ArticleBMC public health2014
Design of non-pharmaceutical intervention strategies for pandemic influenza outbreaks.
Article in BMC public health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- Lessons from past pandemics: a systematic review of evidence-based, cost-effective interventions to suppress COVID-19.Systematic reviews · 2022Pooled it
- Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review.BMC public health · 2018Pooled it
- Multi-source agent-based modeling to optimize influenza mitigation strategies in Hong Kong.Nature communications · 2026Article
- Two-level non-regular fractional factorial designs for public health studies.BMC medical research methodology · 2026Article
- Resilience as a measure of preparedness for pandemic influenza outbreaks.Health systems (Basingstoke, England) · 2024Article
- A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2.Annals of epidemiology · 2022Article
- Article
- Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region.Global epidemiology · 2020Article
- The 2019 coronavirus: Learning curves, lessons, and the weakest link.International journal of clinical practice · 2020Article
- Article
- Estimating disease burden of a potential A(H7N9) pandemic influenza outbreak in the United States.BMC public health · 2017Article
- International Air Travel to Ohio, USA, and the Impact on Malaria, Influenza, and Hepatitis A.Scientifica · 2016Article
- Ethics-sensitivity of the Ghana national integrated strategic response plan for pandemic influenza.BMC medical ethics · 2015Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs seen during past pandemic influenza outbreaks, pharmaceutical interventions (PHIs) with vaccines and antivirals are the most effective methods of mitigation. However, availability of PHIs is unlikely to be adequate during the early stages of a pandemic. Hence, for early mitigation and possible containment, non-pharmaceutical interventions (NPIs) offer a viable alternative. Also, NPIs may be the only available interventions for most underdeveloped countries. In this paper we present a comprehensive methodology for design of effective NPI strategies.
methodsWe develop a statistical ANOVA-based design approach that uses a detailed agent-based simulation as an underlying model. The design approach obtains the marginal effect of the characteristic parameters of NPIs, social behavior, and their interactions on various pandemic outcome measures including total number of contacts, infections, and deaths. We use the marginal effects to establish regression equations for the outcome measures, which are optimized to obtain NPI strategies. Efficacy of the NPI strategies designed using our methodology is demonstrated using simulated pandemic influenza outbreaks with different levels of virus transmissibility.
resultsOur methodology was able to design effective NPI strategies, which were able to contain outbreaks by reducing infection attack rates (IAR) to below 10% in low and medium virus transmissibility scenarios with 33% and 50% IAR, respectively. The level of reduction in the high transmissibility scenario (with 65% IAR) was also significant. As noted in the published literature, we also found school closure to be the single most effective intervention among all NPIs.
conclusionsIf harnessed effectively, NPIs offer a significant potential for mitigation of pandemic influenza outbreaks. The methodology presented here fills a gap in the literature, which, though replete with models on NPI strategy evaluation, lacks a treatise on optimal strategy design.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.