Bayesian compartmental model for an infectious disease with dynamic states of infection

Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a pa...

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Veröffentlicht in:Journal of applied statistics. - 1991. - 46(2019), 6 vom: 08., Seite 1043-1065
1. Verfasser: Ozanne, Marie V (VerfasserIn)
Weitere Verfasser: Brown, Grant D, Oleson, Jacob J, Lima, Iraci D, Queiroz, Jose W, Jeronimo, Selma M B, Petersen, Christine A, Wilson, Mary E
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:Journal of applied statistics
Schlagworte:Journal Article Bayes factor SIR empirically-adjusted reproductive number multinomial seasonality visceral leishmaniasis
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520 |a Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a particular population. Logistic and multinomial regression techniques are often applied to infectious disease modeling of large populations and are suited to identifying variables associated with a particular disease or disease state. However, they are less appropriate for estimating infection state prevalence over time because they do not naturally accommodate known disease dynamics like duration of time an individual is infectious, heterogeneity in the risk of acquiring infection, and patterns of seasonality. We propose a Bayesian compartmental model to estimate latent infection state prevalence over time that easily incorporates known disease dynamics. We demonstrate how and why a stochastic compartmental model is a better approach for determining infection state proportions than multinomial regression is by using a novel method for estimating Bayes factors for models with high-dimensional parameter spaces. We provide an example using visceral leishmaniasis in Brazil and present an empirically-adjusted reproductive number for the infection 
650 4 |a Journal Article 
650 4 |a Bayes factor 
650 4 |a SIR 
650 4 |a empirically-adjusted reproductive number 
650 4 |a multinomial 
650 4 |a seasonality 
650 4 |a visceral leishmaniasis 
700 1 |a Brown, Grant D  |e verfasserin  |4 aut 
700 1 |a Oleson, Jacob J  |e verfasserin  |4 aut 
700 1 |a Lima, Iraci D  |e verfasserin  |4 aut 
700 1 |a Queiroz, Jose W  |e verfasserin  |4 aut 
700 1 |a Jeronimo, Selma M B  |e verfasserin  |4 aut 
700 1 |a Petersen, Christine A  |e verfasserin  |4 aut 
700 1 |a Wilson, Mary E  |e verfasserin  |4 aut 
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