ArticlePloS one2014
A robust parameter estimation method for estimating disease burden of respiratory viruses.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Pooled it
- Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials.Expert review of anti-infective therapy · 2017Pooled it
- Development of influenza-associated disease burden pyramid in Shanghai, China, 2010-2017: a Bayesian modelling study.BMJ open · 2021Article
- Excess pneumonia and influenza mortality attributable to seasonal influenza in subtropical Shanghai, China.BMC infectious diseases · 2017Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPoisson model has been widely applied to estimate the disease burden of influenza, but there has been little success in providing reliable estimates for other respiratory viruses.
methodsWe compared the estimates of excess hospitalization rates derived from the Poisson models with different combinations of inference methods and virus proxies respectively, with the aim to determine the optimal modeling approach. These models were validated by comparing the estimates of excess hospitalization attributable to respiratory viruses with the observed rates of laboratory confirmed paediatric hospitalization for acute respiratory infections obtained from a population based study.
resultsThe Bayesian inference method generally outperformed the classical likelihood estimation, particularly for RSV and parainfluenza, in terms of providing estimates closer to the observed hospitalization rates. Compared to the other proxy variables, age-specific positive counts provided better estimates for influenza, RSV and parainfluenza, regardless of inference methods. The Bayesian inference combined with age-specific positive counts also provided valid and reliable estimates for excess hospitalization associated with multiple respiratory viruses in both the 2009 H1N1 pandemic and interpandemic period.
conclusionsPoisson models using the Bayesian inference method and virus proxies of age-specific positive counts should be considered in disease burden studies on multiple respiratory viruses.
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