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Food Sources May Affect the Symptom Rates of COVID-19, an Epidemiological Analysis Based on the Public Data in Gansu Province, China, During the Summer Epidemic Cycle in 2022

Published online by Cambridge University Press:  11 April 2023

Rui Xu*
Affiliation:
School of Nursing, Gansu University of Chinese Medicine, Lanzhou, P. R. China
Jin-Peng Hu
Affiliation:
Center for Grassland Microbiome, Lanzhou University, Lanzhou, P. R. China
Li-Li Chen
Affiliation:
Cancer Epidemiology Research Center, Gansu Provincial Cancer Hospital, Lanzhou, P. R. China
Jun-Fang Miao
Affiliation:
School of Nursing, Gansu University of Chinese Medicine, Lanzhou, P. R. China
Qiang Wang
Affiliation:
School of Basic Medicine, Gansu University of Chinese Medicine, Lanzhou, P. R. China
Ji-Jun Hu
Affiliation:
Affiliated Hospital of Gansu Medical College, Pingliang, P. R. China
Xu-Hong Chang
Affiliation:
School of Public Health, Lanzhou University, Lanzhou, P. R. China
Jin-Lin Zhang*
Affiliation:
Center for Grassland Microbiome, Lanzhou University, Lanzhou, P. R. China
*
Corresponding authors: Rui Xu, Email: 39435785@qq.com; Jin-Lin Zhang, Email: jlzhang@lzu.edu.cn.
Corresponding authors: Rui Xu, Email: 39435785@qq.com; Jin-Lin Zhang, Email: jlzhang@lzu.edu.cn.

Abstract

According to the public data collected from the Health Commission of Gansu Province, China, regarding the COVID-19 pandemic during the summer epidemic cycle in 2022, the epidemiological analysis showed that the pandemic spread stability and the symptom rate (the number of confirmed cases divided by the sum of the number of asymptomatic cases and the number of confirmed cases) of COVID-19 were different among 3 main epidemic regions, Lanzhou, Linxia, and Gannan; both the symptom rate and the daily instantaneous symptom rate (daily number of confirmed cases divided by the sum of daily number of asymptomatic cases and daily number of confirmed cases) in Lanzhou were substantially higher than those in Linxia and Gannan. The difference in the food sources due to the high difference of the population ethnic composition in the 3 regions was probably the main driver for the difference of the symptom rates among the 3 regions. This work provides potential values for prevention and control of COVID-19 in different regions.

Type
Brief Report
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

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References

Onyelowe, F, Onyelowe, K. Epidemiological analysis and time prediction models of coronavirus (COVID-19/SARS-CoV-2) spread in selected epicentres around the world: Nigeria as a case study. Path Sci. 2020;6(7):4019-4033.Google Scholar
Sun, K, Chen, J, Viboud, C. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. Lancet Digit Health. 2020;2(4):e201-e208.CrossRefGoogle Scholar
Wang, C, Horby, PW, Hayden, FG, et al. A novel coronavirus outbreak of global health concern. Lancet. 2020;395(10223):470-473.Google ScholarPubMed
Qiu, JY, Shen, B, Zhao, M, et al. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations. Gen Psychiatr. 2020;33(2):e100213.CrossRefGoogle ScholarPubMed
worldometer. Covid-19 coronavirus pandemic. Accessed August 24, 2022. https://www.worldometers.info/coronavirus/ Google Scholar
Health Commission of Gansu Province. Accessed August 3, 2022. http://wsjk.gansu.gov.cn/wsjk/c112713/list.shtml Google Scholar
Zhou, L, Xu, C, Song, D. Research on government data opening pattern in major epidemic disasters–empirical analysis based on the COVID-19 data opening. J Mod Inf. 2020;40(6):3-18.Google Scholar
Zhou, N, Zhang, X, Zhang, Y, et al. Epidemiological analysis of coronavirus disease 2019 (COVID-19) in 2 cities in China based on public data. Disaster Med Public Health Prep. 2020;16(3):1156-1160.CrossRefGoogle ScholarPubMed
Kinoshita, R, Anzai, A, Jung, S-M, et al. Containment, contact tracing and asymptomatic transmission of novel coronavirus disease (COVID-19): a modelling study. J Clin Med. 2020;9(10):3125.CrossRefGoogle ScholarPubMed
Kaplan, AK, Sahin, MK, Parildar, H, et al. The willingness to accept the COVID-19 vaccine and affecting factors among healthcare professionals: a cross-sectional study in Turkey. Int J Clin Pract. 2021;75(7):e14226.CrossRefGoogle ScholarPubMed
Burke, PF, Masters, D, Massey, G. Enablers and barriers to COVID-19 vaccine uptake: an international study of perceptions and intentions. Vaccine. 2021;39(36):5116-5128.CrossRefGoogle Scholar
Zhu, X, Wang, X, Li, S, et al. Rapid, ultrasensitive, and highly specific diagnosis of COVID-19 by CRISPR-based detection. ACS Sens. 2021;6(3):881-888.CrossRefGoogle ScholarPubMed
Nguyen, LT, Rananaware, SR, Pizzano, BLM, et al. Clinical validation of engineered CRISPR/Cas12a for rapid SARS-CoV-2 detection. Commun Med (Lond). 2022;2(1):7.CrossRefGoogle ScholarPubMed
Tuladhar, R, Singh, A, Varma, A, et al. Climatic factors influencing dengue incidence in an epidemic area of Nepal. BMC Res Notes. 2019;12(1):131.CrossRefGoogle Scholar
Weaver, AK, Head, JR, Gould, CF, et al. Environmental Factors Influencing COVID-19 incidence and severity. Annu Rev Public Health. 2022;43:271-291.CrossRefGoogle ScholarPubMed
Lammi, C, Arnoldi, A. Food-derived antioxidants and COVID-19. J Food Biochem. 2021;45(1):e13557.CrossRefGoogle ScholarPubMed
Population ranking of prefecture-level cities nationwide. Accessed August 24, 2022. https://www.hongheiku.com/category/shijirenkou Google Scholar