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. 2023 Feb 28;20(5):4321.
doi: 10.3390/ijerph20054321.

Comprehensive Dynamic Influence of Multiple Meteorological Factors on the Detection Rate of Bacterial Foodborne Diseases under Spatio-Temporal Heterogeneity

Xiaojuan Qi  1 , Jingxian Guo  2   3 , Shenjun Yao  4 , Ting Liu  2 , Hao Hou  2 , Huan Ren  2   5
Affiliations

Comprehensive Dynamic Influence of Multiple Meteorological Factors on the Detection Rate of Bacterial Foodborne Diseases under Spatio-Temporal Heterogeneity

Xiaojuan Qi et al. Int J Environ Res Public Health. .

Abstract

Foodborne diseases are a critical public health problem worldwide and significantly impact human health, economic losses, and social dynamics. Understanding the dynamic relationship between the detection rate of bacterial foodborne diseases and a variety of meteorological factors is crucial for predicting outbreaks of bacterial foodborne diseases. This study analyzed the spatio-temporal patterns of vibriosis in Zhejiang Province from 2014 to 2018 at regional and weekly scales, investigating the dynamic effects of various meteorological factors. Vibriosis had a significant temporal and spatial pattern of aggregation, and a high incidence period occurred in the summer seasons from June to August. The detection rate of Vibrio parahaemolyticus in foodborne diseases was relatively high in the eastern coastal areas and northwestern Zhejiang Plain. Meteorological factors had lagging effects on the detection rate of V. parahaemolyticus (3 weeks for temperature, 8 weeks for relative humidity, 8 weeks for precipitation, and 2 weeks for sunlight hours), and the lag period varied in different spatial agglomeration regions. Therefore, disease control departments should launch vibriosis prevention and response programs that are two to eight weeks in advance of the current climate characteristics at different spatio-temporal clustering regions.

Keywords: bacterial foodborne diseases; meteorological factors; principal component analysis; spatio-temporal scanning statistics; vector autoregressive model.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Geographical location of the study area showing the distribution of meteorological stations and hospitals.
Figure 2
Figure 2
Methodological framework.
Figure 3
Figure 3
Time trend for V. parahaemolyticus infection and average daily precipitation, wind speed, relative humidity, temperature (mean, maximum, and minimum), and sunlight duration in the Zhejiang Province, 2014–2018.
Figure 4
Figure 4
Correlations between seven climatic factors and the weekly detection rate of V. parahaemolyticus in Zhejiang Province from 2014 to 2018. Lag number: number of weeks prior; ** p < 0.01.
Figure 5
Figure 5
Spatio-temporal clustering characteristics of the detection rate of V. parahaemolyticus.
Figure 6
Figure 6
Responses of the detection rate to a meteorological component shock in (a) the C1 cluster, (b) C2 cluster, (c) C3 cluster, and (d) Non_C region at given horizon h (h = 1, 2, … 20) with a confidence band. Notes: The forecast horizon [weeks] is given on the horizontal axis. The vertical axis shows the magnitude of the impulse response, and a negative value represents the reduction effect. Dashed lines represent a confidence interval of ±2 standard errors.
Figure 7
Figure 7
Time series of the weekly precipitation and detection rate of V. parahaemolyticus.
See this image and copyright information in PMC

References

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