Skip to main content

Study on the relationship between the incidence of influenza and climate indicators and the prediction of influenza incidence

Environmental Science and Pollution Research volume 28, pages 473–481 (2021)Cite this article

Abstract

In recent 2 years, the incidence of influenza showed a slight upward trend in Guangxi; therefore, some joint actions should be done to help preventing and controlling this disease. The factors analysis of affecting influenza and early prediction of influenza incidence may help policy-making so as to take effective measures to prevent and control influenza. In this study, we used the cross correlation function (CCF) to analyze the effect of climate indicators on influenza incidence, ARIMA and ARIMAX (autoregressive integrated moving average model with exogenous input variables) model methods to do predictive analysis of influenza incidence. The results of CCF analysis showed that climate indicators (PM2.5, PM10, SO2, CO, NO2, O3, average temperature, maximum temperature, minimum temperature, average relative humidity, and sunshine duration) had significant effects on the incidence of influenza. People need to take good precautions in the days of severe air pollution and keep warm in cold weather to prevent influenza. We found that the ARIMAX (1,0,1)(0,0,1)12 with NO2 model has good predictive performance, which can be used to predict the influenza incidence in Guangxi, and the predicted incidence may be useful in developing early warning systems and providing important evidence for influenza control policy-making and public health intervention.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  • Arikawa G, Fujii Y, Abe M, Mai NT, Mitoma S, Notsu K, Nguyen HT, Elhanafy E, Daous HE, Kabali E, Norimine J, Sekiguchi S (2019) Meteorological factors affecting the risk of transmission of HPAI in Miyazaki, Japan. Vet Rec Open 6(1):e000341

    Article  Google Scholar 

  • Bekking C, Yip L, Groulx N, Doggett N, Finn M, Mubareka S (2019) Evaluation of bioaerosol samplers for the detection and quantification of influenza virus from artificial aerosols and influenza virus–infected ferrets. Influenza Other Respir Viruses 13(6088):564–573

    CAS  Article  Google Scholar 

  • Box GE, Jenkins GM (1976) Time series analysis: forecasting and control rev. ed. Oakland, California. Holden-Day 31(4):238–242

    Google Scholar 

  • Brattig NW, Tanner M, Bergquist R, Utzinger J (2019) Impact of environmental changes on infectious diseases: Key findings from an international conference in Trieste, Italy in May 2017. Acta Trop:105165

  • Chong KC, Lee TC, Bialasiewicz S et al (2019) Association between meteorological variations and activities of influenza A and B across different climate zones: a multi-region modeling analysis across the globe. J Inf Secur 30(19):S0163–S44533

    Google Scholar 

  • Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R, Feigin V, Freedman G, Hubbell B, Jobling A, Kan H, Knibbs L, Liu Y, Martin R, Morawska L, Pope CA III, Shin H, Straif K, Shaddick G, Thomas M, van Dingenen R, van Donkelaar A, Vos T, Murray CJL, Forouzanfar MH (2017) Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the global burden of diseases study 2015. Lancet 389(10082):1907–1918

    Article  Google Scholar 

  • Chen EQ, Zeng F, Zhou LY et al (2018) Early warning and clinical outcome prediction of acute-on chronic hepatitis B liver failure. World J Gastroenterol 42:92–101

  • Elhakim M, Hafiz R M, Fahim M, et al. (2019) Epidemiology of severe cases of influenza and other acute respiratory infections in the eastern Mediterranean region, July 2016 to June 2018. J Infect Publ Health

  • Feng Z, Velasco-Hernandez J, Tapia-Santos B (2013) A mathematical model for coupling within-host and between-host dynamics in an environmentally-driven infectious disease. Math Biosci 241(1):49–55

    Article  Google Scholar 

  • Gabriel A et al (2019) Dengue outbreaks: unpredictable incidence time series. Epidemiol Infect 147:E116,1–E116,7

    Article  Google Scholar 

  • Gharbi M, Quenel P, Gustave J et al (2011) Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC Infect Dis 11:166

    Article  Google Scholar 

  • Khan MD, Thi Vu HH, Lai QT, Ahn JW (2019) Ahn JW. Aggravation of human diseases and climate change nexus. Int J Environ Res Public Health 16(15):2799

    Article  Google Scholar 

  • Li H, Luo et al. (2017) An artificial neural network prediction model of congenital heart disease based on risk factors: a hospital-based case-control study 96(6):e6090

  • Liu L, Luan RS, Yin F et al (2016) Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model. Epidemiol Infect 144(01):144–151

    CAS  Article  Google Scholar 

  • Mao Q, Zhang K, Yan W et al (2018) Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. J Infect Publ Health 2018:S1876034118300455

    Google Scholar 

  • Nikonahad A, Khorshidi A, Ghaffari HR, Aval HE, Miri M, Amarloei A, Nourmoradi H, Mohammadi A (2017) A time series analysis of environmental and metrological factors impact on cutaneous leishmaniasis incidence in an endemic area of Dehloran, Iran. Environ Sci Pollut Res 24:14117–14123

    Article  Google Scholar 

  • Priedhorsky R, Daughton AR, Barnard M, O’Connell F, Osthus D (2019) Estimating influenza incidence using search query deceptiveness and generalized ridge regression. PLoS Comput Biol 15(10):e1007165

    CAS  Article  Google Scholar 

  • Rodrigues E, Machado A, Silva S, Nunes B (2018) Excess pneumonia and influenza hospitalizations associated with influenza epidemics in Portugal from season 1998/1999 to 2014/2015. Influenza Other Respir Viruses 12(1):153–160

    Article  Google Scholar 

  • Stewart-Ibarra AM, Romero M, Hinds AQJ, Lowe R, Mahon R, van Meerbeeck CJ, Rollock L, Gittens-St. Hilaire M, St. Ville S, Ryan SJ, Trotman AR, Borbor-Cordova MJ (2019) Co-developing climate services for public health: stakeholder needs and perceptions for the prevention and control of Aedes-transmitted diseases in the Caribbean. PLoS Negl Trop Dis 13(10):e0007772

    Article  Google Scholar 

  • Tian C et al (2019) Time-series modeling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018. Epidemiol Infect 147:E82,1–E82,3

    Article  Google Scholar 

  • Tuerlinckx D, Bodart E, Jamart J, Glupczynski Y (2009) Prediction of Lyme meningitis based on a logistic regression model using clinical and cerebrospinal fluid analysis. Pediatr Infect Dis J 28(5):394–397

    Article  Google Scholar 

  • Wang Y-w, Shen Z-z, Jiang Y (2019) Comparison of autoregressive integrated moving average model and generalized regression neural network model for prediction of hemorrhagic fever with renal syndrome in China: a time-series study. BMJ Open 9(6):e025773

    Article  Google Scholar 

  • Watts N, Amann M, Arnell N, Ayeb-Karlsson S, Belesova K, Berry H, Bouley T, Boykoff M, Byass P, Cai W, Campbell-Lendrum D, Chambers J, Daly M, Dasandi N, Davies M, Depoux A, Dominguez-Salas P, Drummond P, Ebi KL, Ekins P, Montoya LF, Fischer H, Georgeson L, Grace D, Graham H, Hamilton I, Hartinger S, Hess J, Kelman I, Kiesewetter G, Kjellstrom T, Kniveton D, Lemke B, Liang L, Lott M, Lowe R, Sewe MO, Martinez-Urtaza J, Maslin M, McAllister L, Mikhaylov SJ, Milner J, Moradi-Lakeh M, Morrissey K, Murray K, Nilsson M, Neville T, Oreszczyn T, Owfi F, Pearman O, Pencheon D, Pye S, Rabbaniha M, Robinson E, Rocklöv J, Saxer O, Schütte S, Semenza JC, Shumake-Guillemot J, Steinbach R, Tabatabaei M, Tomei J, Trinanes J, Wheeler N, Wilkinson P, Gong P, Montgomery H, Costello A (2018) The 2018 report of the lancet countdown on health and climate change: shaping the health of nations for centuries to come. Lancet. 392(10163):2479–2514

    Article  Google Scholar 

  • Wei W, Jiang J, Gao L et al (2017) A new hybrid model using an autoregressive integrated moving average and a generalized regression neural network for the incidence of tuberculosis in Heng County, China. Am J Tropic Med Hygiene 97(3):799–805

    Article  Google Scholar 

  • Yang X, Zou J, Kong D, Jiang G (2018) The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China. Medicine 97(34):e11787

    Article  Google Scholar 

  • Yi-Yi Z, Wei F, Qi Z et al (2012) Application of multiple seasonal ARIMA model in forecasting the incidence of hepatitis A in Shanghai. Fudan Univ J Med Sci 39(5):460–464

    Google Scholar 

  • Zhao D, Wang L, Cheng J et al (2017) Impact of weather factors on hand, foot and mouth disease, and its role in short-term incidence trend forecast in Huainan City, Anhui Province. Int J Biometeorol 61:453–461

    Article  Google Scholar 

Download references

Funding

This work was supported by the Natural science funding of Xinjiang Uygur Autonomous Region (Grant No.2019D01C206), China.

Author information

Affiliations

  1. College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People’s Republic of China

    Yanling Zheng, Kai Wang, Liping Zhang & Lei Wang

Authors
  1. Yanling Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanling Zheng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Responsible Editor: Lotfi Aleya

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zheng, Y., Wang, K., Zhang, L. et al. Study on the relationship between the incidence of influenza and climate indicators and the prediction of influenza incidence. Environ Sci Pollut Res 28, 473–481 (2021). https://doi.org/10.1007/s11356-020-10523-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-020-10523-7

Keywords

  • Influenza
  • Climate indicators
  • Prediction
  • ARIMA model
  • ARIMAX model