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

Marie V Ozanne; Grant D Brown; Jacob J Oleson; Iraci D Lima; Jose W Queiroz; Selma M. B Jeronimo; Christine A Petersen; Mary E Wilson

doi:10.1080/02664763.2018.1531979

Back

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

Journal article

Peer reviewed

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

Marie V Ozanne, Grant D Brown, Jacob J Oleson, Iraci D Lima, Jose W Queiroz, Selma M. B Jeronimo, Christine A Petersen and Mary E Wilson

Journal of applied statistics, Vol.46(6), pp.1043-1065

2019

DOI: 10.1080/02664763.2018.1531979

PMCID: PMC6752225

PMID: 31537954

Files and links (1)

url

https://www.ncbi.nlm.nih.gov/pmc/articles/6752225View

Open Access

Abstract

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.

Bayes factor

multinomial

seasonality

SIR

visceral leishmaniasis

empirically-adjusted reproductive number

Details

Title: Subtitle: Bayesian compartmental model for an infectious disease with dynamic states of infection
Creators: Marie V Ozanne - National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
Grant D Brown - National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
Jacob J Oleson - National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
Iraci D Lima - National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
Jose W Queiroz - National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
Selma M. B Jeronimo - National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
Christine A Petersen - National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
Mary E Wilson - National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
Resource Type: Journal article
Publication Details: Journal of applied statistics, Vol.46(6), pp.1043-1065
DOI: 10.1080/02664763.2018.1531979
PMID: 31537954
PMCID: PMC6752225
NLM abbreviation: J Appl Stat
ISSN: 0266-4763
eISSN: 1360-0532
Grant note: DOI: 10.13039/100000061, name: Fogarty International Center, award: R01TW010500; DOI: 10.13039/100000060, name: National Institute of Allergy and Infectious Diseases, award: P50 AI30639
Language: English
Date published: 2019
Academic Unit: Microbiology and Immunology; Infectious Diseases; International Programs; Epidemiology; Biostatistics; Internal Medicine
Record Identifier: 9983996074102771

Metrics

31 Record Views

6 Times Cited - Web of Science