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媒体报道对 COVID-19 疫情早期传播的影响。

Impact of media reports on the early spread of COVID-19 epidemic.

机构信息

School of Science, Chang'an University, Xi'an 710064, PR China.

College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China.

出版信息

J Theor Biol. 2020 Oct 7;502:110385. doi: 10.1016/j.jtbi.2020.110385. Epub 2020 Jun 25.

DOI:10.1016/j.jtbi.2020.110385
PMID:32593679
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7316072/
Abstract

Media reports can modify people's knowledge of emerging infectious diseases, and thus changing the public attitudes and behaviors. However, how the media reports affect the development of COVID-19 epidemic is a key public health issue. Here the Pearson correlation and cross-correlation analyses are conducted to find the statistically significant correlations between the number of new hospital notifications for COVID-19 and the number of daily news items for twelve major websites in China from January 11th to February 6th 2020. To examine the implication for transmission dynamics of these correlations, we proposed a novel model, which embeds the function of individual behaviour change (media impact) into the intensity of infection. The nonlinear least squares estimation is used to identify the best-fit parameter values in the model from the observed data. To determine impact of key parameters with media impact and control measures for the later outcome of the outbreak, we also carried out the uncertainty and sensitivity analyses. These findings confirm the importance of the responses of individuals to the media reports, and the crucial role of experts and governments in promoting the public under self-quarantine. Therefore, for mitigating epidemic COVID-19, the media publicity should be focused on how to guide people's behavioral changes by experts, and the management departments and designated hospitals of the COVID-19 should take effective quarantined measures, which are critical for the control of the disease.

摘要

媒体报道可以改变人们对新发传染病的认识,从而改变公众的态度和行为。然而,媒体报道如何影响 COVID-19 疫情的发展是一个关键的公共卫生问题。在这里,我们进行了皮尔逊相关和交叉相关分析,以找到 2020 年 1 月 11 日至 2 月 6 日期间中国 12 家主要网站每日新闻报道数量与 COVID-19 新增住院通知数量之间具有统计学意义的相关性。为了检验这些相关性对传播动力学的影响,我们提出了一个新的模型,该模型将个体行为变化(媒体影响)的功能嵌入到感染强度中。我们采用非线性最小二乘法从观测数据中确定模型中最佳拟合参数值。为了确定具有媒体影响和控制措施的关键参数对疫情后期结果的影响,我们还进行了不确定性和敏感性分析。这些发现证实了个体对媒体报道的反应的重要性,以及专家和政府在促进公众自我隔离方面的重要作用。因此,为了减轻 COVID-19 疫情,媒体宣传应侧重于专家如何引导人们的行为变化,COVID-19 的管理部门和指定医院应采取有效的隔离措施,这对疾病的控制至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/f6221063e487/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/7a7368371239/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/f8ab27e91fcb/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/13e3dd819482/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/ed11899c9772/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/f40d425e8250/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/ae8e9b5d08cf/gr6_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/358f40d25c77/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/927891599b16/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/b5c1877623ed/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/f6221063e487/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/7a7368371239/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/f8ab27e91fcb/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/13e3dd819482/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/ed11899c9772/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/f40d425e8250/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/ae8e9b5d08cf/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/b2ac40ddfae0/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/358f40d25c77/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/927891599b16/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/b5c1877623ed/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f6/7316072/f6221063e487/gr11_lrg.jpg

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