Mobility patterns of the Portuguese population during the COVID-19 pandemic
MM OBILITY PATTERNS OF THE P ORTUGUESE POPULATIONDURING THE
COVID-19
PANDEMIC A PREPRINT SUBMITTED FOR PROCEEDINGS OF THE
NTERNATIONAL C ONFERENCE ON I NFORMATION T ECHNOLOGY & S
YSTEMS
Tiago Tamagusko
Department of Civil EngineeringUniversity of CoimbraCoimbra, Portugal
Adelino Ferreira ∗ Research Center for Territory, Transports and EnvironmentDepartment of Civil EngineeringUniversity of CoimbraCoimbra, PortugalSeptember 4, 2020 A BSTRACT
SARS-CoV-2 emerged in late 2019. Since then, it has spread to several countries, becoming classifiedas a pandemic. So far, there is no definitive treatment or vaccine, so the best solution is to preventtransmission between individuals through social distancing. However, it is difficult to measure theeffectiveness of these distance measures. Therefore, this study uses data from Google COVID-19Community Mobility Reports to try to understand the mobility patterns of the Portuguese populationduring the COVID-19 pandemic. In this study, the Rt value was modeled for Portugal. Also, thechangepoint was calculated for the population mobility patterns. Thus, the change in the mobilitypattern was used to understand the impact of social distance measures on the dissemination ofCOVID-19. As a result, it can be stated that the initial Rt value in Portugal was very close to 3, fallingto values close to 1 after 25 days. Social isolation measures were adopted quickly. Furthermore, itwas observed that public transport was avoided during the pandemic. Finally, until the emergenceof a vaccine or an effective treatment, this is the new normal, and it must be understood that newpatterns of mobility, social interaction, and hygiene must be adapted to this reality. Keywords
COVID-19 · Mobility Patterns · Rt · Changepoint · Modeling
At the end of 2019, the new Coronavirus (SARS-CoV-2) appeared in the province of Wuhan (China) [1], causing adisease named COVID-19 [2]. As a measure to combat COVID-19, China adopted the lockdown of this province onJanuary 23 rd [3]. This disease spread rapidly to other countries, with the first cases reported in Europe in the secondhalf of January [4]. Concerning Portugal, the first confirmed case of COVID-19 was on March 3 rd , 2020 [5], since thePortuguese government has adopted a series of measures to control the pandemic. To date, there are no vaccines for thisdisease, so the best strategy to combat COVID-19 is to prevent its transmission through social distancing. However, thisis not a simple task, since a large part of the social activities are based on contact people and mobility. In the specificcase of the transmission of COVID-19, the ideal scenario would be to monitor people’s contacts. Initiatives in thisdirection have been developed, but they face some concerns related to privacy. Another possibility is to measure thelikelihood of contacts; this approach can be made by measuring the concentration of people in certain places. Thus, thepopulation’s mobility patterns may indicate the degree of adoption of measures for social distancing [6]. Nevertheless,effectively monitoring population mobility is a difficult task for governments. Google recently released the globaltime-limited sharing of mobility data [7]. This report presents several statistics and aims to promote studies that canhelp combat COVID-19. Mobility data is divided into six categories: retail and recreation; grocery and pharmacy;parks; transit stations; workplace; and residential. The values presented are percentage changes to normal (baseline) ∗ [email protected] a r X i v : . [ phy s i c s . s o c - ph ] S e p obility patterns of the Portuguese population during the COVID-19 pandemic PREPRINT
Table 1: Main public policies to mitigate the spread of COVID-19 in Portuga[5, 8]Intervention Description Date (Y-m-d)Public events Gatherings with more than 100 people forbidden. 2020-03-12Social distancing Capacity restrictions in bars and restaurants, closed night clubs, limitingpeople in closed spaces are recommended. 2020-03-12Schools and universities Schools and universities closed. 2020-03-14Social distancing Decrease in capacity to 1/3 and maintenance of a minimum distance of1 m (ideally 2 m) in public services. 2020-03-17Self-isolating of ill people Isolation is mandatory for sick people or being monitored by healthauthorities. 2020-03-19Lockdown start Official start of the lockdown in Portugal. 2020-03-22Public gatherings Gatherings of more than five people prohibited (except for large fami-lies). 2020-04-02Lockdown end Official end of the lockdown in Portugal. 2020-05-03Table 2: Report categories [7]Category SubcategoriesRetail and recreation Restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.Grocery and pharmacy Grocery markets, food warehouses, farmers markets, specialty food shops, drug stores,and pharmacies.Parks National parks, public beaches, marinas, dog parks, plazas, and public gardens.Transit stations Public transport hubs such as subway, bus, and train stations.Workplace Places of work.Residential Places of residence.mobility patterns. Currently, most European countries face the challenge of reactivating their economies; this task islinked to the gradual re-opening of services, public communal areas, and public transport. However, it is still not fullyunderstood how the population has adopted the lockdown measures. In this sense, this paper finds relationships betweenthe mobility patterns, the social distancing measures adopted, and the spread of the disease in Portugal.
During the COVID-19 crisis in Portugal, the government adopted several measures to mitigate the spread of the disease.The main measures are grouped in Table 1. Other measures were adopted, but these events were considered morerelevant.
To develop this study, we used mobility data [7] and the cases of COVID-19 in Portugal [9, 10]. The mobility report,called Google COVID-19 Community Mobility Reports, is data collected from mobile devices to quantify the movementof people during the pandemic. These values are anonymous and are aggregated based on the algorithm developed byGoogle, and the artificial noise sample is added to ensure that no individual can be identified based on their locationinformation [11]. The report shows how the population moves and how long they stay in different locations (Table 2).The values presented for the categories are related to a baseline, which corresponds to the days of the week (fromJanuary 3 rd to February 6 th , 2020). With these parameters, it is possible to assess the population’s adherence to thesocial isolation measures enacted by the government. The daily variation of values over time in Portugal, from February15 th to August 16 th , is shown in Fig. 1.In this graph, the vertical axis represents the distance to the baseline. Also, the area between red dashed lines representsthe lockdown period in Portugal (started on March 22 nd and finished on May 3 rd ). According to the data provided evenbefore the lockdown, the values for the first five categories show falls. There was growth only in the residential category.In the days before the lockdown, there are peaks in the items Grocery and Pharmacy; this can be explained by thegeneral rush to get supplies. The park-related peaks do not have a simple explanation. However, after this brief initial2obility patterns of the Portuguese population during the COVID-19 pandemic PREPRINT
Figure 1: Mobility trends for Portugal.Figure 2: Cases of COVID-19 in Portugal.period, the population followed the imposed recommendations avoiding these locations during the lockdown. After thesoftening of the measures, there is an increasing demand (over 100% at the end of August) for parks. It is assumed thatinfluence of the adaptation of the population’s routines to outdoor activities. It should also be noted that before March22 nd , schools and universities were closed, and several companies started to operate in teleworking. After lockdown,the values remained historically low. As expected, the tendency to stay at home is highly related to the workplace, in anapproximately reversed trend. In addition to the mobility data, values related to the cases of COVID-19 in Portugalwere used between March 3 rd (first confirmed case) and August 20 th (Fig. 2).It is observed that the notification of the number of new cases is somewhat irregular. On weekends and holidays, thenotifications are lower, and the following notifications are “inflated”. Another problem observed is that the number of3obility patterns of the Portuguese population during the COVID-19 pandemic PREPRINT
Table 3: Changepoint for mobility categories in Portugal Category Changepoint (Y-m-d)Retail and recreation 2020-03-12Grocery and pharmacy 2020-03-13Parks 2020-05-20Transit stations 2020-03-13Workplace 2020-03-14Residential 2020-03-13confirmed cases is proportional to the number of tests performed. Therefore, the procedures adopted for testing thepopulation influence the results of Rt in this study. The idea of this study is that Rt can be influenced by the number of contacts between infected and susceptible individuals.Therefore, the social distancing measures adopted by the public authorities can influence this risk factor. In this study,the contact rate is approximated by the population’s mobility patterns during the pandemic period. Thus, it is consideredthat if the population decreases its presence in parks, restaurants, transportation stations, among others, the number ofcontacts decreases. Supported by the R programming language [12, 13], the
Changepoint framework [14] was used todetect changing values for mobility over time. Thus, it was possible to determine (approximately) the day when themobility values changed their trend. Therefore, the goal is to detect the changepoint from the time series of mobilitydata provided. In this study, the mean approach was used, which uses the
AMOC (at most one change) method [15] bydefault to detect a changepoints from the mobility patterns sample. Another objective of this study is to calculate the Rt in Portugal, a task that was developed with support from the R , and from the EpiEstim framework [16]. Rt is consideredas to be the average number of secondary cases that each infected individual would infect if conditions remained as theywere at time t . Thus, the value of Rt is determined according to Equation 1 [16]. R ( t ) = t (cid:88) s =1 I t − s ws (1)Where I is the number of people infected at any given time, and ws corresponds to the probability of distribution ofinfections. This distribution depends on the characteristics of the disease. So, to determine the ws , the method adoptedwas Non-Parametric SI . We used the serial interval (SI) parameters presented by Nishiura et al. [17], with µ = 4.6 days(median serial interval) and σ = 2.9 days (standard deviation). Therefore, according to the study mentioned before[17], the average time for infected people to generate a second infection is 4.6 days. However, COVID-19 does notpresent itself equally in all infected individuals, they can be infectious over a period (serial interval). Consequently, it isexpected that an individual exposed to COVID-19 may be infected and have an infectious window that lasts up to 14days. This is the concept that endorses the WHO’s 14-day quarantine recommendation [18]. Finally, Rt is an importantindicator, as it can identify the stage of an infectious disease. For example, an Rt of 2 means that each infected person,on average, transmits the disease to two other people. On the other hand, an Rt less than 1 indicates that the spread ofthe disease is controlled, and it tends to disappear [19]. The first result to be presented is the estimated day for a change in the behavior of the Portuguese population (Table 3).As presented before, these values were calculated using the
Changepoint framework [14] and based on the daily valuesfor mobility data in Portugal [7].The approximate changepoint is between the 12 th and the 14 th of March. Still, it is observed that the most significantdrop occurs from March 12 th , which is the date when the first public measures of social distancing were adopted. Thus,from the moment the government recommended people to stay at home, avoid public places, and maintain socialdistance, the population’s mobility pattern fell rapidly. Also, due to the characteristics of COVID-19, the number of newcases took time to slow down. The first case of Covid-19 in Portugal was on March 3 rd , however, the first calculated Rt value is for March 10 th . Therefore, these first seven days are used by EpiEstim to calculate R . Another observation Graphical representation in the appendix B.
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Figure 3: Estimated Rt for Portugalis that the 95% confidence interval (a grey area in the graph) is wide at the beginning of the pandemic in Portugal.The result of the Rt value, based on epidemiological modeling developed for the COVID-19 pandemic in Portugal,calculated between March 10 th and 20 th August, is shown in Fig. 3.It is possible to see that the measures adopted between March and April managed to bring the Rt value from a levelclose to 3 for values below 1. However, after the end of the lockdown (May 3 rd ), the values have been close to 1, whichmeans that the COVID-19 pandemic in Portugal has not yet been overcome. Likewise, in other European countries,measures of social distancing and lockdown were adopted, with similar Rt results or even lower than those observed inPortugal [20]. Considering a Rt less than 1 to be an indicator of “control” of the pandemic, it can be seen that this value was reachedonly on April 8 th in Portugal, i.e., 25 days after the consolidation of the change in the behavior of the mobility patternsof the Portuguese population. Even after this date, the Rt value was very close to 1, yet in some moments, it was abovethis threshold. Also, the change in the population’s behavior (changepoint) happened before the lockdown. Therefore,this indicator may point out that even without the end of normal activities, people’s mobility is altered to adapt tothe existing pandemic situation. Still, the places with the lowest flow of people during the monitored period are thetransport stations. Nevertheless, this behavior of avoiding public transport creates a challenge for cities at this momentin the resumption of the economy. Likewise, it must be understood that COVID-19 has not been eradicated in Portugal,and the second wave of contagions remains on the radar in Europe [21]. Based on the values in Portugal, there issuch a possibility since the number of confirmed cases so far is not likely to protect a population with herd immunity.Currently, it is essential to define red lines for the number of new daily cases. Similarly, successful measures usedin other countries must be adopted. Another critical point is that the Rt value was obtained based on the number ofinfected individuals confirmed daily. These numbers may not correspond to the reality of the disease, because thenumber of confirmed infected depends on the number of tests performed, and the criteria adopted to test the populationwas not well explained. As the main result of this study, it was observed that the Portuguese population reacted quickly, adopting socialdistancing, and changing their mobility pattern, even be-fore the government decreed restrictive measures. Still, it took Result of the modeling developed in the appendix C.
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25 days for a Rt value close to 3 to reach values near to 1. Now, it is expected that after the first wave of COVID-19,countries are better prepared for a probable second wave. Notwithstanding, observing the behavior adopted by thePortuguese population during that first lockdown, a second intervention of this type to be effective should last betweentwo to four weeks. It was also possible to observe that the sharpest drop occurred in public transport stations. Probablyfor fear of crowded locations, people sought individualized alternatives. A significant part of the population mostlikely used the car on their travels. With the re-opening of cities and the economy, this alternative may quickly proveunfeasible. Therefore, there is now a small window to co-opt users for active transport. Another observation was thesignificant increase in mobility in parks after the softening of lockdown measures. This trend of outdoor activitiesshows the importance of these spaces for cities. Finally, we must understand that, for now, life cannot be as it wasbefore the pandemic. Hence, until the discovery of a vaccine, the population, and the governments must be prepared forthis new normal. References [1] Chaolin Huang, Yeming Wang, Xingwang Li, Lili Ren, Jianping Zhao, Yi Hu, Li Zhang, Guohui Fan, JiuyangXu, Xiaoying Gu, Zhenshun Cheng, Ting Yu, Jiaan Xia, Yuan Wei, Wenjuan Wu, Xuelei Xie, Wen Yin, Hui Li,Min Liu, Yan Xiao, Hong Gao, Li Guo, Jungang Xie, Guangfa Wang, Rongmeng Jiang, Zhancheng Gao, Qi Jin,Jianwei Wang, and Bin Cao. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.
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Appendix A Data and reproducibility
The raw data used for the development of this study are [7, 9, 10], the processed data and generated images can beaccessed at github.com/tamagusko/icts21. The codes developed in R are available by request. Appendix B Graphical representation of the changepoint
These graphs (Fig. B.1) support the interpretation of the data presented in Table 3. The approximate changepoint is onMarch 13 th (day 28) for most categories (excluding parks). Still, it is observed that the most significant drop occurssince March 12 th , this being the day of the first measures of social distance in Portugal. Appendix C Result of epidemiological modeling