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  • Xu, Chunjie  (2)
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  • 1
    Online Resource
    Online Resource
    The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018
    Springer Science and Business Media LLC ; 2020
    In:  Scientific Reports Vol. 10, No. 1 ( 2020-10-14)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-10-14)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-020-74363-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2615211-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-11-01)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-11-01)
    Abstract: In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on the COVID-19 prevalence and mortality data from 28 February 2020 to 27 June 2020 in South Africa and Nigeria. By comparing the accuracy level of forecasting measurements with the basic ARIMA and NARANN models, it was shown that this novel data-driven hybrid model did a better job of capturing the dynamic changing trends of the target data than the others used in this work. Our proposed mixture technique can be deemed as a helpful policy-supportive tool to plan and provide medical supplies effectively. The overall confirmed cases and deaths were estimated to reach around 176,570 [95% uncertainty level (UL) 173,607 to 178,476] and 3454 (95% UL 3384 to 3487), respectively, in South Africa, along with 32,136 (95% UL 31,568 to 32,641) and 788 (95% UL 775 to 804) in Nigeria on 12 July 2020 using this data-driven EEMD-ARIMA-NARANN hybrid technique. The contributions of this study include three aspects. First, the proposed hybrid model can better capture the dynamic dependency characteristics compared with the individual models. Second, this new data-driven hybrid model is constructed in a more reasonable way relative to the traditional mixture model. Third, this proposed model may be generalized to estimate the epidemic patterns of COVID-19 in other regions.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-00948-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
    Location Call Number Limitation Availability
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    Online Resource
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