Mushroom poisoning outbreaks in Guizhou Province, China: a prediction study using SARIMA and Prophet models

Author:

Zhang Li,Chen Qing-yuan,Xiong Su-fang,Zhu Shu,Tian Ji-gui,Li Jun,Guo Hua

Abstract

AbstractMushroom poisoning is a public health concern worldwide that not only harms the physical and mental health of those who are poisoned but also increases the medical and financial burden on families and society. The present study aimed to describe and analyze the current situations and factors influencing mushroom poisoning outbreaks in Guizhou province, Southwest China, between January 2012 and June 2022, and to predict the future trends of its occurrence. Our study provides a basis for the rational formulation of prevention and control and medical resource allocation policies for mushroom poisoning. The epidemiological characteristics and factors influencing mushroom poisoning incidence were analyzed using descriptive epidemiological methods and the chi-squared test, respectively. Then, future occurrence trends were predicted using the SARIMA and Prophet models. In total, 1577 mushroom poisoning incidents were recorded in Guizhou Province, with 7347 exposures, 5497 cases, 3654 hospitalizations, and 93 fatalities. The mortality rate was 4.45% in 1 ~ 6 years higher than other age groups. There were notable geographic and seasonal characteristics, with the number of occurrences much higher in rural areas (1198) than in cities (379), and poisoning cases were more common during the rainy season (June to September). The mortality rate of household poisoning cases was 1.86%, with the most deaths occurring in households. Statistically significant differences were observed in the incidence across various cities, periods, and poisoning locations (P < 0.05). Both models had advantages and disadvantages for prediction. Nevertheless, the SARIMA model had better overall prediction results than the Prophet model (R > 0.9, the residual plot of the prediction results was randomly distributed, and RMSESARIMA < RMSEProphet). However, the prediction result plot of the Prophet model was more explanatory than the SARIMA model and could visualize overall and seasonal trends. Both models predicted that the prevalence of mushroom poisoning would continue to increase in the future; however, the number of fatalities is generally declining. Seasonal patterns indicated that a high number of deaths from gooseberry mushroom poisoning occurred in October. The epidemiological trends of mushroom poisoning remain severe, and health education on related knowledge must be strengthened in rural areas, with June to October as the key prevention and control phase. Further, medical treatment of mushroom poisoning cases with clinical symptoms should pay attention to inquiries to check whether the mushroom is similar in appearance to the Amanita, particularly in October.

Funder

the National KPlaney R & D

the Guizhou Science and Technology Cooperation Support

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference34 articles.

1. Cao, Z. B. Circular of the National Health and Wellness Commission on the Issuance of the Code of Practice for Monitoring and Reporting of Foodborne Diseases (for Trial Implementation). https://www.gov.cn/xinwen/2019-10/22/content_5443246.htm.

2. White, J. et al. Mushroom poisoning: A proposed new clinical classification. Toxicon 157, 53–65 (2019).

3. Chan, C. et al. Mushroom poisoning in Hong Kong: A ten-year review. Toxicon 22(2), 124 (2016).

4. Chen, L. et al. Epidemiological analysis of wild mushroom poisoning in Zhejiang province, China, 2016–2018. Food Sci. Nutr. 10(1), 60–66 (2022).

5. Wennig, R. et al. Mushroom poisoning. Deutsch. Ärztebl. Int. 117(42), 701–708 (2020).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3