How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD.
Epidemiol Infect
; 149: e34, 2021 01 15.
Article
em En
| MEDLINE
| ID: mdl-33446283
ABSTRACT
This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (-24.88%; t = -5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (-16.69%; t = -4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Controle de Doenças Transmissíveis
/
Monitoramento Ambiental
/
Doença de Mão, Pé e Boca
/
Conceitos Meteorológicos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article