2021
DOI: 10.1371/journal.pcbi.1009230
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Accurate influenza forecasts using type-specific incidence data for small geographic units

Abstract: Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting m… Show more

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Cited by 6 publications
(4 citation statements)
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“…In previous studies on influenza and influenza-like-illness [ 28 30 ] we used a smoothly varying two-value functional form to describe the time-dependent reproduction number: R ( t ) = β ( t ) γ , where β ( t ) is the time-dependent transmission rate and γ is the total recovery rate. Here we extended this model to an arbitrary number of values: …”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…In previous studies on influenza and influenza-like-illness [ 28 30 ] we used a smoothly varying two-value functional form to describe the time-dependent reproduction number: R ( t ) = β ( t ) γ , where β ( t ) is the time-dependent transmission rate and γ is the total recovery rate. Here we extended this model to an arbitrary number of values: …”
Section: Methodsmentioning
confidence: 99%
“…Our model can also be made more flexible by including differences in quality of healthcare over time and location [48,49], and allowing for coupling between geographic regions [29]. Our previous work on forecasting ILI in the U.S. [28][29][30] highlights the important role that spatial coupling can play in respiratory disease transmission, and which we anticipate will become increasingly more important as travel restrictions have been relaxed and movement between states, countries, and continents significantly increases.…”
Section: Plos Computational Biologymentioning
confidence: 99%
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“…To cite just a few examples, methods used include multiscale probabilistic Bayesian random walk models (Osthus & Moran, 2021), Gaussian processes (Johnson et al, 2018), kernel conditional density estimation (Ray et al, 2017;Brooks et al, 2018), and generalized additive models (Lauer et al, 2018). Other models have an implicit or explicit representation of a disease transmission process, such as variations on the susceptible-infectious-recovered (SIR) model (Shaman & Karspeck, 2012;Lega & Brown, 2016;Osthus et al, 2017;Pei et al, 2018;Turtle et al, 2021).…”
Section: Related Literaturementioning
confidence: 99%