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Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012)
OBJECTIVES: Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552134/ https://www.ncbi.nlm.nih.gov/pubmed/28796834 http://dx.doi.org/10.1371/journal.pone.0182937 |
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author | Yan, Long Wang, Hong Zhang, Xuan Li, Ming-Yue He, Juan |
author_facet | Yan, Long Wang, Hong Zhang, Xuan Li, Ming-Yue He, Juan |
author_sort | Yan, Long |
collection | PubMed |
description | OBJECTIVES: Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and to establish an effective forecasting model. METHODS: A time series analysis was conducted in the Beijing area based upon monthly data on weather variables (i.e. temperature, rainfall, relative humidity, vapor pressure, and wind speed) and on the number of BD cases during the period 1970–2012. Autoregressive integrated moving average models with explanatory variables (ARIMAX) were built based on the data from 1970 to 2004. Prediction of monthly BD cases from 2005 to 2012 was made using the established models. The prediction accuracy was evaluated by the mean square error (MSE). RESULTS: Firstly, temperature with 2-month and 7-month lags and rainfall with 12-month lag were found positively correlated with the number of BD cases in Beijing. Secondly, ARIMAX model with covariates of temperature with 7-month lag (β = 0.021, 95% confidence interval(CI): 0.004–0.038) and rainfall with 12-month lag (β = 0.023, 95% CI: 0.009–0.037) displayed the highest prediction accuracy. CONCLUSIONS: The ARIMAX model developed in this study showed an accurate goodness of fit and precise prediction accuracy in the short term, which would be beneficial for government departments to take early public health measures to prevent and control possible BD popularity. |
format | Online Article Text |
id | pubmed-5552134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-55521342017-08-25 Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012) Yan, Long Wang, Hong Zhang, Xuan Li, Ming-Yue He, Juan PLoS One Research Article OBJECTIVES: Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and to establish an effective forecasting model. METHODS: A time series analysis was conducted in the Beijing area based upon monthly data on weather variables (i.e. temperature, rainfall, relative humidity, vapor pressure, and wind speed) and on the number of BD cases during the period 1970–2012. Autoregressive integrated moving average models with explanatory variables (ARIMAX) were built based on the data from 1970 to 2004. Prediction of monthly BD cases from 2005 to 2012 was made using the established models. The prediction accuracy was evaluated by the mean square error (MSE). RESULTS: Firstly, temperature with 2-month and 7-month lags and rainfall with 12-month lag were found positively correlated with the number of BD cases in Beijing. Secondly, ARIMAX model with covariates of temperature with 7-month lag (β = 0.021, 95% confidence interval(CI): 0.004–0.038) and rainfall with 12-month lag (β = 0.023, 95% CI: 0.009–0.037) displayed the highest prediction accuracy. CONCLUSIONS: The ARIMAX model developed in this study showed an accurate goodness of fit and precise prediction accuracy in the short term, which would be beneficial for government departments to take early public health measures to prevent and control possible BD popularity. Public Library of Science 2017-08-10 /pmc/articles/PMC5552134/ /pubmed/28796834 http://dx.doi.org/10.1371/journal.pone.0182937 Text en © 2017 Yan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Yan, Long Wang, Hong Zhang, Xuan Li, Ming-Yue He, Juan Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012) |
title | Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012) |
title_full | Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012) |
title_fullStr | Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012) |
title_full_unstemmed | Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012) |
title_short | Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012) |
title_sort | impact of meteorological factors on the incidence of bacillary dysentery in beijing, china: a time series analysis (1970-2012) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552134/ https://www.ncbi.nlm.nih.gov/pubmed/28796834 http://dx.doi.org/10.1371/journal.pone.0182937 |
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