The Correlation of Mobility Trend and Situation of COVID-19 by Country, Territory, and Area

Each country around the world has taken several approaches in order to stop the spreading of the virus. This study was conducted to correlate the mobility trend and the situation of COVID-19 by country, territory, and area. This research paper adopts an observational analytic study with a crosssectional approach for 115 countries from February 17 to August 27, 2020. Tajikistan (r=0,956) and Italy (r=0,931) has the highest positive correlation for retail and recreations. For grocery,the highest degree of positive correlation is Mongolia (r=0,945) and Tajikistan (r=0,933). Bostwana and Italy showed highest significant positive correlation among countries (r=0,985 and r=0,902, respectively) for transit stations and residential (r=0,994 and r=0,984). Bostwana also has the highest significant positive correlation for the park (r=0.980). Meanwhile, for the workplace, Mauritius (r=0,863) and Dominica (r=0,785) are countries with the highest degree of positive correlation with a cumulative case of COVID-19. Society's behavior plays an important role by following the government policy in order to slow down the spread of the virus. Retail and recreations, groceries and pharmacy stores, transit stations, parks, and workplaces found to have a significant positive correlation while residential have a significant negative correlation with cumulative cases of COVID-19 in most countries.


Introduction
At the end of 2019, the world was faced with a series of cases of a new type of pneumonia in Wuhan, China. 1 This disease was originally referred to as Fever of unknown origin with pneumonia caused by 2019-novel coronavirus (2019-nCoV). 2 WHO stated this situation as International Public Health Emergency of International Concern. On February 11, 2020, after a long analysis, it was discovered that the cause of pneumonia was severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In February 2020, 26,359 suspected cases were found; 31,225 confirmed cases with 639 deaths. The disease caused by the SARS-Cov-2 is designated by WHO as COVID-19. 3 The COVID-19 epidemic began to spread in early December from the city of Wuhan, one of the cities in China with the largest population and highest export and import activity. COVID-19 cases were first reported outside China on January 13, 2020 in Bangkok (Thailand). In almost two months, 67 territories outside China reported 8,565 confirmed cases of COVID-19. Reports of confirmed cases of COVID-19 around the world are increasing, especially in Iran and Italy which reported the highest cases. On March 11, 2020, WHO declared COVID-19 a pandemic. 1,250,000 suspected cases have been found in 180 countries, 69,210 cases of death with a crude fatality rate of 5.5% as of April 7, 2020. 1,2,4 Lock downs along with restrictions on mobility have been implemented around the world to reduce the spread of the COVID-19 epidemic. Without the lockdown in Wuhan, it is estimated that the number of COVID-19 cases will increase by 64.81% in 347 cities in China outside Hubei province, and 52.64% higher in 16 other cities in Hubei. Many countries have strict mobility restrictions, travel bans, minimum distances and quarantines to slow the spread of the virus. Italy, the first country in Europe to experience a large-scale outbreak, imposed restrictions on the mobility of citizens on March 8, and starting March 21 there was a decrease in the number of new cases. 5,6,7 Since 31 December 2019 to 12 September 2020 the number of COVID-19 cases worldwide reached 28,516,744 with 16,005 deaths. 8 In Indonesia, the number of confirmed COVID-19 cases reached 214,746 cases with 8,650 deaths. 9 The number of new cases continues to increase every day accompanied by an increasing number of deaths. With the increase number of cases of COVID-19, human mobility and order are being enforced. As of 17 August 2020, a total of 219 countries and territories have issued 83,694 trips which represents a 3 percent increase from the 81,843 trips reported on 10 August 2020. 10 In order to identify the effectiveness of handling the pandemic situation by reducing the society mobility, COVID-19 community mobility reports were presented by Google to measure the numbers of visitors in specific categories everyday. This study was conducted to correlate the mobility trend and the situation of COVID-19 by country, territory and area.

Design and sample
This research used observational analytic study with a cross-sectional approach conducted on 115 countries situation of COVID-19 and mobility trend which includes retail and recreation, grocery and pharmacy stores, residential, transit, park and workplace from February 17 th , 2020 until August 27 th , 2020. Countries with missing data and without mobility trend data was excluded from this research.

Data sources
Data needed in this study included the situation of COVID-19 by country, territory and area, and the mobility trend data of retail and recreation, grocery and pharmacy

Data analysis
The correlation between the cumulative cases and mobility trend were then analyse using STATA Version 15. The cumulative cases of COVID-19 were matched with the data of mobility trend available in each country, territory and area. Spearman correlation test were used to measure the correlation between the variables. A data of p-value <0.05 were noted as significantly correlated.

Results
Data was collected from WHO cumulative cases of COVID-19 while mobility trend were taken from Google Mobility Trend website. Data were analyzed and correlated based on six category which are retail, grocery, residential, transit, park, and workplace. We examine correlation between mobility trend and cumulative cases of COVID-19 throughout 115 countries. Based on the table 1, it could be summarized there tren significant positive correlation between retail and recreation, grocery and pharmacy stores, transit station, park and workplace to cumulative case of COVID-19 (p=0,000). There tren significant negative correlation between residential and cumulative cases of COVID-19 (p=0,000). However, there are some countries showing negative correlation on retail and recreation, grocery and pharmacy stores, transit stations and workplace such as India and Japan. There are also countries that show positive correlation on residential such as Bostwana and Italy.
European countries have park as the highest correlated variable and residential has negative correlation. That results are different in Italy. Countries from Europe continent has retail and recreation as the highest correlated variable.

COVID-19 began to spread in early
December from the city of Wuhan, one of the cities in China with the largest population and highest export and import activity.WHO has declared the COVID-19 pandemic as a global health emergency. Since 31 December 2019 to 12 September 2020 the number of COVID-19 cases worldwide reached 28,516,744 with 16,005 deaths. 8 In Indonesia, the number of confirmed COVID-19 cases reached 214,746 cases with 8,650 deaths. 9 Till date there have been no reports of clinically approved antiviral drugs or vaccines that are effective against COVID-19. World Health Organization (WHO) recommends non-pharmaceutical intervention to bring down the rising number of COVID-19 cases. 11 One of the examples in non-pharmaceutical intervention is social distancing and "stay at home" orders.
COVID-19 has spread rapidly across the globe, posing enormous health, economic, environmental and social challenges for the entire human population. The corona outbreak virus is very disruptive to the global economy. Almost all countries struggle to assist disease transmission by treating and caring for patients, quarantining suspected persons through contact, limiting large gatherings, maintaining full or partial lock downs, etc. Many countries have strict mobility restrictions, travel bans, minimum distances and quarantines to slow the spread of the virus. Italy, the first country in Europe to experience a largescale outbreak, imposed restrictions on the mobility of citizens on March 8, and starting March 21 there was a decrease in the number of new cases. 6,7 Without the lockdown in Wuhan, it is estimated that the number of COVID-19 cases will increase by 64.81% in 347 cities in China outside Hubei province, and 52.64% higher in 16 other cities in Hubei. 5 Several countries have misled aggressive policies and directed their resources to the reality of this global health emergency, based on the experiences of countries that suffered from the initial onslaught of this pandemic. However, not all country has the same capability to maintain economy stability within this crisis period. Many developing countries are unable to take the same action as developed or wealthy developing countries. 12 Reported data prove that total mobility in many countries of the world has decreased as a result of sudden cessation of human activities due to lock downs. This causes an extreme breakdown of mobility patterns. Human mobility is one of the major factor in the spread of a diseases. High human mobility may introduce one diseases to another area where the diseases may have not been transmitted. Therefore, high human mobility has an association to high transmission of a disease. 13 We use mobility data from Google to elucidate the role of mobility have on cumulative cases of COVID-19.
We collected 115 countries mobility data from Google then examine correlation between mobility trend and cumulative cases of COVID-19. It was found majority of countries have positive correlation to retail, grocery, transit, park and have negative correlation on residential.
Majority of countries have enacted various orders as responses to COVID-19. One strategy that is particularly important is social distancing that may be assessed indirectly by our mobility patterns data. We found positive correlation on high visit to retail, grocery, transit and park on cumulative cases of COVID-19.
However, even during a pandemic, foodstuffs are still a basic human need. In fact, consumer spending on groceries continues in every kind of grocery store. By far, the biggest increase in consumer spending on groceries is making online retailers. 14 The COVID-19 lockdown improved food shopping performances.
In line with our findings, Delen et al used the cross-national mobility trends to analyses the efficacy of social distancing and found 47% variation in COVID-19 transmission is due to change in mobility patterns from enforcing social distancing with mobility in public places such as restaurants, grocery stores, public transport being the most important determinants of transmission rate. 15 Study from Badr et al also confirmed the same findings that mobility patterns are strongly correlated (r=0,7) with decreased COVID-19 case growth rates for 20 of the 25 most affected counties in USA. All these results point out that increase visit to public places also increase COVID-19 infection significantly. 16 Similarly, Valenti et al found in the mathematical model made to predict effects of social distancing on estimated death in Brazil, it was predicted that on May 24 there would be 32,825 total number of deaths related to COVID-19 compared to actual numbers 22,965. 17 This suggest social distancing has curb down the number and saved approximately 10 thousand of Brazilian lives.
Other studies by Jianhong Wu found reproduction number estimate decreasing during time interval from 3.25 to 2.97 to 2.84 indicating a gradually efficacy of interventions adopted. 18 The study also predicted the number of cumulative confirmed cases per 7 April to be 6132 (95%ci 4250-8000) and could be reduced further by decreasing contact rates further by 90%.
Study by VoPham et al on association of social distancing and COVID-19 incidence found higher social distancing was associated with 29% reduction of COVID-19 incidence (adjusted IRR 0.71;95% CI 0,57-0,87) and 35% reduction of COVID-19 mortality (adjusted IRR 0,65; 95% CI 0,55-0,76). 19 Overall, individuals made about 1.62 trips/day/person during COVID-19 compared to about 3.33 trips/day/person in the pre-pandemic period, which is down more than 50%. 20 For residential, we found negative correlation to cumulative case of COVID-19. Residential indicates how long an individual stay within nonpublic places such as home,etc. Increase duration on stay at home mean decreased mobility within public places that potentially has many people. This decrease risk of transmission of COVID-19 as it can be transmitted via droplet.
Tran PhuocBao found social distancing has worked on 10 countries although the effect may be showed delayed, that is after 1-4 weeks. 21 Courthemanche also supported the same findings that without social distancing being issued, there would be 10 times greater the number of COVID-19 infection and more than 35 times transmission. 22 However, there are some countries such as Philippines, Thailand, Oman, Mexico, Bahrain, Costa Rica, India, Nigeria, Nepal and Nicaraguay that shows no significant correlation on residential and cumulative cases of COVID-19. This is maybe because lack of active strategies such as testing, isolation and contact tracing. Passive social distancing is not enough to flatten the curve of COVID-19 as the infection may still occur somewhere else, for example in household. 23 Majority of Europe countries has correlation to COVID-19 cases. Italy is one of Europe countries with highest degree of positive correlation to retail and park and highest negative correlation to grocery. Italy is one of Europe countries with highest elderly population, only second after Japan. 24 COVID-19 is virus with severity and risk of death that is dependent on age. This may have an impact particularly for the residents of Italy. Family members that continue with their daily activities may be infected and transmit the virus to their family. Asymptomatic infections complicate the matter even further. High negative correlation on grocery is puzzling however it can be explained by the number of COVID-19 cases that has been going down on Italy. Therefore it is shown as false negative correlation.
There are also some countries that showed no significant correlation between mobility change and COVID-19 cumulative cases. In this study, there were some countries with cumulative cases of COVID-19 that begin to decline such as Japan. Some lesson to be learned from the country success dealing with the pandemic involve innovative strategies to test and trace, transparency of government decision and citizen behaviour as one of the most important factor. Thus, to achieve success in curbing COVID-19 cases, there needs to be many factors to had, however these combinations are often lacking in most developing countries. 25 Mobility change is implicated in social distancing, that is a method to minimize crowd interaction and therefore decrease the spread of a disease within people. 26 COVID-19 outbreak isn't the first time social distancing has been implemented. Previous occurrence includes 1918 influenza pandemic in which, 50-100 million deaths were reported worldwide. Due to the risk carried by the virus, every individual of a community needs to protect the vulnerable groups such as comorbid group, homeless, etc to decrease transmission of COVID-19 by reducing mobility to public places. 27 Study of the effect of mobility habits on the spread of the Coronavirus in Italy. Daily COVID-19 cases are directly related to mobility habits carried out 21 days earlier. Population density, PM pollutants and number of tests per day are directly related to infection. Temperature has an inverse relationship with the spread of viruses. Areas close to the outbreak have a higher risk of transmission (time decay phenomenon). 28 The public health response to COVID-19 in China has illustrated that it is possible to contain COVID-19 if the government focuses on a tried and tested public health outbreak response. Isolation, quarantine, social distancing and community containment measures were swiftly implemented. In China, patients with COVID-19 are immediately isolated in existing hospitals, and new hospitals are rapidly being built to handle the increasing number of cases in the hardest-hit areas. Home quarantine for contact began and large gatherings were canceled. In addition, community incarceration for an estimated 40 million to 60 million inhabitants has been institutionalized.
A significant positive relationship between the incidence of COVID-19 cases and mortality is evident in China's response. 29 Most of the COVID-19 cases associated with local transmission have been identified in countries located in the northern hemisphere, which are experiencing the "flu" winter season. Likewise, the global SARS-CoV-2 outbreak in February 2003 did not affect Africa or South America on a large scale, suggesting that the respiratory virus spreads more effectively in winter and, therefore, the southern hemisphere will be affected later in the year, if at all. Climatespecific culture differences (living more outdoors than indoors), effects of UV light on viral survival at surface, differences in population immunity (innate immunity), pre-exposure to the coronavirus, or higher temperatures can all contribute. To date, all identified cases of COVID-19 in Africa have originated in Europe and not from China. 29 Areas with higher population densities have a higher probability of contagion, being less able to guarantee social distancing (increase in social activities with overcrowding). Respect to air quality impacts upon the spread of COVID-19, some recent research has shown that people living with long-term exposure to air pollution are more likely to become infected by coronavirus. 28 Areas of the country with the highest PM pollution are mainly located the industrial areas and/or most of the population live, according to with the circumstance that PMs are mainly generated by industries, heating (e.g., home, office) and transport sector (e.g. mobility habits). The opposite area with lower PM pollution characterized by an economy mainly based on tourism and agriculture. 28 Our study has several strength. This study has the most countries to examine so far. However, this study also have several limitations. First, the mobility data on this study were obtained from Google Mobility data. However, this data couldn't represent the entire population in the countries since there were only 57% of 7 billion people around the world that use the internet. 30 Thus, we may underestimate the impact of mobility change in some countries. Second, this study evaluate mobility change as the only factor affecting COVID-19 cases. Mobility change may be mediated by several factor such as massive test and trace, community behaviour that is not yet assessed in this study. Besides, mobility trend change may also be affected by internet shopping, especially on goods transport. Due to desire to avoid crowded stores out of fear of contracting coronavirus, consumers turned to the internet to do grocery shopping. However, internet shopping may increase online transport mobility to buy consumer needs and deliver them to respective place. Moreover "Park & Pickup" program, a non-contact shopping service allowing customers to have groceries delivered to designated parking lots, and other programs offered for public shopping alternatives which are not assessed and taken into measure in this study. This may potentially give an impact towards the results. Future work should keep in mind of these factors.
Our study may elucidate the evidence for effectiveness of social distancing policies by mobility change data. We believe this study shed light on high potential of technology to study the pandemic. Even though we only took retrospective approach by using provided mobility trend data, study in the future may use proactive approach that use tracking technologies to identify people and location at risk to help the government and policy makers. Our result may provide an illustration to the government that still had not implemented mobility restriction. Finally, further research is still needed to examine behaviour predictive to reduce mobility and possible implementation to policy.

Conclusion
Mobility trend among countries are variable during the COVID-19 pandemic. Society behavior plays an important role in order to slow down the spread of the virus. In which the society needs to obey the government order such as "stay at home", social distancing, avoid going to public places to reduce society mobility accordingly to their own country policy made while facing this pandemic. This study shows that the mobility trend gives an impact towards the COVID-19 cases.