The impact of social ties and SARS memory on the public awareness of 2019 novel coronavirus (SARS-CoV-2) outbreak

The early warnings of the outbreak

As early as Dec 31st, 2019, when Wuhan Municipal Health Commission first informed the public about the emerging pneumonia cases²¹, most of the cities (326 out of 346) exhibited at least some awareness of the emerging SARS-CoV-2 outbreak (Fig. 2b). However, awareness then decreased until Jan 19th, 2020, one day before the Chinese Centre for Disease Control and Prevention confirmed human-to-human transmissions of the novel coronavirus⁹. Since Jan 20th, 2020, overall awareness increased by a magnitude of at least five, demonstrating significant awareness across all cities (Fig. 2b). Awareness remained low as the epidemic spread, falling close to its lowest point on the starting day of Chunyun (Jan 10th, 2020). Considering cities that showed initial novel coronavirus awareness levels at least 1.5 times that of the search term “common cold”, we found a total of 166 alert cities as early as Dec 31st, 2019 (48 cities at a tighter threshold of (C = 3.0) times, illustrated in Fig. 2a). However, awareness decreased significantly during Chunyun.

The evolution of public awareness over time followed an unusual pattern. In a typical UAU process, people are unaware of emerging catastrophic events until they are told by their social contacts. They remain aware during the event, and awareness then fades subsequently^2,22. However, during the Wuhan outbreak, the public experienced a process as aware-unaware-aware, with public awareness declining during the early phase of the outbreak.

Dividing cities into two groups according to whether or not they had reported SARS cases in 2003–04, we found the cities that had been struck by SARS to be more alert during onset (Fig. 2b). Therefore, we believe the SARS memory still conditions public awareness. We provide evidence of its effects at the end of this section.

Awareness advantage

Three features define the awareness advantage of alert cities, including early awareness, strong magnitude and high retention of awareness.

Lead-time advantage

During onset, between Dec 31st, 2019 and Jan 23rd, 2020, 266 cities exhibited significant public awareness (using a threshold at (C = 3.0); 210 cities at (C = 4.0); 314 cities at (C = 2.0); and 322 cities at (C = 1.5)). The lead-time advantage ( Delta t_{i}) ranges between − 13 and 10 ((C = 3.0)), with an average of − 7.4 days and a median of − 10 days. Forty-eight cities emerge with early signals of public awareness, as early as Dec 31st, 2019, while for most others (255 cities), awareness is as late as Jan 20th, 2020 (Fig. 2a).

Magnitude of awareness

In terms of the magnitude of awareness, all 346 cities exhibit at least some awareness during the onset. The values of (O_{{t_{warningleft( i right)} }}^{{{text{COVID}} – 19}}) range between 0.50 and 61.51, with an average of 2.18 and a median of 1.49. Wuhan undoubtedly ranked the first, while Wuzhong in Ningxia ranked last. Cities in Hubei province exhibit much greater awareness, with an average of 7.83 and a median of 3.46. Shenzhen, Shanghai and Beijing also had high scores at 10.40, 10.65, and 9.78 respectively. Those three cities are both close to Wuhan in terms of social ties (D_{i}) and struck by SARS.

Retention of awareness

Even though most of the cities exhibit at least some awareness as early as Dec 31st, 2019, only a few retain it over the following weeks as the virus began to spread. The retention rates, ({Delta }O_{i}), range between zero and 137%, with an average of 54% and a median of 55%. Eight cities lost awareness before Chunyun, while four cities developed greater awareness. Xilingol League in Inner Mongolia ranked 4th, with a retention rate at 103%. Xilingol is far away from Wuhan in terms of social ties distance, but it was struck by SARS. It is worth noting that a confirmed case of plague was reported in Xilingol on Nov 16th, 2019, only 45 days before the Wuhan authority confirmed the emerging pneumonia cases²¹.

Estimation of the social ties and SARS memory effects

The effects of social ties and SARS memory on the lead-time advantage are estimated according to Eq. 4, controlled by Euclidean distances, GDP per capita and the city’s administrative level (Table 1). We found that, in model (3) in Table 1, (SARS_{i}) exhibits positive effects, while (D_{i}) shows a negative association with awareness. That means cities of strong SARS memory and which are closer to Wuhan in terms of Social ties develop early awareness. Moreover, the interaction term (D_{i} * SARS_{i}) exhibits negative effects, indicating that the SARS memory effect becomes stronger where cities are closer to Wuhan in terms of social ties distance.

While controlling the model with Euclidean distances (model (5) in Table 1), we found that SARS memory effect becomes non-significant, but social ties and its interaction with SARS memory hold. Meanwhile, Euclidean distances are non-significant, even though it exhibits a negative effect on its own in model (4) in Table 1.

We further control the model with GDP per capita and administrative level (models (6) & (7) in Table 1). Using Akaike information criterion (AIC) to select the best model²³, we found the performance of model (6) and (7) are very similar. However, because model (6) achieves a slightly lower AIC score²⁴ at 1893.62 with fewer degrees of freedom (df = 8) than model (7) (AIC = 1894.77, df = 9), model (6) is [very slightly] preferred. For more information about the model selection for Eq. 4, 5 and 6, see File S3 in the SM. In model (6) in Table 1, we found that both social ties and Euclidean distances exhibit negative effects, but the social ties effects decrease almost half compared to model (5) in Table 1. The SARS memory effects hold. Also, the interaction term (D_{i} * SARS_{i}) is still significant, which means cities with stronger SARS memory will develop more lead-time advantage, particularly when they are closer to Wuhan. For example, Changchun in Jilin province with 34 SARS cases and far away from Wuhan still achieved a ten days lead-time advantage. The cities that did not exhibit awareness, such as Qaramay and Heihe, are mainly located far away from Wuhan and did not suffer from the SARS outbreak. GDP per capita and the binary variable (SubProvincial_{i}) exhibit significant positive effects on the lead-time advantage.

The effects of social ties and SARS memory on the magnitude of awareness are estimated according to Eq. 5 (Table 2). Similar to the findings in Table 1, (SARS_{i}) memory positively affects public awareness in all models. Social ties (D_{i}) show a significant negative effect only in the models without controlling variables (model (2) & (3) in Table 2). However, the interaction term between social ties with SARS memory show a significant negative effect. Using AIC-based model selection method, we found that model (6), which control by Euclidean distances, GDP per capita and the administrative level, is the best model. The effects of administrative level and development level both exhibit positive effects on the magnitude of awareness. We hypothesize that residents with better education (proxied by GDP per capita) better understand the danger of deadly infectious diseases and, accordingly, tend to seek up-to-date information online.

The effects of social ties and SARS memory on retention of awareness are estimated according to Eq. 6. Model (6) in Table 3 is the best model based on the model selection using AIC. Unlike the results in Tables 1 and 2, we observe no effects from SARS memory (model (6) in Table 3). When we control Euclidean distances, development level and administrative level, the explanatory power of the model is still relatively weak (Adj. (R^{2} = 0.104)). It seems the decreasing awareness is a collective behavior that occurred simultaneously. Interestingly, social ties have a significant effect while the Euclidean distances do not. Development level exhibits positive effects, which suggests residents of better educated cities could be more alert during the epidemic onset. However, administrative level shows a negative effect. It seems residents living in important cities (in terms of administrative power) lost interest in the disease before Chunyun.

Source: Ecology - nature.com