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. 2022 May 29;19(11):6625.
doi: 10.3390/ijerph19116625.

Association between Meteorological Factors and Mumps and Models for Prediction in Chongqing, China

Hong Zhang  1 , Kun Su  1   2   3 , Xiaoni Zhong  1
Affiliations

Association between Meteorological Factors and Mumps and Models for Prediction in Chongqing, China

Hong Zhang et al. Int J Environ Res Public Health. .

Abstract

(1) Background: To explore whether meteorological factors have an impact on the prevalence of mumps, and to make a short−term prediction of the case number of mumps in Chongqing. (2) Methods: K−means clustering algorithm was used to divide the monthly mumps cases of each year into the high and low case number clusters, and Student t−test was applied for difference analysis. The cross−correlation function (CCF) was used to evaluate the correlation between the meteorological factors and mumps, and an ARIMAX model was constructed by additionally incorporating meteorological factors as exogenous variables in the ARIMA model, and a short−term prediction was conducted for mumps in Chongqing, evaluated by MAE, RMSE. (3) Results: All the meteorological factors were significantly different (p < 0.05), except for the relative humidity between the high and low case number clusters. The CCF and ARIMAX model showed that monthly precipitation, temperature, relative humidity and wind velocity were associated with mumps, and there were significant lag effects. The ARIMAX model could accurately predict mumps in the short term, and the prediction errors (MAE, RMSE) were lower than those of the ARIMA model. (4) Conclusions: Meteorological factors can affect the occurrence of mumps, and the ARIMAX model can effectively predict the incidence trend of mumps in Chongqing, which can provide an early warning for relevant departments.

Keywords: ARIMA model; multivariate time series analysis; mumps.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Monthly cases of mumps and clusters from 2009 to 2019 in Chongqing.
Figure 2
Figure 2
Sequence diagram of mumps: (a) Reported monthly mumps cases from January 2009 to December 2019; (b) monthly mumps cases after difference.
Figure 3
Figure 3
ACF and PACF plots of mumps and difference mumps sequences.
Figure 4
Figure 4
Cross–correlation between mumps and meteorological factors.
Figure 5
Figure 5
Prediction diagram of ARIMA and ARIMAX.
See this image and copyright information in PMC

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