key: cord-0733684-7ecudwtw authors: Zhou, Shuli; Zhou, Suhong; Zheng, Zhong; Lu, Junwen; Song, Tie title: Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China date: 2022-04-20 journal: Appl Geogr DOI: 10.1016/j.apgeog.2022.102702 sha: 70081559385f7ed5440479809ca5629038c4ec43 doc_id: 733684 cord_uid: 7ecudwtw Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks. The spread of COVID-19 poses unprecedented challenges for governments throughout the world 27 J o u r n a l P r e -p r o o f (Anderson, 2020) . Strict non-pharmaceutical interventions have been adopted in many countries (e.g. China) to control the COVID-19 pandemic (Tian et al., 2020) . China is gradually relaxing lock-29 down and social distancing measures when restarting economic and public activities. However, the 30 prevention of COVID-19 in cities is still under large pressure and facing the disease resurgences 31 before achieving herd immunity. This is because sporadic cases are continuously occurring, 32 particularly in megacities with high-density populations. For example, a local outbreak occurred in 33 Nanjing on 20 July 2021 had spread to more than twenty cities in the whole wave. It is urgent to Since, the contacts (random encounters) in these places were difficult to track. It is more challenging 57 J o u r n a l P r e -p r o o f to control the epidemic transmission occuring in public places than residences and workplaces. However, few studies assessed the disease transmission risk in public places, which is pivotal for 59 reopening economic and public activities. Therefore, it is of great significance to discuss the 60 location-based precise intervention measures of COVID-19 epidemic in public places. Geographical big data, such as mobile phone data, social media data, GPS tracking data and However, access to location-based mobile data is a challenge. Most mobile network operators, 77 tended to be very reluctant to publish data to public and researchers (Oliver et al., 2020) . Fine-scale 78 or personal data (such as individual-level mobility data) were often inaccessible, due to legal and 79 ethical considerations, as well as privacy and security concerns (Parker et Kraemer et al., 2020) . Google released weekly mobility reports in a sub-national scale (Google, J o u r n a l P r e -p r o o f 2020). Apple Inc. released a similar dataset of daily mobility. However, these datasets cannot reflect intra-city mobility since they only represent travel flow between cities or provinces (Apple, 2020). Fortunately, public datasets, such as Tencent location request data (https://heat.qq.com) and Baidu 90 Heat data (https://map.baidu.com/), are available with high spatial-temporal resolution (25m*25m) 91 in the intra-city level. These datasets can be converted by mathematical methods to approximate the 92 intra-city travel flows. The estimate of intra-city travel flows is incredibly valuable and essential for 93 evaluating the disease transmission risk and develop precise intervention strategies of COVID-19 94 epidemic. In summary, most of the previous research was limited in spatio-temporal transmission risk 96 assessment at the micro scale (most of them at national, provincial, and regional level etc.), and the 97 research results could not be directly adopted by the local government to implement precise intra-98 city intervention measures of COVID-19 epidemic. The micro level research plays an important 99 role in providing the categorization and prioritization of intervention measures. Individual-based 100 simulation has been proven to be an effective method for the risk assessment, but the acquisition of 101 individual-level mobility data was limited. Moreover, the existing studies had little concern about 102 the intra-city intervention measures, especially for the epidemic occurring in public places. Therefore, how to simulate the intra-city spatio-temporal spreading process with publicly available 104 aggregated data under public health emergency events is an urgent task to be discussed. In order to fill these gaps, this study used publicly available dataset to approximate dynamic stay was defined by the telecom company when a user stopped at the same location more than 0.5 135 hour. The company has the original raw data of individual trajectories in the database, but it did not 136 provide the raw data to researchers due to the privacy policy. Rather, the company aggregated users' 137 locations to 500m*500m spatial grids from the database. It counted the number of users in each grid 138 per hour. Finally 24-hour dynamic distribution datasets were provided to us. In particular, the users 139 who stayed at the same grid for more than 20 hours in one day were removed from the dataset, 140 because they did not move all day and contributed little to the spread of disease. This study estimated OD flows from the dynamic population distribution data by a gravity 158 model. We emphasized the use of estimated OD flows rather than actual OD flows. The reason was 159 to make this study have wider applicability. Dynamic population distribution data with a high 160 spatial-temporal resolution in the intra-city is publicly published and accessible, such as Tencent 161 location-based big data and Baidu Heat data etc. In contrast, intra-city OD flow data is not easily 162 accessible. In order to test the reliability of the model, this research used the dynamic population 163 distribution data from mobile phone operator other than Tencent or Baidu, since the actual OD flow 164 data for model validation were limited accessible from Tencent and Baidu. Our study aimed to use 165 publicly available dynamic population data to simulate the spatio-temporal spreading process of 166 COVID-19. It provided a novel approach to respond to the public health emergency events rapidly 167 under limited access to individual-level or intra-city mobility data. The gravity model is a useful spatial interaction modeling method, which has been widely 170 applied in transportation and human geography research (Duffus et al., 1987) . Many studies used it Where is the decay parameter, which equals to the reciprocal of the expectation value of 183 the exponential distribution. E( ) is the average travel distance between grids in this study. Previous studies showed that the average travel distance in Guangzhou was about 5km ( The dynamic process of attack rate with time in each scenario was shown in Figure 3 , as well 317 as daily-newE /daily-newI /daily-newR (the number of new exposed/infected/removed agents per 318 day). The attack rate (Figure 3a) increases rapidly, then reaches a peak and gradually flattens, 319 presenting an S-shape curve. While, the other three curves (Figure 3b-3d) show inverted U-shaped 320 shapes, increasing firstly and decreasing afterward. Eleven scenarios could be classified into four 321 levels based on the temporal changes. The daily-newE increased sharply from the 7 th day, reached the peak on the 14 th day and then began 326 to decline, down to zero after the 22 nd day (Figure 3b ). The daily-newI began to increase three day 327 later than daily-newE, and took about one week to reach a turning point on the 18 th day (Figure 3c ). Because, the infected cases would be removed through an 'infectious period', the daily-newR 329 followed the same trend curve with "daily-newI" after a few days later. Scenario 10 and scenario 11 (public places in the rural outer suburbs) belonged to the same 331 category. The attack rate of the two scenarios, less than 2%, was the lowest in the all scenarios. They 332 were located farthest from the city center. The epidemic occurring in this places didn't spread widely 333 and the transmission stopped after local spread within a small area. Scenario 8 and scenario 9 (public places also located in the outer suburbs) could be classified 335 into another category. The final attack rates of these two scenarios were the highest (82% and 80%) 336 among all the scenarios. Simultaneously, they had the slowest speed and the longest transmission 337 duration. The attack rate increased explosively from the 20 th day, which was 10 days later than the 338 first category (Scenario 1-5) . The daily new infected cases reached the peak at the 25th day, with 339 the same lag time of ten days compared with Category I. J o u r n a l P r e -p r o o f The remaining scenario 6 and scenario 7 could be summarized into the last category. The final 341 attack rate of these two scenarios was more than 77.5%, fluctuating between Category I (scenario 342 [1] [2] [3] [4] [5] and Category III (scenario 8-9). The peaks of the number of daily new E/I/R cases were 2-3 343 days later than Category I, but 6-8 days earlier than Category III. The two areas were close to the 344 periphery of the city center, but located in the inner side of suburbs. The Finally, we found that the epidemic occurring in the rural outer suburbs (scenario 10-11) did 394 not spread widely, but stopped after only local propagation in a small area nearby (Figure 4f ). Interestingly, we noted that the epidemic occurring in these two areas seldom spread to other regions, 396 nor did other regions to these two areas. 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New 629 England journal of medicine The spatial structure of residential and industrial land use in Guangzhou The spatio-temporal simulation method based on dynamic intra-city travel flows estimated by available big data and gravity model well presented the process of COVID-19 epidemic Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location The following are our statements for the submitting of our paper entitle "Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China" to Applied Geography:(1) the manuscript is our original research;(2) It has not been submitted elsewhere in print or electronic form to another journal or as a proposed book chapter;(3) It has not been published previously or otherwise accessible to the public (e.g., posted on website);(4) No similar or exact submission will be sent elsewhere until your review is completed.