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. 2021 Jul 2;18(13):7120.
doi: 10.3390/ijerph18137120.

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

Zhijuan Song  1 , Xiaocan Jia  1   2 , Junzhe Bao  1 , Yongli Yang  1 , Huili Zhu  1 , Xuezhong Shi  1
Affiliations

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

Zhijuan Song et al. Int J Environ Res Public Health. .

Abstract

About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011-2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)52 model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.

Keywords: SARIMA model; influenza-like illness; prediction; spatiotemporal analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Epidemiological characteristics of influenza and ILI in the United States from 1st week 2011 to 29th week 2020. (a) Weekly ILI cases of different age, (b) age distribution of ILI.
Figure 2
Figure 2
Map of population density by state in the United States. The map was created by ArcGIS software.
Figure 3
Figure 3
Maps of ILI incidence for each state in the United States from 2011 to 2019 by ArcGIS software.
Figure 4
Figure 4
Maps of local spatial autocorrelation cluster of ILI for each state in the United States from 2011 to 2019 by GeoDa software.
Figure 5
Figure 5
The time diagram, ACF and PACF graphs for estimating the parameter: (a) the time diagram of ILI incidence after one-order seasonal difference data, (b) the ACF graph of the raw data (d = 0, D = 0), (c) the PACF graph of the raw data (d = 0, D = 0), (d) the time diagram of ILI incidence a, (e) the ACF graph of one-order seasonal difference data (d = 0 and D = 1), (f) the PACF graph of one-order seasonal difference data (d = 0 and D = 1).
Figure 6
Figure 6
Comparison of actual and predicted incidence of ILI in the United States.
See this image and copyright information in PMC

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