool was built in MS Excel© and contains a user-friendly interface which allows users to select mild or severe pandemic scenarios, change resource parameters and run simulations for one or multiple regions. Besides epidemiological estimations, the simulator provides indications on resource gaps or surpluses, and the impact of shortages on public health for each selected region. It allows for a comparative analysis of the effects of resource availability and consequences of different strategies of resource use, which can provide guidance on resource prioritising and/or mobilisation. Simulation results are displayed in various tables and graphs, and can also be easily exported to GIS software to create maps for geographical analysis of the distribution of resources.ConclusionsThe AsiaFluCap Simulator is freely available software (http://www.cdprg.org) which can be used by policy makers, policy advisors, donors and other stakeholders involved in preparedness for providing evidence based and illustrative information on health care resource capacities during future pandemics. The tool can inform both preparedness plans and simulation exercises and can help increase the general understanding of dynamics in resource capacities during a pandemic. The combination of a mathematical model with multiple resources and the linkage to GIS for creating maps makes the tool unique compared to other available software.

show abstract

Choosing pandemic parameters for pandemic preparedness planning: A comparison of pandemic scenarios prior to and following the influenza A(H1N1) 2009 pandemic

Cited by 10 publications

References 61 publications

Epidemic Spread and Variation of Peak Times in Connected Regions Due to Travel-Related Infections---Dynamics of an Antigravity-Type Delay Differential Model

Epidemic Spread and Variation of Peak Times in Connected Regions Due to Travel-Related Infections---Dynamics of an Antigravity-Type Delay Differential Model

Online Respondent-Driven Sampling for Studying Contact Patterns Relevant for the Spread of Close-Contact Pathogens: A Pilot Study in Thailand

Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator

Contact Info

Product

Resources

About