ArticleScientific reports2015
Estimating the Attack Ratio of Dengue Epidemics under Time-varying Force of Infection using Aggregated Notification Data.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 2 citations in OpenAlex.
- Dengue epidemiology and transmission intensity across Panama during 2000-2024: a modelling study.Lancet regional health. Americas · 2026Article
- The epidemicity index of recurrent SARS-CoV-2 infections.Nature communications · 2021Article
- Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986-1990.Journal of the Royal Society, Interface · 2020Article
- Spatial Dynamics of Chikungunya Virus, Venezuela, 2014.Emerging infectious diseases · 2019Article
- Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models.PLoS computational biology · 2018Article
- Estimating under-observation and the full size of the 2016 Zika epidemic in Rio de Janeiro.PloS one · 2018Article
- The introduction of dengue follows transportation infrastructure changes in the state of Acre, Brazil: A network-based analysis.PLoS neglected tropical diseases · 2017Article
- Reconstruction of Rift Valley fever transmission dynamics in Madagascar: estimation of force of infection from seroprevalence surveys using Bayesian modelling.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantifying the attack ratio of disease is key to epidemiological inference and public health planning. For multi-serotype pathogens, however, different levels of serotype-specific immunity make it difficult to assess the population at risk. In this paper we propose a Bayesian method for estimation of the attack ratio of an epidemic and the initial fraction of susceptibles using aggregated incidence data. We derive the probability distribution of the effective reproductive number, Rt, and use MCMC to obtain posterior distributions of the parameters of a single-strain SIR transmission model with time-varying force of infection. Our method is showcased in a data set consisting of 18 years of dengue incidence in the city of Rio de Janeiro, Brazil. We demonstrate that it is possible to learn about the initial fraction of susceptibles and the attack ratio even in the absence of serotype specific data. On the other hand, the information provided by this approach is limited, stressing the need for detailed serological surveys to characterise the distribution of serotype-specific immunity in the population.
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