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Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory
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Journal of Traditional Chinese Medical Sciences (2018) xx, 1e7
Available online at www.sciencedirect.com
ScienceDirect journal homepage: http://www.elsevier.com/locate/jtcms
Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory Hong Wang a,1, Xuan Zhang b,1, Zhili Gao a, Ling Han a, Zhongdi Liu b, Long Yan a, Mingyue Li c, Juan He a,* a School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China b School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong c Department of Applied Language, Peking University Health Science Center, Beijing, 100191, China
Received 29 November 2017; received in revised form 13 June 2018; accepted 13 June 2018
Available online - - -
KEYWORDS Incidence of influenza; Meteorological factors; Yunqi theory; Back-propagation artificial neural network
Abstract Objective: To analyze the impact of meteorological factors on the incidence of influenza based on the Yunqi theory in Beijing area, and to establish an effective forecast model. Methods: Monthly data on the incidence of influenza from 1970 to 2004 and daily data on the meteorological factors (including daily averages of temperature, wind speed, relative humidity, vapor pressure, and daily total precipitation) from 1966 to 2004 were collected and processed under the traditional Chinese medicine (TCM) theory of six qi. A back-propagation artificial neural network was then performed to analyze the data. Results: The highest incidence of influenza occurs in the sixth qi (the period of December and January), which is characterized by dryness and coldness. Altogether six models were successfully established. Climatic data used were of the same year, one year prior, two years prior, and three years prior to the influenza data respectively. The last two models involve climatic data of the previous three years plus the current year and of the past four years plus the current year. Finally, we determined the fifth model has the highest forecast accuracy (49%). Conclusions: Meteorological factors can exert an influence on the incidence of influenza, which corresponds to TCM theory that “the pestilence occurred three years after the abnormal climatic changes”. This study may generate interest among the public health community and other TCM theories can be applied so that public health measures can be taken to prevent and control influenza, particularly during the winter months.
* Corresponding author. E-mail address: [email protected] (J. He). Peer review under responsibility of Beijing University of Chinese Medicine. 1 These authors contributed equally to the article. https://doi.org/10.1016/j.jtcms.2018.06.003 2095-7548/ª 2018 Beijing University of Chinese Medicine. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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H. Wang et al. ª 2018 Beijing University of Chinese Medicine. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).
Introduction As one of the most common communicable diseases, influenza impacts human populations of all age groups and causes considerable morbidity and mortality worldwide. The influenza virus spans from self-limiting conditions to life threatening complications to which children under age 5, the elderly, and pregnant women are highly vulnerable. To effectively relieve the burden posed on the healthcare system from influenza, efforts have focused on viral resistance, development of vaccines and vaccination programs and clinical treatment.1,2 In addition to these measures, predicting the timing and magnitude of influenza epidemics is also important so that preventative public health measures can be taken. In analyzing the inter-annual and intraannual variety of influenza peaks, recent studies have associated meteorological factors such as absolute humidity and temperature with the incidence of influenza.3e5 The recognition that climatic conditions might relate to infectious diseases has been extensively documented throughout history in the West and the East. According to Yunqi theory, detailed in the Yellow Emperor’s Canon of Medicine, the human body and the natural environment correspond with each other, and systematically discusses how a variety of abnormal climatic changes may impact on human health. According to Yunqi theory there are long term effects that result from significant climatic abnormalities on infectious diseases: “the pestilence occurred three years after the abnormal climate changes” (hereafter referred to as “Sannian huayi”, a generalized doctrine in TCM). Gu ZS reported the SARS outbreak in 2003 coincided with this theory since they have successfully predict the outbreak utilizing the theory and produced effective treatment.6 After increased attention has been paid to Yunqi theory, research institutions in health sectors incorporated TCM treatments in response to the outbreak of influenza in 2009. Some experts held the view that the lower incidence of serious cases and deaths in China, compared to other countries, was due in part to the employment of TCM treatments based on Yunqi theory in the early stages and throughout the entire course of outbreak.7e9 For this study we explored the impact of meteorological factors on the incidence of influenza in Beijing over the past 35 years based on the TCM Yunqi theory. Our study aimed to establish statistical models to investigate correlations over this period and to develop an influenzameteorological forecasting model based on backpropagation artificial neural networks.
Methods Study area Beijing, the capital of China, was selected for this study primarily because of its large population and advanced
disease surveillance system. With a location of 40 N and 116 E, Beijing has a semi-humid continental monsoon climate and four distinct seasons: cold dry winter, hot rainy summer, and relatively short spring and autumn.10,11
Data collection Monthly influenza incidence from January 1970 to December 2004 were provided by the Beijing Center for Disease Control and Prevention. An analysis of Yunqi theory required us to take 39 years of meteorological data from January 1, 1966 to December 31, 2004. Data based on five meteorological variables were retrieved from the Beijing Meteorological Observatory, including daily averages of temperature, wind speed, relative humidity, vapor pressure and daily total precipitation.
Data pre-processing Today the seasons are divided into four periods, however, traditional Chinese calendar divided one year near equally into 24 solar terms and these fell into six near equal periods (with each four terms forming one period) based on Yunqi theory. The six periods are called six qi. The first period is qi Feng (wind) (from the Spring Beginning to the Vernal Equinox according to the lunar Chinese calendar, a period roughly corresponding to February and March); the 2nd qi Huo (heat) (from Fresh Green to Lesser Fullness, April and May); the 3rd qi Shu (summer heat) (from Grain in Ear to Greater Heat, June and July); the 4th qi Shi (wet) (from the beginning of Autumn to the Autumn Equinox, August and September); the 5th qi Zao (dry) (from Cold Dew to Light Snow, October and November); and the 6th qi Han (cold) (from Heavy Snow to Greater Cold, December and next January).12 It should be noted that, take the first qi as an example, the period from the Beginning of Spring to the Spring Equinox spans from the end of January to the end of March, but not exactly February and March. Yet owing to the limitations of the data available to us, we could only estimate that February and March is equivalent of the first qi period, and similar estimations also apply to other qi periods. Additionally, translation of the proper nouns regarding the Chinese lunar calendar and TCM terms in this paper are in accordance with standards regulated by China National Committee for Terms in Sciences and Technologies. Consequently, the incidence of influenza in Beijing for each qi period from 1970 to 2003 was calculated to establish a database. Similarly, another database was created with the average values of the five meteorological variables from 1966 to 2003. Due to the lack of influenza data in 2005, the data of the sixth period in 2004 could not be incorporated.
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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Impact of meteorological factors
Statistical methods An artificial neural network (ANN) is a massively parallel distributed processor that simulates the biological neural networks of animal brains.13 ANN includes a large number of highly interconnected processing units (neurons) that work together to solve specific problems in the sciences. A back-propagation artificial neural network (BPANN) is currently a widely used ANN, which is a multi-layer feedforward neural network trained according to backpropagation algorithm. Training a BPANN consists of two steps. First, the input signal spreads across the network until the output layer is reached. Second, an error signal is computed by comparing the output of the network and the expectation. The error signal is transmitted again through the network layer by layer, but this time the propagation is in the opposite direction. In the second stages, the synaptic weights of the network are constantly revised. The two steps are repeated until the response of the network to the input reaches a predetermined target area. The advantages of BPANN include stronger adaptability, faster parallel processing and less experiential knowledge involvement, which provide a simple and effective method for solving complex problems.14 BPANN has been applied in various fields of medical research during the past decade.15,16 Descriptive statistical analysis was first conducted to demonstrate the general regularities of weather changes and influenza incidence in Beijing during the six qi phases over the past 35 years. Then, a BPANN was performed to examine effects of various meteorological factors on the outbreak of influenza. The modeling process of BPANN is as follows: meteorological factors were used as input variables and the predictive influenza incidence as output variables. All of the samples were divided into training samples (TRS) and test samples (TES) to prevent over fitting. An ideal network model could be achieved when the TES were less than the TRS. Consequently, the data for training and forecasting were randomly selected at the default ratio of 7:3, a ratio most commonly used in the statistical analysis.17 Six influenza prediction models were established based on data of five meteorological factors and influenza morbidity. Corresponding periods of climatic data and influenza data from 1970 to 2003 were adopted in the modelling. (1) Model One was based on the assumption that one years’ influenza incidence is largely influenced by the same year’s weather, and therefore the influenza data and climatic data used both ranged from 1970 to 2003. (2) For Model Two, climatic data used were one year prior to influenza data so as to examine whether certain previous year’s weather could also influence this year’s influenza morbidity. Thus the data on climatic factors were from 1969 to 2002. (3) Correspondingly Model Three climatic data were from 1968 to 2001, two years prior to influenza data. (4) Same as in Model Two but involved climatic data between 1967 and 2000, the climate data of Model Four were three years prior to influenza data. From Model One to Model Four, meteorological data of one single year was utilized while Model Five and Six involved multiple years of climatic data. (5) As far as Model Five is concerned, for each year’s influenza data, climatic data of the same year
3 plus its previous three years were all employed. For example, if influenza data from 1970 is used, four years of climatic data from 1967 to 1970 were all incorporated into the modeling process. The same applied to other years’ data. (6) Similarly, Model Six used climatic data of the previous four years’ plus the same year’. Next, we evaluated the forecasting accuracy of the above six models and selected the one with the highest accuracy. All analyses were conducted with SPSS version 17.0.
Results Descriptive analysis The average temperature, average precipitation, and average vapor pressure peaked in the 3rd qi period (June and July), and the lowest points of these variables were observed in the 6th qi (December and next January). The average relative humidity peaked in the 4th qi (August and September), followed by a secondary peak in the 3rd qi, and reached the lowest point in the 6th qi followed by the 1st qi (February and March). Additionally, the average wind speed peaked in the 2nd qi (April and May) and hit bottom in the 4th qi. As was manifested by the curves diagrams of the five basic meteorological factors, the 6th qi in Beijing that usually corresponds to December and January was dominated by extreme coldness and dryness (Fig. 1). Influenza incidence reported from 1970 to 2003 in Beijing was 359,374 and the average annual incidence was 10,570. The distribution of influenza cases exhibited a considerable intra-annual variability and marked seasonality. The 6th qi associated with 69% of the annual influenza incidence accounts for the largest proportion. Among the 1762 average influenza cases in all the six qi periods, the counts in the 6th qi was 4.12-fold that of the average (Fig. 2).
Influenza-meteorological forecast model based on back propagation artificial neural network Meteorological data used were of the same year with influenza data in Model One Samples throughout the 35 years fell into the category of TRS and TES, and the TRS comprised 74.0% of all samples while the TES accounted for the rest 26.0%. The training relative error (TRRE) value was 0.871, and test relative error (TERE) value was 0.711. Both of the TRRE and TERE values were less than 1, which indicated Model One was successfully built up (the criteria for the success of model establishment is the same below). Among the meteorological factors, average temperature has the most significant impact on influenza, followed by average wind speed (an impact degree of over 50% was considered to be significant) (Table 1). Meteorological data one year prior to influenza data in Model Two The TRS and the TES comprised 71.1% and 28.9% of all samples, respectively. Because the values of TRRE and TERE were 0.849 and 0.984, respectively, Model Two again
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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Figure 1 Distributions of the daily averages of five meteorological factors in the six phases from 1967 to 2003. Note (A) average temperature, (B) average wind speed, (C) average precipitation, (D) average relative humidity, (E) average vapor pressure.
Figure 2
Distribution of influenza incidence in six qi in Beijing from 1970 to 2003.
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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Impact of meteorological factors Table 1
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Contributions of the meteorological factors in Model One to Four.
Meteorological factors
Model One
Model Two
Model Three
Model Four
Average Average Average Average Average
100.0 74.9 30.9 11.0 5.7
100.0 63.2 60.6 37.5 14.2
99.9 36.4 61.2 27.2 100.0
100.0 49.2 70.4 35.8 64.6
temperature wind speed relative humidity precipitation vapor pressure
was successfully established. Among the significant meteorological factors, their contribution to influenza incidence rank (from the greatest to least) as: average temperature, average wind speed and average relative humidity (Table 1). Meteorological data two years prior to influenza data in Model Three The percentage of the TRS was 64.2%, and the TES 35.8%. The values of TRRE and TERE were 0.873 and 0.773 respectively, indicating the successful establishment of Model Three. Significant meteorological factors in descending order were: average vapor pressure, average temperature and average relative humidity, and the first two factors have the same contribution (Table 1). Meteorological data three years prior to influenza data in Model Four The percentage of TRS and of TES was 71.1% and 28.9%, respectively. Because the TRRE value was 0.789, and the TERE value 0.869, Model Four was also successful. Significant meteorological factors in descending order were: average temperature, average relative humidity and average vapor pressure (Table 1). The latest four years of meteorological data in Model Five Given that the above four models were all successfully built, multiple years of climatic data were incorporated together to establish a predictive model: for each year’s influenza data, climatic data of the same year and of its previous three years were all considered. For this new model, the percentage of the TRS was 72.5%, and of the TES 27.5%. The value of TRRE was 0.456, and that of the TERE was 0.570. Obviously, Model Five was successfully built up. It should be noted that the relative error values in Model Five were significantly lower than those in the previous four models. Among the significant meteorological factors, average relative humidity two years ago contributed the most to the incidence of influenza, followed by average wind speed three years ago, and average wind speed one year ago (Table 2). The latest five years of meteorological data in Model Six Similar to Model Five, for influenza data of each year, its previous four years’ and the same year’s climatic data were all utilized to establish a new predictive model. For Model Six, the percentage of TRS was 70.1%, and that of TES 29.9%. The TRRE value was 0.783, and the TERE value 0.858. Although Model Six was also successfully established with the two values less than 1, both values were remarkably higher than those in Model Five. Thus we neglected the
meteorological factors in the even more previous years. The “S. huayi” theory therefore was proved to be reasonable. Comparison of forecasting accuracy in the six models The first four models illustrated above used the data of influenza morbidity and climatic factors of one single year, while Model Five used four years of climatic data. Much improvement was observed in Model Five, as the prediction accuracy nearly doubled or tripled that of the previous four models. Yet the accuracy tended to decrease when more climatic data of previous year’s was integrated (Table 3).
Discussion Based upon the theory of Yunqi this retrospective study examined correlations between influenza incidence and meteorological factors in Beijing. By performing BPANN, six forecasting models were established with meteorological data varying from one single year to multiple years. Then significant meteorological factors were identified and a forecasting model was determined.
Table 2 Contributions of the meteorological factors in Model Five. Time Current year
Meteorological factors
Average Average Average Average Average One year ago Average Average Average Average Average Two years ago Average Average Average Average Average Three years ago Average Average Average Average Average
vapor pressure relative humidity wind speed temperature precipitation wind speed vapor pressure relative humidity precipitation temperature relative humidity wind speed vapor pressure precipitation temperature wind speed temperature precipitation relative humidity vapor pressure
Contributions (%) 42.8 38.9 30.7 27.8 22.9 54.4 35.9 35.4 33.9 19.5 100.0 76.4 39.9 33.7 23.4 57.8 48.0 37.1 24.7 19.3
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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The results of the influenza-meteorological forecast models.
Model
Relative error (Training)
Relative error (Test)
Prediction accuracy (total)
One (Current year) Two (1 year ago) Three (2 years ago) Four (3 years ago) Five (4 years in total) Six (5 years in total)
0.871 0.849 0.873 0.789 0.456 0.783
0.711 0.984 0.773 0.869 0.570 0.858
21% 9% 18% 17% 49% 18%
Unlike most previous studies based on monthly climatic data, our study made use of data calculated by six qi theory.18 According to six qi theory one year is near equally divided into 24 solar terms under Chinese calendar. One qi phase is comprised of four successive solar terms. Another feature of this study was its focus on the comparatively long-term impact of meteorological variables. To our knowledge, such analyses were relatively rare. The “S. huayi”19e22 doctrine was initially raised in Yellow Emperor’s Canon of Medicine$Plain Conversation. This work contributed observations on how abrupt climatic changes or unusual weather conditions during the past three years exerted influences on the present incidence of infectious diseases. Our consideration of six different models dealing with climatic data of different years was also inspired by this doctrine. Our success in establishing the six models in turn can be aligned with those outlined in the Yellow Emperor’s Canon of Medicine. With the forecast accuracy of the TRS and TES both approximating 49%, Model Five exceeded all the rest five models. This finding further illustrated that forecast accuracy might be improved when four years’ climatic data were all incorporated in modeling while using one single year’s data or adding data of the even previous years might not be as accurate. When it comes to the most important meteorological factors, temperature is worth a mention. Among the six models we established, temperature was proved to be the most significant factor in three models and ranked the second in one model. The important contribution of temperature to the occurrence of influenza is also consistent with previous studies. Biological evidence includes higher probability of influenza virus to enter the respiratory tract when nasal cilia’s movement slow down at low temperature.23,24 Yet as the model with the best prediction accuracy, Model Five suggested otherwise. Other than temperature, both average relative humidity and average wind speed two years ago were selected as the most significant meteorological factors. Other local climatic changes apart from temperature should also be accounted for and this observation also coincides with the past findings on infectious diseases.25e27 The innovations of this study are summarized as follows. First, based on the theory of Yunqi, six qi doctrine was used to describe the seasonal pattern of the meteorological variables and influenza incidence. Moreover, we concentrated on how climatic changes during as long as the past three years exert influences on the present incidence of influenza, which might contribute to timely forecasts on the ground of early warning models. Additionally, BPANN was introduced to select the most significant weather
indicators and to build up influenza-meteorological predictive models. The determination of the best model rests on the analysis and comparison among the established models. We integrated TCM theory with the analysis of meteorological data while other study purely adopted modern statistical method. Finally comes our successful confirmation of the Yunqi doctrine that, “the pestilence occurred three years after the abnormal climate changes.” Yet this study was not free from limitations. Influenza outbreaks are influenced by multifaceted determinants yet our study merely concentrated on meteorological variables. The role other relevant factors might play in influenza incidence remained less documented, such as economic development, improvements in health service, population growth, school schedules, air pollution, indoor airconditioner and winter heating systems. In addition, the sources of data were restricted to a single study area (Beijing) with limited sample size. Local climate conditions, demographic differences, and ecological characteristics might all affect the results to some degree. Probably more accurate results free of confounding factors can be obtained by comparison between different places, which is expected to be achieved in future studies. Another point is that our verification of the Yunqi theory had better be extended elsewhere in regions of different geographical and meteorological factors. It is hoped that future research will empower more public health sectors to translate such ancient theories.
Funding This work was granted by the National Natural Science Foundation of China (No. 81072896 and No. 81704198).
Competing interests The authors declare that they have no competing interests.
Authors’ contributions Hong Wang and Xuan Zhang wrote and revised the manuscript. Zhili Gao, Ling Han, Zhongdi Liu, Long Yan and Mingyue Li checked and analyzed the data. Juan He conceived and designed the experiments. All authors have read and approved the contents of the final version.
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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Acknowledgment We would like to express our sincere gratitude to all the data producers: the Beijing Meteorological Observatory and the Beijing Center for Disease Control and Prevention.
Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jtcms.2018.06.003.
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7 13. Simon H. Neural Networks Learning Machines. Beijing, China: China Machine Press; 2011. 14. Sun ZQ. Medical Statistics. Beijing, China: People’s Health Publishing Press; 2007. 15. Ma SL, Tang QL, Liu HW, et al. Correlation analysis for the attack of bacillary dysentery and meteorological factors based on the Chinese medicine theory of Yunqi and the medicalmeteorological forecast model. Chin J Integr Med. 2013;3: 182e186. 16. Hu JY, Jiang MJ, Jing YS, et al. Application of neural network in forecasting tuberculosis caused by meteorological factors. Bull Sci Technol. 2013;29:19e23. 17. Zhang WT, Zhong YF. Case Pristine in Data Analysis and Mining of IBM SPSS. Beijing, China: Tsinghua University Press; 2013. 18. Critselis E, Nastos PT, Theodoridou K, et al. Time trends in pediatric hospitalizations for varicella infection are associated with climatic changes: a 22-year retrospective study in a Tertiary Greek Referral Center. PLoS One. 2012;7:1e7. 19. Zhang X, Hao Y, Fei ZY, et al. Effect of meteorological factors on incidence of tuberculosis: a 15-year retrospective study based on Chinese medicine theory of five circuits and six qi. Chin J Integr Med. 2015;21(10):751e758. 20. Zhang X, Liu ZD, He J. Exploring the correlation between the incidence of measles and revious meteorological factors and establishing prediction model based on the theory of pestilence occurring after three years. Chin J Tradit Chin Med Pharm. 2014;11:3400e3403. 21. Cui HT, Su Y. Regularity exploration about three years of disease theory in the Canon of Internal Medicine. J Changchun Univ Chin Med. 2016;32(5):881e883. 22. Gu ZS. Explain SARS with three years of disease theory. Chin J Basic Med Tradit Chin Med. 2003;9(12):1e3. 23. Yang J, Qu CQ, Ding Y, et al. Distributed lag non-linear model. Zhong Guo Wei Sheng Tong Ji. 2012;29(5):772e773. 24. Kang Y, Li XN, Lu JY, et al. Correlation between meteorological factors and influenza incidence during 2010-2014 in Guangzhou of China. Chin J Viral Dis. 2016;4:294e299. 25. Liu ZD, Zhang X, He J. Relationship between meteorological factors and incidence of dysentery in Beijing area based on theory of pestilence occurring after three years. J Beijing univ Tradit Chin Med. 2013;4:258e262. 26. Zhang X, Zulihumaer A, Liu ZD, et al. Correlation between typhoid/paratyphoid and meteorological factors and establishment of forecast model based on theory of five circuits and six qi. J Beijing univ Tradit Chin Med. 2013;12:797e801. 27. Zhang X, He J. Correlation between meteorological factors and mumps incidence in Beijing from 1990 to 2004. Chin J Tradit Chin Med Pharm. 2014;5:1740e1745.
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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Journal of Traditional Chinese Medical Sciences (2018) xx, 1e7
Available online at www.sciencedirect.com
ScienceDirect journal homepage: http://www.elsevier.com/locate/jtcms
Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory Hong Wang a,1, Xuan Zhang b,1, Zhili Gao a, Ling Han a, Zhongdi Liu b, Long Yan a, Mingyue Li c, Juan He a,* a School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China b School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong c Department of Applied Language, Peking University Health Science Center, Beijing, 100191, China
Received 29 November 2017; received in revised form 13 June 2018; accepted 13 June 2018
Available online - - -
KEYWORDS Incidence of influenza; Meteorological factors; Yunqi theory; Back-propagation artificial neural network
Abstract Objective: To analyze the impact of meteorological factors on the incidence of influenza based on the Yunqi theory in Beijing area, and to establish an effective forecast model. Methods: Monthly data on the incidence of influenza from 1970 to 2004 and daily data on the meteorological factors (including daily averages of temperature, wind speed, relative humidity, vapor pressure, and daily total precipitation) from 1966 to 2004 were collected and processed under the traditional Chinese medicine (TCM) theory of six qi. A back-propagation artificial neural network was then performed to analyze the data. Results: The highest incidence of influenza occurs in the sixth qi (the period of December and January), which is characterized by dryness and coldness. Altogether six models were successfully established. Climatic data used were of the same year, one year prior, two years prior, and three years prior to the influenza data respectively. The last two models involve climatic data of the previous three years plus the current year and of the past four years plus the current year. Finally, we determined the fifth model has the highest forecast accuracy (49%). Conclusions: Meteorological factors can exert an influence on the incidence of influenza, which corresponds to TCM theory that “the pestilence occurred three years after the abnormal climatic changes”. This study may generate interest among the public health community and other TCM theories can be applied so that public health measures can be taken to prevent and control influenza, particularly during the winter months.
* Corresponding author. E-mail address: [email protected] (J. He). Peer review under responsibility of Beijing University of Chinese Medicine. 1 These authors contributed equally to the article. https://doi.org/10.1016/j.jtcms.2018.06.003 2095-7548/ª 2018 Beijing University of Chinese Medicine. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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H. Wang et al. ª 2018 Beijing University of Chinese Medicine. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).
Introduction As one of the most common communicable diseases, influenza impacts human populations of all age groups and causes considerable morbidity and mortality worldwide. The influenza virus spans from self-limiting conditions to life threatening complications to which children under age 5, the elderly, and pregnant women are highly vulnerable. To effectively relieve the burden posed on the healthcare system from influenza, efforts have focused on viral resistance, development of vaccines and vaccination programs and clinical treatment.1,2 In addition to these measures, predicting the timing and magnitude of influenza epidemics is also important so that preventative public health measures can be taken. In analyzing the inter-annual and intraannual variety of influenza peaks, recent studies have associated meteorological factors such as absolute humidity and temperature with the incidence of influenza.3e5 The recognition that climatic conditions might relate to infectious diseases has been extensively documented throughout history in the West and the East. According to Yunqi theory, detailed in the Yellow Emperor’s Canon of Medicine, the human body and the natural environment correspond with each other, and systematically discusses how a variety of abnormal climatic changes may impact on human health. According to Yunqi theory there are long term effects that result from significant climatic abnormalities on infectious diseases: “the pestilence occurred three years after the abnormal climate changes” (hereafter referred to as “Sannian huayi”, a generalized doctrine in TCM). Gu ZS reported the SARS outbreak in 2003 coincided with this theory since they have successfully predict the outbreak utilizing the theory and produced effective treatment.6 After increased attention has been paid to Yunqi theory, research institutions in health sectors incorporated TCM treatments in response to the outbreak of influenza in 2009. Some experts held the view that the lower incidence of serious cases and deaths in China, compared to other countries, was due in part to the employment of TCM treatments based on Yunqi theory in the early stages and throughout the entire course of outbreak.7e9 For this study we explored the impact of meteorological factors on the incidence of influenza in Beijing over the past 35 years based on the TCM Yunqi theory. Our study aimed to establish statistical models to investigate correlations over this period and to develop an influenzameteorological forecasting model based on backpropagation artificial neural networks.
Methods Study area Beijing, the capital of China, was selected for this study primarily because of its large population and advanced
disease surveillance system. With a location of 40 N and 116 E, Beijing has a semi-humid continental monsoon climate and four distinct seasons: cold dry winter, hot rainy summer, and relatively short spring and autumn.10,11
Data collection Monthly influenza incidence from January 1970 to December 2004 were provided by the Beijing Center for Disease Control and Prevention. An analysis of Yunqi theory required us to take 39 years of meteorological data from January 1, 1966 to December 31, 2004. Data based on five meteorological variables were retrieved from the Beijing Meteorological Observatory, including daily averages of temperature, wind speed, relative humidity, vapor pressure and daily total precipitation.
Data pre-processing Today the seasons are divided into four periods, however, traditional Chinese calendar divided one year near equally into 24 solar terms and these fell into six near equal periods (with each four terms forming one period) based on Yunqi theory. The six periods are called six qi. The first period is qi Feng (wind) (from the Spring Beginning to the Vernal Equinox according to the lunar Chinese calendar, a period roughly corresponding to February and March); the 2nd qi Huo (heat) (from Fresh Green to Lesser Fullness, April and May); the 3rd qi Shu (summer heat) (from Grain in Ear to Greater Heat, June and July); the 4th qi Shi (wet) (from the beginning of Autumn to the Autumn Equinox, August and September); the 5th qi Zao (dry) (from Cold Dew to Light Snow, October and November); and the 6th qi Han (cold) (from Heavy Snow to Greater Cold, December and next January).12 It should be noted that, take the first qi as an example, the period from the Beginning of Spring to the Spring Equinox spans from the end of January to the end of March, but not exactly February and March. Yet owing to the limitations of the data available to us, we could only estimate that February and March is equivalent of the first qi period, and similar estimations also apply to other qi periods. Additionally, translation of the proper nouns regarding the Chinese lunar calendar and TCM terms in this paper are in accordance with standards regulated by China National Committee for Terms in Sciences and Technologies. Consequently, the incidence of influenza in Beijing for each qi period from 1970 to 2003 was calculated to establish a database. Similarly, another database was created with the average values of the five meteorological variables from 1966 to 2003. Due to the lack of influenza data in 2005, the data of the sixth period in 2004 could not be incorporated.
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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Statistical methods An artificial neural network (ANN) is a massively parallel distributed processor that simulates the biological neural networks of animal brains.13 ANN includes a large number of highly interconnected processing units (neurons) that work together to solve specific problems in the sciences. A back-propagation artificial neural network (BPANN) is currently a widely used ANN, which is a multi-layer feedforward neural network trained according to backpropagation algorithm. Training a BPANN consists of two steps. First, the input signal spreads across the network until the output layer is reached. Second, an error signal is computed by comparing the output of the network and the expectation. The error signal is transmitted again through the network layer by layer, but this time the propagation is in the opposite direction. In the second stages, the synaptic weights of the network are constantly revised. The two steps are repeated until the response of the network to the input reaches a predetermined target area. The advantages of BPANN include stronger adaptability, faster parallel processing and less experiential knowledge involvement, which provide a simple and effective method for solving complex problems.14 BPANN has been applied in various fields of medical research during the past decade.15,16 Descriptive statistical analysis was first conducted to demonstrate the general regularities of weather changes and influenza incidence in Beijing during the six qi phases over the past 35 years. Then, a BPANN was performed to examine effects of various meteorological factors on the outbreak of influenza. The modeling process of BPANN is as follows: meteorological factors were used as input variables and the predictive influenza incidence as output variables. All of the samples were divided into training samples (TRS) and test samples (TES) to prevent over fitting. An ideal network model could be achieved when the TES were less than the TRS. Consequently, the data for training and forecasting were randomly selected at the default ratio of 7:3, a ratio most commonly used in the statistical analysis.17 Six influenza prediction models were established based on data of five meteorological factors and influenza morbidity. Corresponding periods of climatic data and influenza data from 1970 to 2003 were adopted in the modelling. (1) Model One was based on the assumption that one years’ influenza incidence is largely influenced by the same year’s weather, and therefore the influenza data and climatic data used both ranged from 1970 to 2003. (2) For Model Two, climatic data used were one year prior to influenza data so as to examine whether certain previous year’s weather could also influence this year’s influenza morbidity. Thus the data on climatic factors were from 1969 to 2002. (3) Correspondingly Model Three climatic data were from 1968 to 2001, two years prior to influenza data. (4) Same as in Model Two but involved climatic data between 1967 and 2000, the climate data of Model Four were three years prior to influenza data. From Model One to Model Four, meteorological data of one single year was utilized while Model Five and Six involved multiple years of climatic data. (5) As far as Model Five is concerned, for each year’s influenza data, climatic data of the same year
3 plus its previous three years were all employed. For example, if influenza data from 1970 is used, four years of climatic data from 1967 to 1970 were all incorporated into the modeling process. The same applied to other years’ data. (6) Similarly, Model Six used climatic data of the previous four years’ plus the same year’. Next, we evaluated the forecasting accuracy of the above six models and selected the one with the highest accuracy. All analyses were conducted with SPSS version 17.0.
Results Descriptive analysis The average temperature, average precipitation, and average vapor pressure peaked in the 3rd qi period (June and July), and the lowest points of these variables were observed in the 6th qi (December and next January). The average relative humidity peaked in the 4th qi (August and September), followed by a secondary peak in the 3rd qi, and reached the lowest point in the 6th qi followed by the 1st qi (February and March). Additionally, the average wind speed peaked in the 2nd qi (April and May) and hit bottom in the 4th qi. As was manifested by the curves diagrams of the five basic meteorological factors, the 6th qi in Beijing that usually corresponds to December and January was dominated by extreme coldness and dryness (Fig. 1). Influenza incidence reported from 1970 to 2003 in Beijing was 359,374 and the average annual incidence was 10,570. The distribution of influenza cases exhibited a considerable intra-annual variability and marked seasonality. The 6th qi associated with 69% of the annual influenza incidence accounts for the largest proportion. Among the 1762 average influenza cases in all the six qi periods, the counts in the 6th qi was 4.12-fold that of the average (Fig. 2).
Influenza-meteorological forecast model based on back propagation artificial neural network Meteorological data used were of the same year with influenza data in Model One Samples throughout the 35 years fell into the category of TRS and TES, and the TRS comprised 74.0% of all samples while the TES accounted for the rest 26.0%. The training relative error (TRRE) value was 0.871, and test relative error (TERE) value was 0.711. Both of the TRRE and TERE values were less than 1, which indicated Model One was successfully built up (the criteria for the success of model establishment is the same below). Among the meteorological factors, average temperature has the most significant impact on influenza, followed by average wind speed (an impact degree of over 50% was considered to be significant) (Table 1). Meteorological data one year prior to influenza data in Model Two The TRS and the TES comprised 71.1% and 28.9% of all samples, respectively. Because the values of TRRE and TERE were 0.849 and 0.984, respectively, Model Two again
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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Figure 1 Distributions of the daily averages of five meteorological factors in the six phases from 1967 to 2003. Note (A) average temperature, (B) average wind speed, (C) average precipitation, (D) average relative humidity, (E) average vapor pressure.
Figure 2
Distribution of influenza incidence in six qi in Beijing from 1970 to 2003.
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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5
Contributions of the meteorological factors in Model One to Four.
Meteorological factors
Model One
Model Two
Model Three
Model Four
Average Average Average Average Average
100.0 74.9 30.9 11.0 5.7
100.0 63.2 60.6 37.5 14.2
99.9 36.4 61.2 27.2 100.0
100.0 49.2 70.4 35.8 64.6
temperature wind speed relative humidity precipitation vapor pressure
was successfully established. Among the significant meteorological factors, their contribution to influenza incidence rank (from the greatest to least) as: average temperature, average wind speed and average relative humidity (Table 1). Meteorological data two years prior to influenza data in Model Three The percentage of the TRS was 64.2%, and the TES 35.8%. The values of TRRE and TERE were 0.873 and 0.773 respectively, indicating the successful establishment of Model Three. Significant meteorological factors in descending order were: average vapor pressure, average temperature and average relative humidity, and the first two factors have the same contribution (Table 1). Meteorological data three years prior to influenza data in Model Four The percentage of TRS and of TES was 71.1% and 28.9%, respectively. Because the TRRE value was 0.789, and the TERE value 0.869, Model Four was also successful. Significant meteorological factors in descending order were: average temperature, average relative humidity and average vapor pressure (Table 1). The latest four years of meteorological data in Model Five Given that the above four models were all successfully built, multiple years of climatic data were incorporated together to establish a predictive model: for each year’s influenza data, climatic data of the same year and of its previous three years were all considered. For this new model, the percentage of the TRS was 72.5%, and of the TES 27.5%. The value of TRRE was 0.456, and that of the TERE was 0.570. Obviously, Model Five was successfully built up. It should be noted that the relative error values in Model Five were significantly lower than those in the previous four models. Among the significant meteorological factors, average relative humidity two years ago contributed the most to the incidence of influenza, followed by average wind speed three years ago, and average wind speed one year ago (Table 2). The latest five years of meteorological data in Model Six Similar to Model Five, for influenza data of each year, its previous four years’ and the same year’s climatic data were all utilized to establish a new predictive model. For Model Six, the percentage of TRS was 70.1%, and that of TES 29.9%. The TRRE value was 0.783, and the TERE value 0.858. Although Model Six was also successfully established with the two values less than 1, both values were remarkably higher than those in Model Five. Thus we neglected the
meteorological factors in the even more previous years. The “S. huayi” theory therefore was proved to be reasonable. Comparison of forecasting accuracy in the six models The first four models illustrated above used the data of influenza morbidity and climatic factors of one single year, while Model Five used four years of climatic data. Much improvement was observed in Model Five, as the prediction accuracy nearly doubled or tripled that of the previous four models. Yet the accuracy tended to decrease when more climatic data of previous year’s was integrated (Table 3).
Discussion Based upon the theory of Yunqi this retrospective study examined correlations between influenza incidence and meteorological factors in Beijing. By performing BPANN, six forecasting models were established with meteorological data varying from one single year to multiple years. Then significant meteorological factors were identified and a forecasting model was determined.
Table 2 Contributions of the meteorological factors in Model Five. Time Current year
Meteorological factors
Average Average Average Average Average One year ago Average Average Average Average Average Two years ago Average Average Average Average Average Three years ago Average Average Average Average Average
vapor pressure relative humidity wind speed temperature precipitation wind speed vapor pressure relative humidity precipitation temperature relative humidity wind speed vapor pressure precipitation temperature wind speed temperature precipitation relative humidity vapor pressure
Contributions (%) 42.8 38.9 30.7 27.8 22.9 54.4 35.9 35.4 33.9 19.5 100.0 76.4 39.9 33.7 23.4 57.8 48.0 37.1 24.7 19.3
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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H. Wang et al. Table 3
The results of the influenza-meteorological forecast models.
Model
Relative error (Training)
Relative error (Test)
Prediction accuracy (total)
One (Current year) Two (1 year ago) Three (2 years ago) Four (3 years ago) Five (4 years in total) Six (5 years in total)
0.871 0.849 0.873 0.789 0.456 0.783
0.711 0.984 0.773 0.869 0.570 0.858
21% 9% 18% 17% 49% 18%
Unlike most previous studies based on monthly climatic data, our study made use of data calculated by six qi theory.18 According to six qi theory one year is near equally divided into 24 solar terms under Chinese calendar. One qi phase is comprised of four successive solar terms. Another feature of this study was its focus on the comparatively long-term impact of meteorological variables. To our knowledge, such analyses were relatively rare. The “S. huayi”19e22 doctrine was initially raised in Yellow Emperor’s Canon of Medicine$Plain Conversation. This work contributed observations on how abrupt climatic changes or unusual weather conditions during the past three years exerted influences on the present incidence of infectious diseases. Our consideration of six different models dealing with climatic data of different years was also inspired by this doctrine. Our success in establishing the six models in turn can be aligned with those outlined in the Yellow Emperor’s Canon of Medicine. With the forecast accuracy of the TRS and TES both approximating 49%, Model Five exceeded all the rest five models. This finding further illustrated that forecast accuracy might be improved when four years’ climatic data were all incorporated in modeling while using one single year’s data or adding data of the even previous years might not be as accurate. When it comes to the most important meteorological factors, temperature is worth a mention. Among the six models we established, temperature was proved to be the most significant factor in three models and ranked the second in one model. The important contribution of temperature to the occurrence of influenza is also consistent with previous studies. Biological evidence includes higher probability of influenza virus to enter the respiratory tract when nasal cilia’s movement slow down at low temperature.23,24 Yet as the model with the best prediction accuracy, Model Five suggested otherwise. Other than temperature, both average relative humidity and average wind speed two years ago were selected as the most significant meteorological factors. Other local climatic changes apart from temperature should also be accounted for and this observation also coincides with the past findings on infectious diseases.25e27 The innovations of this study are summarized as follows. First, based on the theory of Yunqi, six qi doctrine was used to describe the seasonal pattern of the meteorological variables and influenza incidence. Moreover, we concentrated on how climatic changes during as long as the past three years exert influences on the present incidence of influenza, which might contribute to timely forecasts on the ground of early warning models. Additionally, BPANN was introduced to select the most significant weather
indicators and to build up influenza-meteorological predictive models. The determination of the best model rests on the analysis and comparison among the established models. We integrated TCM theory with the analysis of meteorological data while other study purely adopted modern statistical method. Finally comes our successful confirmation of the Yunqi doctrine that, “the pestilence occurred three years after the abnormal climate changes.” Yet this study was not free from limitations. Influenza outbreaks are influenced by multifaceted determinants yet our study merely concentrated on meteorological variables. The role other relevant factors might play in influenza incidence remained less documented, such as economic development, improvements in health service, population growth, school schedules, air pollution, indoor airconditioner and winter heating systems. In addition, the sources of data were restricted to a single study area (Beijing) with limited sample size. Local climate conditions, demographic differences, and ecological characteristics might all affect the results to some degree. Probably more accurate results free of confounding factors can be obtained by comparison between different places, which is expected to be achieved in future studies. Another point is that our verification of the Yunqi theory had better be extended elsewhere in regions of different geographical and meteorological factors. It is hoped that future research will empower more public health sectors to translate such ancient theories.
Funding This work was granted by the National Natural Science Foundation of China (No. 81072896 and No. 81704198).
Competing interests The authors declare that they have no competing interests.
Authors’ contributions Hong Wang and Xuan Zhang wrote and revised the manuscript. Zhili Gao, Ling Han, Zhongdi Liu, Long Yan and Mingyue Li checked and analyzed the data. Juan He conceived and designed the experiments. All authors have read and approved the contents of the final version.
Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003
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Acknowledgment We would like to express our sincere gratitude to all the data producers: the Beijing Meteorological Observatory and the Beijing Center for Disease Control and Prevention.
Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jtcms.2018.06.003.
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Please cite this article in press as: Wang H, et al., Impact of meteorological factors on the incidence of influenza in Beijing: A 35-year retrospective study based on Yunqi theory, Journal of Traditional Chinese Medical Sciences (2018), https://doi.org/10.1016/ j.jtcms.2018.06.003