key: cord-0690576-wp8bc0t0 authors: Yan, Qinling; Tang, Yingling; Yan, Dingding; Wang, Jiaying; Yang, Linqian; Yang, Xinpei; Tang, Sanyi title: Impact of media reports on the early spread of COVID-19 epidemic date: 2020-06-25 journal: J Theor Biol DOI: 10.1016/j.jtbi.2020.110385 sha: e0870a29e3968f2bc9a247c6bf29a9b536403968 doc_id: 690576 cord_uid: wp8bc0t0 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. However, how do media reports and individuals' behaviour changes affect the dynamical development of COVID-19 epidemic during the outbreak? Therefore, the main aim of this paper is to address the following issues: (1) Table ? ?. We also obtain the daily news items related to the COVID-19 epidemic from ). This is shown in Figure ? ?(A-C). Besides, we collect the rate of wearing masks from the totally 30 news videos relevant to the qq.com videos et al.) in Wuhan from January 11th to 23rd 2020. As Wuhan adopted the lock down policy on the 23rd, and stipulated that everyone must wear a mask when going out, therefore, we assume that the rate of wearing masks is 1 from January 24th to February 6th 2020, as shown in Figure ? ?(D). Figure ?? shows that the peak time of new hospital notifications is: (A) February 4th to 5th, (B) January 29th to 31st and February 2nd to 4th, (C) January 29th to 31st and February 4th to 6th, (D) January 31st and February 2nd to 5th. It is related to the control measures taken. Since Wuhan was locked down on January 23rd, and before that, more than 5 million people left Wuhan and went to all parts of the country. At this time, some of them may have been infected. After a incubation period of 3-10 days, they were diagnosed from January 29th to 31st. Therefore, cities in other provinces except Hubei first broke out from January 29th to 31st. This can be confirmed by individual detailed data released by MHC and media reports in various provinces. In addition, during this period, people in the incubation period will transmit the virus to close contacts, resulting in another small outbreak on February 2nd to 6th. It can be seen from Figure ? ? that the accumulated number of hospital notifications in most provinces shows an exponential growth pattern. Besides, the accumulated number of cured cases and death cases also show an exponential growth pattern in Hubei province and China, as shown in Figure ? ?. The rate of cured in non-Hubei regions higher than that of Hubei after February 3. Most of the deaths occurred in Hubei during the study. From These provinces where accumulated number of hospital notifications account for more than 5% of the total number of tracking close contacts are: Hubei (21.76%), Tianjin (13.19%), Guizhou (7.37%), Liaoning (5.50%), Jiangsu (5.18%). These results show that the average diagnosis rate in these provinces is relatively higher. Conversely, the provinces with smallest rate of that are: Xinjiang (1.63%), Guangxi (2.15%), Shaanxi (2.21%), Ningxia (2.37%), Gansu (2.75%), Hebei (2.89%), which indicate the control measures in these provinces are relatively more strict. Provinces with more than 50% of the total number of tracking close contacts removed are: Yunnan (80.19%), Shanxi (55.76%), Shandong (53.92%), Hainan (53.19%), and which means the epidemic situation in these provinces has been gradually alleviated. The statistical description for the collected data at the individual level is shown in Table ? ?. Considering the lack of data in the individual level, we analyze provinces with the missing data less than 30% in the following. For gender indicators, 18 regions are selected and the proportion of male in the known individuals is 53.63%. The statistical description of the proportion of male are: mean (52.34%), minimum In the following, y and y 1 denote the number of new hospital notifications in China and Hubei province, respectively. For the number of daily news items from each source, we use x 1 for news.cn, x 2 for sina.com, x 3 for cnr.cn, x 4 for youth.cn, x 5 for Chinanews.com, x 6 for gov.com, x 7 for 163.com, x 8 for people.com, x 9 for sohu.com, x 10 for globalhealth.net.cn, x 11 for China.com, x 12 for qq.com, respectively. To explore the relationships between the number of daily news items and the number of new hospital notifications for COVID-19 in China and Hubei province during the specified period, the Pearson correlation (??) and the cross-correlation analyses (??) methods are used. We conduct the Pearson correlation analysis to determine the association between the number of daily news items and the number of new hospital notifications from January 11th to February 6th 2020. These results are summarized in Table ? ?. We conclude that the number of daily news items from website of news.cn (x 1 ), cnr.cn (x 3 ), youth.cn (x 4 ), Chinanews.com (x 5 ), gov.com (x 6 ), 163.com(x 7 ) and people.com (x 8 ) are statistically significantly highly correlated with the number of new hospital notifications (y and y 1 ) over the study period according to the labeling systems roughly categorized (low or weak correlations (|γ| ≤ 0.35), moderate correlations (0.36 ≤ |γ| ≤ 0.67) and strong/high correlations (0.68 ≤ |γ| ≤ 1.0)(?)). And the number of daily news items of sina.com (x 2 ), sohu.com (x 9 ) and globalhealth.net.cn (x 10 ) are moderately correlated with y and y 1 . Among these seven popular websites, the number of daily news items of news.cn is most closely correlated with the number of new hospital notifications (r x 1 y = 0.974, r x 1 y 1 = 0.949, p < 0.01), while the correlation for globalhealth.net.cn is relatively weak (r x 10 y = 0.562, r x 10 y 1 = 0.509, Besides, Table ? ? also shows that most of these websites have statistically significant high or moderate correlations in terms of reporting the COVID-19 infection dynamics. In particular, we notice that x 1 are highly correlated to x 2 , x 3 , x 4 , x 5 , x 6 , x 7 , x 8 and x 9 , while x 10 is moderately correlated to x 1 . We also notice the high or moderate correlation pairs except for (x 1 , x 12 ), (x 3 , x 12 ), (x 4 , x 12 ), (x 5 , x 11 ), (x 5 , x 12 ), (x 6 , x 11 ), (x 6 , x 12 ) and (x 7 , x 12 ). To identify time lags between the daily reported news and the daily hospital notifications, cross-correlation analysis is also necessary despite Pearson correlation analysis can reveal the statistically significant correlation between them. Cross-correlation is a spectral analysis technique that can be used to provide qualitative insights on the casual temporal interaction between the number of daily The results are summarized in Figure ? ? and Figure ? ?. We observe that there are statistically significant cross-correlation between the number of daily news items at the sites x 1 , x 2 , x 3 , x 4 , x 5 , x 6 , x 7 , x 8 , x 9 , x 10 and the number of new hospital notifications y at lags ranging from -5 to 5 days, -3 to 8 days, -4 to 6 days, -3 to 6 days, -5 to 5 days, -4 to 6 days, -3 to 6 days, -3 to 5 days, -1 to 8 days, -1 to 6 days, respectively (see Figure ? ? and Figure ? ?). Therefore, there exists a feedback relationship (for k ̸ = 0) and a contemporaneous relationship (for k = 0) between the number of daily news items at We now incorporate media impact into the general SEIR-type (susceptible-exposedinfective -recovery) epidemiological model, which incorporates appropriate compartments relevant to interventions such as quarantine, isolation and treatment (??). We stratify the susceptible (S(t)), exposed (E(t)), infectious but not yet symptomatic (asymptomatic) (A), infectious with symptoms (I), hospitalized (H) and recovered (R), the quarantined susceptible (S q ), isolated exposed (I q ), and isolated infected (I q ) compartments in the general SEIR-type model (donated by SEIRDAH model). Due to media reports lead to individual behavior changes, and based on the model selection results of literature (?), the model with rate of behaviour change depicted by an exponential function could fit the observed data best. Therefore, the function of the rate of wearing masks (e −kp(t) ) is used to describe the impact of individual behavior changes (media) on the COVID-19 epidemic (The rate of wearing masks, p(t), is used to represent the collected data of the wearing masks rate from January 11th to February 6th 2020, as shown in Figure ??(D) ). Let the individuals expose to the virus is quarantined with a proportion q by contact tracing. If the quarantined individuals are effectively infected, they will move to E q compartment, S q compartment otherwise. The individuals who exposed to the virus are missed from the contact tracing with rate 1 − q, can either move to the compartment E or still stay in compartment S, depending on whether they are effectively infected or not. We assume that the base transmission probability is β and the contact rate is constant c. Besides, the impact of individual behavior changes (media) on the COVID-19 epidemic (e −kp(t) ) is embed into the transmission probability. Then, the quarantined individuals, if infected (or uninfected), move to the compartment E q (or S q ) at the rate of βe −kp(t) cq (or (1 − βe −kp(t) )cq). For those who are not quarantined, they will move to the compartment E at the rate of The infected individuals can be detected and, then, isolated at a rate of δ I and can also move to the compartment R at the rate of γ I due to recovery. The infectious with symptoms individuals I and the quarantined infected individuals H all move to the compartment D at the rate of α. And also the asymptomatic individuals are driving the tracking quarantine(?). All of these leads to the following SEIRDAH model: (4.1) The diagram of the model (??) are showed in Figure ? ?. The more detailed definitions of variables and parameters for model (??) are provided in Table ? ?. The population of Wuhan is around 11081000 inhabitants (?), hence, we set To do so, we utilized the nonlinear least-square (NLES) method in Matlab to fit the aforementioned real data sets which correspond to the model solution time series, i.e. C(t) and D(t), where C(t) follows the dC(t)/dt = δ I I(t) + δ q E q (t), as shown in Figure ? ?. The estimated parameter values are listed in Table ? ?. Besides, we achieve the mean values and standard deviations of the unknown parameters by stochastic simulation technique. Predictions of model (??) for the accumulated number of infected cases and death cases from January 24th to February 6th 2020 for different behaviour change constant are shown in Figure ? ?(A) and (B). From the observed data, we note that the epidemic of COVID-19 is becoming more and more serious, and a more reasonable explanation is that, during the Spring Festival, people returning home had led to the spread of the epidemic from Wuhan (urban concentrations) to vast provinces/provincial municipalities. However, if people kept their behaviour as it was over a period of time, i.e., the behaviour change constant from January 24th to February 6th is k = 5.0147 × 10 −13 , very small and almost zero, which will lead to a significant increase in the and deaths cases with quarantined rate of exposed individuals q. It follows from Figure ? ?(C) and (D) that the larger quarantined rate of exposed individuals, the less estimated accumulated number of infected cases and deaths cases are. This indicates the effectiveness of increasing the quarantined intensity for the exposed individuals. When the quarantined rate is equal to 1.35q, estimated accumulated number of infected cases is exactly consistent with the real data, which also shows that strong quarantined measures has been taken for the exposed population in the later period. Media reports play increasingly important roles in the outbreak of the COVID-19 epidemic, which can be used to provide the public with information about the dynamic situation of the epidemic and the effective prevention and control measures proposed by experts. It follows from the Pearson correlation and the cross-correlation analyses that the number of daily news items at the ten most popular websites (news.cn, sina.com, cnr.cn, youth.cn, Chinanews.com, gov.com, 163.com, people.com, sohu.com, globalhealth.net.cn) complexity, and we will address the effects of these important factors on the emerging infectious disease in near future. Secondly, we have assumed the exposed individuals do not to infect others in our model(??). However, if the exposed (latent) individuals are assumed to infect others, even if infectivity is very low, it will certainly affect the severity of the epidemic. This study presents a novel methodology through using cross-correlation analysis and embedding the function of individual behavior changes (media impact) into the SEIR-type model, showed that combining statistical analysis with a mathematical model are beneficial for analyzing media impacts. It demonstrated that the media reports and these control measures affect the accumulated number of hospital no-tifications by reducing the transmission rate (increasing the individual behaviour change constant), the contact rate and quarantined duration of uninfected contacts individuals, and also increasing the quarantined rate of exposed individuals. All these results confirmed 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 COVID-19 epidemic, 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. (D) Figure 9 : Sensitivity analyses of key parameter c and q related to the contact rate and quarantined rate of exposed individuals on the COVID-19 infected cases and deaths from January 11th to February 6th 2020, where the baseline value c = 22.169 and q = 3.1363 × 10 −7 . x 5 x 6 x 7 x 8 x 9 x 10 x 11 x 12 y 1 y 1 0.993** 1 x 1 0.974** 0.949** 1 x 2 0.646** 0.583** 0.737** 1 x 3 0.948** 0.905** 0.966** 0.747** 1 x 4 0.767** 0.716** 0.838** 0.739** 0.817** 1 x 5 0.976** 0.947** 0.968** 0.711** 0.981** 0.808** 1 x 6 0.919** 0.885** 0.916** 0.707** 0.947** 0.832** 0.957** 1 x 7 0.763** 0.723** 0.808** 0.626** 0.783** 0.796** 0.763** 0.760** 1 x 8 0.806** 0.776** 0.864** 0.825** 0.829** 0.683** 0.793** 0.738** 0.736** 1 x 9 0.569** 0.494** 0.678** 0.882** 0.716** 0.687** 0.629** 0.651** 0.610** 0.782** 1 x 10 0.562** 0.509** 0.642** 0.801** 0.665** 0.562** 0.618** 0.596** 0.601** 0.804** 0. Significance of correlation coefficient different from zero: ** represents p < 0.01, * represents p < 0.05. Figure A1 : Cross-correlation coefficients between the number of daily news items and the number of new hospital notifications of COVID-19 in Hubei province from January 11th to February 6th 2020. The two dotted lines in the graphs represent the upper and lower confidence bounds of 95% confidence intervals. Figure A2 : Cross-correlation coefficients between the number of daily news items and the number of new hospital notifications of COVID-19 in Hubei province from January 11th to February 6th 2020. The two dotted lines in the graphs represent the upper and lower confidence bounds of 95% confidence intervals. Figure A3 : Diagram of the model (??) for simulating the COVID-19 infection. Interventions including media report, individual behavior change, intensive contact tracing followed by quarantine and isolation are indicated. 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