Using big data to predict pertussis infections in Jinan city, China: a time series analysis

Zhang, Yuzhou; Bambrick, Hilary; Mengersen, Kerrie; Tong, Shilu; Feng, Lei; Zhang, Li; Liu, Guifang; Xu, Aiqiang

doi:10.1007/s00484-019-01796-w

Cited by 23 publications

(26 citation statements)

References 55 publications

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“…Similarly, in the present work, we found that the monthly average temperature is positively correlated with the number of pertussis cases, which can also be used to explain the peak phenomenon of pertussis incidence observed in hot weather in our study and previous other studies 53,66 . About the positive correlation between them, this is congruous with the recent findings from the studies in Jinan 14,26 and Auckland 67 , which found that temperature was positively associated with pertussis among different age groups and could be considered as a good predictor. Also, in line with the findings that have been observed in other infectious diseases, such as HFMD (in Guangdong) 68 , bacillary dysentery (in Hunan Province) 31 , and scarlet fever (in Hefei City) 69 .…”

Section: Discussionsupporting

confidence: 70%

“…Importantly, several epidemiological studies have suggested that the morbidity of pertussis may be seriously underreported to a large extent in China 11 – 13 . Furthermore, escalating work is showing that there is a tendency to continue to increase in the incidence of pertussis in China 1 , 3 , 14 . However, little is so far known regarding the causes of this vaccine-preventable disease in its growing numbers.…”

Section: Introductionmentioning

confidence: 99%

“…Blackwood et al reported that the rainy season may play an important role in driving the seasonal pattern of pertussis transmission 25 . Zhang et al found that mean temperature and relative humidity may increase the incidence risk of pertussis among different age groups 14,26 . However, these researches only assessed the influences of one or two meteorological parameters on pertussis.…”

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confidence: 99%

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The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

Wang

Ren

et al. 2020

Sci Rep

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Evidence on the long-term influence of climatic variables on pertussis is limited. This study aims to explore the long-term quantitative relationship between weather variability and pertussis. Data on the monthly number of pertussis cases and weather parameters in Chongqing in the period of 2004–2018 were collected. Then, we used a negative binomial multivariable regression model and cointegration testing to examine the association of variations in monthly meteorological parameters and pertussis. Descriptive statistics exhibited that the pertussis incidence rose from 0.251 per 100,000 people in 2004 to 3.661 per 100,000 persons in 2018, and pertussis was a seasonal illness, peaked in spring and summer. The results from the regression model that allowed for the long-term trends, seasonality, autoregression, and delayed effects after correcting for overdispersion showed that a 1 hPa increment in the delayed one-month air pressure contributed to a 3.559% (95% CI 0.746–6.293%) reduction in the monthly number of pertussis cases; a 10 mm increment in the monthly aggregate precipitation, a 1 °C increment in the monthly average temperature, and a 1 m/s increment in the monthly average wind velocity resulted in 3.641% (95% CI 0.960–6.330%), 19.496% (95% CI 2.368–39.490%), and 3.812 (95% CI 1.243–11.690)-fold increases in the monthly number of pertussis cases, respectively. The roles of the mentioned weather parameters in the transmission of pertussis were also evidenced by a sensitivity analysis. The cointegration testing suggested a significant value among variables. Climatic factors, particularly monthly temperature, precipitation, air pressure, and wind velocity, play a role in the transmission of pertussis. This finding will be of great help in understanding the epidemic trends of pertussis in the future, and weather variability should be taken into account in the prevention and control of pertussis.

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Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

Wang

Ren

et al. 2020

Sci Rep

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“…To assist in statistical analysis, the software SPSS (Statistical Package for the Social Sciences), validated by different authors (Bragazzi et al 2017 ; Siriyasatien et al 2018 ; Gianfredi et al 2018 ; Li et al 2019 ; Zhang et al 2020 ) is largely used.…”

Section: Resultsmentioning

confidence: 99%

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Corsi

Souza

Pagani

et al. 2020

J Ambient Intell Human Comput

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Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

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“…In recent decades, coinciding with increasingly rapid advances in the technology of computers, the use of statistical techniques for modelling and forecasting has become widespread. Of these statistical techniques, the autoregressive integrated moving average (ARIMA) method based on an assumption of linearity is currently the most extensively applied to analyze and evaluate the morbidity or mortality time series of contagious diseases, such as TB, 7 scarlet fever, 8 human brucellosis, 9 pertussis, 10 etc. Yet the incidence series of contagious diseases over time includes not only linearity but also nonlinearity due to their secular trend, cyclic pattern, seasonality and stochastic fluctuation, and therefore may result in a limited ability to extract the nonlinear clues using the ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

<p>An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China</p>

Wang¹,

Xu²,

Yuchun³

et al. 2020

IDR

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Qinghai province has invariably been under an ongoing threat of tuberculosis (TB), which has not only been an obstacle to local development but also hampers the prevention and control process for ending the TB epidemic. Forecasting for future epidemics will serve as the base for early detection and planning resource requirements. Here, we aim to develop an advanced detection technique driven by the recent TB incidence series, by fusing a seasonal autoregressive integrated moving average (SARIMA) with a neural network nonlinear autoregression (NNNAR). Methods: We collected the TB incidence data between January 2004 and December 2016. Subsequently, the subsamples from January 2004 to December 2015 were employed to measure the efficiency of the single SARIMA, NNNAR, and hybrid SARIMA-NNNAR approaches, whereas the hold-out subsamples were used to test their predictive performances. We finally selected the best-performing technique by considering minimum metrics including the mean absolute error, root-mean-squared error, mean absolute percentage error and mean error rate. Results: During 2004-2016, the reported TB cases totaled 71,080 resulting in the morbidity of 97.624 per 100,000 persons annually in Qinghai province and showed notable peak activities in late winter and early spring. Moreover, the TB incidence rate was surging by 5% per year. According to the above-mentioned criteria, the best-fitting basic and hybrid techniques consisted of SARIMA(2,0,2)(1,1,0) 12 , NNNAR(7,1,4) 12 and SARIMA(2,0,2)(1,1,0) 12-NNNAR(3,1,7) 12 , respectively. Amongst them, the hybrid technique showed superiority in both mimic and predictive parts, with the lowest values of the measured metrics in both the parts. The sensitivity analysis indicated the same results. Conclusion: The best-mimicking SARIMA-NNNAR hybrid model outperforms the bestsimulating basic SARIMA and NNNAR models, and has a potential application in forecasting and assessing the TB epidemic trends in Qinghai. Furthermore, faced with the major challenge of the ongoing upsurge in TB incidence in Qinghai, there is an urgent need for formulating specific preventive and control measures.

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Using big data to predict pertussis infections in Jinan city, China: a time series analysis

Cited by 23 publications

References 55 publications

The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

Big data analytics as a tool for fighting pandemics: a systematic review of literature

<p>An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China</p>

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