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. 2022 Aug 9:12:881745.
doi: 10.3389/fcimb.2022.881745. eCollection 2022.

Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

Jing Chen  1   2 , Rui-Lian Ding  3 , Kang-Kang Liu  4 , Hui Xiao  2 , Gang Hu  5 , Xiang Xiao  6 , Qian Yue  2 , Jia-Hai Lu  7 , Yan Han  3 , Jin Bu  3 , Guang-Hui Dong  8 , Yu Lin  9   10
Affiliations

Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

Jing Chen et al. Front Cell Infect Microbiol. .

Abstract

Background: Dengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods.

Methods: The dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics. Spearman correlation analysis was used to analyze the correlation between lagged meteorological factors and dengue fever cases and determine the maximum lagged correlation coefficient of different meteorological factors. Then, Generalized Additive Models were used to analyze the non-linear influence of lagged meteorological factors on local dengue cases and to predict the number of local dengue cases under different weather conditions.

Results: We described the temporal and spatial distribution characteristics of dengue fever cases and found that sporadic single or a small number of imported cases had a very slight influence on the dengue epidemic around. We further created a forecast model based on the comprehensive consideration of influence of lagged 42-day meteorological factors on local dengue cases, and the results showed that the forecast model has a forecast effect of 98.8%, which was verified by the actual incidence of dengue from 2005 to 2016 in Guangzhou.

Conclusion: A forecast model for dengue epidemic was established with good forecast effects and may have a potential application in global dengue endemic areas after modification according to local meteorological conditions. High attention should be paid on sites with concentrated patients for the control of a dengue epidemic.

Keywords: Spearman correlation analysis; dengue fever; generalized additive models; meteorological parameter; spatial distribution; temporal characteristics.

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Conflict of interest statement

Author YL was employed by the company Guangzhou South China Biomedical Research Institute co., Ltd. and Shenzhen Withsum Technology Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Temporal and spatial distribution characteristics of dengue cases from 2005 to 2016. (A) Accumulative spatial distribution of dengue cases from 2005 to 2016 in Guangzhou. Each patient with dengue fever reported from 2005 to 2016 represents a point, and all of the points are represented on the map. Black dots represent weather stations providing weather data. (B) Time distribution of dengue cases from 2005 to 2016. The left ordinate shows the case numbers of dengue for all years except 2014, and the right ordinate shows the case numbers of dengue in 2014.
Figure 2
Figure 2
Temporal distribution characteristics of imported dengue cases from 2005 to 2016. (A) Time distribution of dengue imported cases. (B) Ratio of imported cases to total cases in each year.
Figure 3
Figure 3
The influence of imported cases on the epidemic of surrounding dengue. The imported case was set as the original point, and the high-density area for case distribution is 5,000 to 6,500 m from the imported case on distance and within 5 days on time of diagnosis of the imported case, with a potential dengue incidence of 0.0181%. The area on the distance within 1,000 m and on time longer than 15 days shows a low-density area of cases reported, with a potential dengue incidence of only 0.0021%.
Figure 4
Figure 4
Changes in meteorological factors from 2005 to 2016. (A) Daily average temperature. (B) Relative humidity. (C) Accumulated precipitation. (D) Average daily wind.
Figure 5
Figure 5
Changes in correlation coefficients between the number of people with dengue on different lag days and meteorological factor. The number of local cases is related to the daily average temperature (A), relative humidity (B), accumulated precipitation (C), and average daily wind speed lagging 1–168 days (D) as well as the cumulative number of local cases in the previous 1–168 days (E) and the cumulative number of input cases in the previous 1–168 days (F).
Figure 6
Figure 6
Verification of predictive results and actual incidence of local dengue fever from 2005 to 2016 in China. Red curves present the actual cases, and blue curves are the predicted curves created by Generalized Additive Models. (A) 2005. (B) 2006. (C) 2007. (D) 2008. (E) 2009. (F) 2010. (G) 2011. (H) 2012. (I) 2013. (J) 2014. (K) 2015. (L) 2016.
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