Estimating incidence of infection from diverse data sources: Zika virus in Puerto Rico, 2016.
PLoS Comput Biol
; 17(3): e1008812, 2021 03.
Article
em En
| MEDLINE
| ID: mdl-33784311
ABSTRACT
Emerging epidemics are challenging to track. Only a subset of cases is recognized and reported, as seen with the Zika virus (ZIKV) epidemic where large proportions of infection were asymptomatic. However, multiple imperfect indicators of infection provide an opportunity to estimate the underlying incidence of infection. We developed a modeling approach that integrates a generic Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases in a Bayesian framework and applied it to the 2016 Zika epidemic in Puerto Rico using three indicators suspected arboviral cases, suspected Zika-associated Guillain-Barré Syndrome cases, and blood bank data. Using this combination of surveillance data, we estimated the peak of the epidemic occurred during the week of August 15, 2016 (the 33rd week of year), and 120 to 140 (50% credible interval [CrI], 95% CrI 97 to 170) weekly infections per 10,000 population occurred at the peak. By the end of 2016, we estimated that approximately 890,000 (95% CrI 660,000 to 1,100,000) individuals were infected in 2016 (26%, 95% CrI 19% to 33%, of the population infected). Utilizing multiple indicators offers the opportunity for real-time and retrospective situational awareness to support epidemic preparedness and response.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Epidemias
/
Zika virus
/
Infecção por Zika virus
Tipo de estudo:
Incidence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Caribe
/
Puerto rico
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos