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

Author

Listed:
  • Zhijuan Song

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Xiaocan Jia

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
    Zhengzhou University Library, Zhengzhou University, Zhengzhou 450001, China)

  • Junzhe Bao

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Yongli Yang

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Huili Zhu

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

  • Xuezhong Shi

    (Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China)

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.

Suggested Citation

  • Zhijuan Song & Xiaocan Jia & Junzhe Bao & Yongli Yang & Huili Zhu & Xuezhong Shi, 2021. "Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020," IJERPH, MDPI, vol. 18(13), pages 1-14, July.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:13:p:7120-:d:587779
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    References listed on IDEAS

    as
    1. Ying Mao & Rongxin He & Bin Zhu & Jinlin Liu & Ning Zhang, 2020. "Notifiable Respiratory Infectious Diseases in China: A Spatial–Temporal Epidemiology Analysis," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    2. Tatiana Petukhova & Davor Ojkic & Beverly McEwen & Rob Deardon & Zvonimir Poljak, 2018. "Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-17, June.
    3. Bin Zhu & Jinlin Liu & Yang Fu & Bo Zhang & Ying Mao, 2018. "Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003–2015): Implications for Prevention and Control Policies," IJERPH, MDPI, vol. 15(4), pages 1-17, April.
    4. Helene Økland & Svenn-Erik Mamelund, 2019. "Race and 1918 Influenza Pandemic in the United States: A Review of the Literature," IJERPH, MDPI, vol. 16(14), pages 1-18, July.
    5. Junyi Lu & Sebastian Meyer, 2020. "Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    6. Fred S. Lu & Mohammad W. Hattab & Cesar Leonardo Clemente & Matthew Biggerstaff & Mauricio Santillana, 2019. "Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    7. Hutchins, S.S. & Fiscella, K. & Levine, R.S. & Ompad, D.C. & McDonald, M., 2009. "Protection of racial/ethnic minority populations during an influenza pandemic," American Journal of Public Health, American Public Health Association, vol. 99(S2), pages 261-270.
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