Spatiotemporal Analysis of Influenza in China, 2005–2018

Abstract

Influenza is a major cause of morbidity and mortality worldwide, as well as in China. Knowledge of the spatial and temporal characteristics of influenza is important in evaluating and developing disease control programs. This study aims to describe an accurate spatiotemporal pattern of influenza at the prefecture level and explore the risk factors associated with influenza incidence risk in mainland China from 2005 to 2018. The incidence data of influenza were obtained from the Chinese Notifiable Infectious Disease Reporting System (CNIDRS). The Besag York Mollié (BYM) model was extended to include temporal and space-time interaction terms. The parameters for this extended Bayesian spatiotemporal model were estimated through integrated nested Laplace approximations (INLA) using the package R-INLA in R. A total of 702,226 influenza cases were reported in mainland China in CNIDRS from 2005–2018. The yearly reported incidence rate of influenza increased 15.6 times over the study period, from 3.51 in 2005 to 55.09 in 2008 per 100,000 populations. The temporal term in the spatiotemporal model showed that much of the increase occurred during the last 3 years of the study period. The risk factor analysis showed that the decreased number of influenza vaccines for sale, the new update of the influenza surveillance protocol, the increase in the rate of influenza A (H1N1)pdm09 among all processed specimens from influenza-like illness (ILI) patients, and the increase in the latitude and longitude of geographic location were associated with an increase in the influenza incidence risk. After the adjusting for fixed covariate effects and time random effects, the map of the spatial structured term shows that high-risk areas clustered in the central part of China and the lowest-risk areas in the east and west. Large space-time variations in influenza have been found since 2009. In conclusion, an increasing trend of influenza was observed from 2005 to 2018. The insufficient flu vaccine supplements, the newly emerging influenza A (H1N1)pdm09 and expansion of influenza surveillance efforts might be the major causes of the dramatic changes in outbreak and spatio-temporal epidemic patterns. Clusters of prefectures with high relative risks of influenza were identified in the central part of China. Future research with more risk factors at both national and local levels is necessary to explain the changing spatiotemporal patterns of influenza in China.

Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020

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Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour

“…The parameters of the regression model are estimated by a computationally effective algorithm, integrated nested Laplace approximations (INLA) ( Rue et al., 2009 ) that has been proven to be an efficient alternative to the Markov Chain Monte Carlo (MCMC), on the R package R-INLA platform ( Lindgren and Rue, 2015 ). As recommended by Zhang et al. (2019) , the most commonly used deviance-based criterion of model selection for Bayesian models, the deviance information criterion (DIC) ( Spiegelhalter et al., 2002 ), is employed to select the best one from different spatial-temporal interactions based on Types I to IV.…”

Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions, in the United States from 2011 to 2020

“…Inadequate In uenza Vaccine Supplementation, the emergence of in uenza A (H1N1)pdm09 and the expansion of in uenza surveillance efforts may be the main reasons for the dramatic changes in in uenza outbreaks and spatio-temporal patterns. [22] States of relatively high risk for in uenza have been identi ed in United States. In order to explain the spatio-temporal pattern of in uenza, it is necessary to conduct more future studies on risk factors at the national and local levels.…”

“…This nding was con rmed by other studies. [6,22] Transmission of in uenza varies across seasons and geographical areas in the United States. The obvious temporal clusters fell during the winter and spring, which was in accordance with the seasonality of the respiratory disease.…”