Abstract
Objectives
This study aimed to determine whether there is a correlation between COVID-19 cases and deaths because of COVID-19 and community movements in Turkey and to develop a strategy for future outbreaks.
Methods
The study's data covers COVID-19 cases and deaths between March 11, 2020, and December 16, 2021, and Turkey's Google community movements between these dates. The COVID-19 cases and deaths were obtained from Turkey's Ministry of Health COVID-19 Information Platform. Community mobility collated by Google is retail and recreation, supermarket and pharmacy, parks, public transport, workplaces visits, and residential. The data were transferred via "SPSS (Statistical Package for Social Sciences) for Windows 25.0 (SPSS Inc, Chicago, IL" and statistical analysis was performed. The Spearman correlation test was used as a statistical method. In the Kruskal-Wallis Test, categorical variables were created using increases and decreases in community movements based on the baseline.
Results
A weak positive correlation between daily COVID-19 deaths and supermarket and pharmacy activity (r = 0.28 p < 0.01). A weak negative correlation with park activity (r = -0.23 p < 0.01). A weakly positive and significant relationship with workplace visits mobility (r = 0.10 p < 0.05). There was a weak positive significant relationship with public transport mobility (r = 0.10 p < 0.01), including a weak positive significant relationship with residential (r = 0.12 p < 0.01).
Conclusions
Social distancing measures (such as reducing community mobility) and educating people on viral transmission in possible epidemics will save us time developing new diagnostic tests and vaccine studies.
Keywords: COVID-19, SARS-CoV-2, Social distancing, Pandemic
1. Introduction
Many countries have implemented mobility restrictions to contain the pandemic coronavirus disease 2019 (COVID-19).1 Unfortunately, the COVID-19 epidemic is not over yet, new variants are emerging, and deaths from COVID-19 continue despite vaccination with newly developed vaccines.2
Countries have taken measures to reduce the spread of the virus. These include mandating face masks, ensuring hand hygiene with disinfectants, physical distancing regulations, and quarantine practices.3
During the pandemic, the authorities have established social distancing strategies such as self-isolation, quarantine, and full quarantine.4 Studies5, 6, 7, 8 have shown that social distancing measures effectively reduce the increase in COVID-19 cases and reduce morbidity and mortality. As population movements accelerated the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strict movement restrictions were introduced early in some countries, such as China, to slow the epidemic. Thus, they could control the spread of the disease more quickly.9
In 2020, the first COVID-19 case in Turkey was seen on March 11, while the first death was on March 17.10 In addition, most of the SARS-CoV-2 cases were seen between the dates we examined the data.11 Sixty-three thousand eighty-two cases by April 16, 2021; It was determined that there had been 394 deaths by April 30, 2021,10, 11, 12 with the most deaths. Regretfully, the virus has yet to be eradicated and continues to exist worldwide.3
Post-COVID-19 countries have differed in their methods of combating the pandemic. While some countries take measures to reduce community movements during the rapid spread of the virus (such as South Korea and Turkey), measures taken in some countries are more limited (such as Sweden and Brazil). In this case, measures to minimize community mobility; It is essential to investigate the number of COVID-19 cases and its impact on deaths from COVID-19. This study aimed to determine whether there is a correlation between COVID-19 cases and deaths because of COVID-19 and community movements in Turkey and to develop a strategy for future outbreaks.
2. Methods
2.1. Data collection and processing
The data covers the number of COVID-19 cases and deaths in Turkey between March 11, 2020, and December 16, 2021, and Turkey's Google community movements' exact dates. The number of cases and deaths from COVID-19 was obtained from the Ministry of Health's COVID-19 Information Platform. Community mobility reported by Google is "retail and recreation" (restaurant, theme park, cafe, shopping mall, cinema, etc.), "supermarket and pharmacy" (supermarket, food store, specialty food store, and pharmacy, etc.), "parks" (national parks, public parks, marinas, dog parks, public beaches, plazas, etc.), "public transport" (bus, subway and train stations, etc.), "workplaces visits" and "residential".13 From these data, the movement tendencies of people can be determined.13 We used the region-level epidemiological dataset provided by Turkey's official website,10 TURCOVID19,11 and news.google.com/covid19/map.12 The daily changes in mobility were determined according to the reference value. The reference value is the median for the five weeks from January 3 to February 6, 2020. Change according to baseline in community movements; 40% or more shows, 20%–39.99% shows, 1%–19.99% shows, 0%-(-19.99%) shows, (−20%)-(-39.99%) shows, were converted to categorical variables with (−40%) and fewer scenes.
2.2. Statistical analysis
The data obtained from the study were uploaded to the computer environment via “SPSS (Statistical Package for Social Sciences) for Windows 25.0 (SPSS Inc, Chicago, IL)” and statistical analysis was performed. Spearman's correlation was used as a statistical method for how two quantitative variables were related. In addition, in the Kruskal-Wallis Test, categorical variables were created using increases and decreases in community movements based on the baseline. The pos-hoc Mann Whitney-u test was used for the variables with a significant relationship due to the Kruskal Wallis test. Accepted the statistical significance standard was p < 0.05.
3. Results
According to the histogram and Q-Q Plots analysis, the daily number of COVID-19 cases and deaths between the specified dates does not follow the normal distribution (Fig. 1, Fig. 2, Fig. 3, Fig. 4 ). The Spearman correlation test was selected as the simple correlation test because the dependent variables (number of cases and death) do not follow the normal distribution.
Fig. 1.
Histogram of number of cases per day.
Fig. 2.
Normal QQ plot of number of cases per day.
Fig. 3.
Histogram of number of death per day.
Fig. 4.
Normal QQ plot of number of death per day.
According to the Spearman correlation test results, there was a moderate positive correlation between daily COVID-19 cases and supermarket and pharmacy activity (r = 0.46 p < 0.01), a weak positive correlation with retail and recreation mobility (r = 0.17 p < 0.01), there was a weak positive significant relationship with workplace visits mobility (r = 0.22 p < 0.01), and a weak positive significant relationship with public transport mobility (r = 0.25 p < 0.01) (Table 1 ).
Table 1.
Spearman correlation test results between daily number of COVID-19 cases and number of deaths from COVID-19 and community actions.
| Community Actions | Number of Cases per Day | Number of Deaths per Day | |
|---|---|---|---|
| Supermarket and Pharmacy | rs | 0.46a | 0.28a |
| p | <0.01 | <0.01 | |
| Retail and Recreation | rs | 0.17a | 0.02 |
| p | <0.01 | 0.60 | |
| Parks | rs | −0.07 | −0.23a |
| p | 0.06 | <0.01 | |
| Workplace | rs | 0.22a | 0.10b |
| p | <0.01 | 0.01 | |
| Public Transport | rs | 0.25a | 0.10a |
| p | <0.01 | <0.01 | |
| Residential | rs | −0.05 | 0.12a |
| p | 0.19 | <0.01 |
Abbreviations: rs: Spearman correlation coefficient; a: Correlation significant at p < 0.01; b: Correlation significant at p < 0.05 level.
Weak positive correlation between daily COVID-19 deaths and supermarket and pharmacy activity (r = 0.28 p < 0.01); a weak negative correlation with parks activity (r = -0.23 p < 0.01); a weakly positive and significant relationship with workplace visits mobility (r = 0.10 p < 0.05); There was a weak positive significant relationship with public transport mobility (r = 0.10 p < 0.01) and a weak positive significant relationship with residential (r = 0.12 p < 0.01).
The average number of cases and deaths in changes to the community mobility baseline are shown in Fig. 5, Fig. 6 . In the Kruskal-Wallis Test, categorical variables were created using increases and decreases in community movements based on the baseline. The average number of cases was significantly higher when supermarket and pharmacy activity was 40% or more from baseline. A similar situation was found in retail and recreation, workplace, and public transport. There needs to be more consistency in the number of cases between park mobility changes. Considering housing, the number of cases decreased as mobility increased (Table 2 ).
Fig. 5.
Community mobility changes and the average number of cases.
Fig. 6.
Community mobility changes and the average number of deaths.
Table 2.
The relationship between mobility changes and the average number of cases.
| Community Action Categories | Change in Movements According to Baseline | Average number of cases | p valuea |
|---|---|---|---|
| Supermarket and Pharmacy | 40% and abovec,f,g | 19937.20 ± 8575.541 | 0.000 |
| 20%–39.99% | 13719.72 ± 10697.181 | ||
| 1%–19.99%d,e,f | 9560.67 ± 15815.884 | ||
| 0% - (−19.99%)a,d,g | 12767.03 ± 10774.225 | ||
| (-20%) - (−39.99%)a,b,c | 2803.25 ± 1229.012 | ||
| (-40%) and lowerh | 3862.50 ± 1310.269 | ||
| Retail and Recreation | 40% and above | 0 | 0.000 |
| 20%–39.99% | 0 | ||
| 1%–19.99%a | 21708.46 ± 7614.962 | ||
| 0% - (−19.99%)a | 2572.26 ± 2559.457 | ||
| (-20%) - (−39.99%)a | 10693.14 ± 15462.332 | ||
| (-40%) and lessa | 13297.82 ± 14219.409 | ||
| Parks | 40% and aboveb | 10406.97 ± 10841.193 | 0.000 |
| 20%–39.99%c | 11710.75 ± 12221.855 | ||
| 1%–19.99%d | 10174.64 ± 8684.629 | ||
| 0% - (−19.99%)a,b,c,d,e | 25264.47 ± 18257.004 | ||
| (-20%) - (−39.99%)e | 12724.19 ± 12103.977 | ||
| (-40%) and lessa | 4249.20 ± 4627.896 | ||
| Workplace | 40% and above | 0 | 0.000 |
| 20%–39.99% | 0 | ||
| 1%–19.99%b,d | 22640.35 ± 8748.585 | ||
| 0% - (−19.99%)c,d | 11105.00 ± 12778.226 | ||
| (-20%) - (−39.99%)a,c | 17260.42 ± 15664.560 | ||
| (-40%) and lessa,b | 5594.84 ± 5820.582 | ||
| Public Transport | 40% and above | 0 | 0.000 |
| 20%–39.99%b,d,m, | 22759.59 ± 6602.085 | ||
| 1%–19.99%e | 7569.12 ± 5570.407 | ||
| 0% - (−19.99%)a | 5253.33 ± 8855.989 | ||
| (-20%) - (−39.99%)a,b,c,f | 17377.95 ± 18383.818 | ||
| (-40%) and lessc,d | 8537.74 ± 9773.522 | ||
| Residential | 40% and above | 0 | 0.000 |
| 20%–39.99%a,b | 3826.21 ± 3672.957 | ||
| 1%–19.99%a | 13221.45 ± 14412.000 | ||
| 0% - (−19.99%)b | 14621.43 ± 11560.421 | ||
| (-20%) - (−39.99%) | 0 | ||
| (-40%) and less | 0 |
Kruskal Wallis Test Note: The statistically significant difference was found between variables with the same upper letter due to post-hoc analysis.
The results from the Kruskal Wallis test showed that the highest number of deaths was determined on days when supermarket and pharmacy activity was 40% or more compared to baseline. The highest number of deaths was determined when retail and recreation, workplace, and public transport mobility were at their highest. No consistent relationship was found between the categories of parks and residential (Table 3 ).
Table 3.
The relationship between community mobility changes and the average number of deaths.
| Community Action Categories | Change in Movements According to Baseline | Average Number of Deaths | p valuea |
|---|---|---|---|
| Supermarket and Pharmacy | 40% and above b,d | 165.68 ± 75.106 | 0.000 |
| 20%–39.99% e | 141.10 ± 79.483 | ||
| 1%–19.99% a,b | 86.11 ± 88.566 | ||
| 0% - (−19.99%) a,c | 157.05 ± 88.454 | ||
| (-20%) - (−39.99%) c,d | 77.66 ± 32.930 | ||
| (-40%) and less f | 93.00 ± 5.657 | ||
| Retail and Recreation | 40% and above | 0 | 0.000 |
| 20%–39.99% | 0 | ||
| 1%–19.99% c,e | 177.80 ± 72.559 | ||
| 0% - (−19.99%) a,b,c | 41.77 ± 24.869 | ||
| (-20%) - (−39.99%) a,d,e | 91.32 ± 67.489 | ||
| (-40%) and less b,d | 150.07 ± 97.332 | ||
| Parks | 40% and above a,b | 104.89 ± 96.857 | 0.000 |
| 20%–39.99% c | 110.83 ± 80.839 | ||
| 1%–19.99% d | 133.45 ± 62.691 | ||
| 0% - (−19.99%) b | 157.62 ± 97.173 | ||
| (-20%) - (−39.99%) a,c | 157.32 ± 96.044 | ||
| (-40%) and less e | 98.92 ± 63.418 | ||
| Workplace | 40% and above | 0 | 0.000 |
| 20%–39.99% | 0 | ||
| 1%–19.99% b,d | 181.32 ± 64.893 | ||
| 0% - (−19.99%) a,b | 99.85 ± 79.720 | ||
| (-20%) - (−39.99%) a,c | 165.68 ± 99.528 | ||
| (-40%) and less c,d | 102.38 ± 81.378 | ||
| Public Transport | 40% and above | 0 | 0.000 |
| 20%–39.99% c,f,g,h | 187.72 ± 64.052 | ||
| 1%–19.99% d,e,f | 64.88 ± 42.530 | ||
| 0% - (−19.99%) a,b,c | 65.02 ± 47.751 | ||
| (-20%) - (−39.99%) b,e,h | 147.42 ± 106.097 | ||
| (-40%) and lower a,d,g | 125.23 ± 90.580 | ||
| Residential | 40% and above | 0 | 0.141 |
| 20%–39.99% | 95.87 ± 59.285 | ||
| 1%–19.99% | 128.54 ± 89.805 | ||
| 0% - (−19.99%) | 120.91 ± 95.365 | ||
| (-20%) - (−39.99%) | 0 | ||
| (-40%) and less | 0 |
Kruskal Wallis Test Note: The statistically significant difference was found between variables with the same upper letter due to post-hoc analysis.
4. Discussion
Severe restrictions were introduced to reduce virus-related illnesses and virus-related deaths. Consequently, assessing how regulations affect the virus's spread, hospitalizations, and deaths is crucial.14 A modeling study estimated that approximately 3.1 million deaths had been prevented in 11 countries thanks to the restrictions since the beginning of the epidemic.8 In a simulation model, it was shown that social distancing could reduce the rate of spread of COVID-19 as well as death rates.15 COVID-19 studies have shown that quarantine, contact tracing, screening, and isolation are beneficial. Estimates of modeling studies have shown that quarantining people exposed to the viral agent prevents 44%–81% of cases and 31%–63% of deaths.16 In a study conducted in the United States, a 3% increase in the intensity of social distance caused a 1% increase in the number of cases; On the other hand, an 11% increase in social distance intensity also increased the number of deaths by 1%.17 Similar to the literature, in our study, an increase in cases and death were detected in cases where the social distance rule could not be applied. The most significant association for both cases and death was with supermarkets and pharmacies (r:0.46 and r:0.28, respectively). Then, as a solution to this situation in Turkey, strict restrictions were imposed on the population over 65, and the authorities met their market and drug needs.
It has been seen that practicing and increasing social distancing has led to a considerable decrease in the spread of infection and deaths. It has been shown that infection rates are significantly reduced when social distancing is implemented between 80% and 100%.15 When social distancing is 0%, the death rate is much higher as COVID-19 more quickly. The optimal level of social distancing intervention was at least 80% for a minimal spread of infection and deaths.15 One study reported the total number of COVID-19 cases after the introduction of social distancing measures; A decrease of approximately 1,600 cases occurred after a week, 56,000 cases after two weeks, and 621,000 cases after three weeks.5 In another study, the increase in COVID-19 cases decreased statistically (p < 0:01) after social distancing restrictions. 4.5% after 6–11 days, 5.9% after 11–15 days, and 8.6% after 16–21 days.6 The oldest 20% of the population accounted for about 15% of cases, unfortunately, about 90% of deaths.14 Each day school closures were delayed, the risk of death from COVID-19 increased by 5%–6%.7 These two situations should be taken into account when determining the policy. In Turkey, schools were suspended as of March 16, 2020. Due to the holiday of schools, school mobility has decreased, but it has caused an increase, especially in residential mobility. Increased indoor mobility may be related to the increase in cases and deaths.
A change point analysis with a Bayesian probability approach to measuring the effectiveness of the restrictions imposed due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in eight countries.3 Countries that are late initiating measures have needed help to control the increase in cases.3 In a study covering 27 European countries where social distancing policies affected the spread of the disease, the most significant reductions were in mandatory home quarantine, followed by the closure of businesses and schools.18 A study conducted in Taiwan found that intra-city travel restrictions were more effective in epidemic control than inter-city travel restrictions.19 A web-based viral reproduction prediction application developed in Malaysia has shown a significant increase in the number of people with the disease after the events in which many people participate in closed areas.20 In a study conducted in Indonesia, the most significant increase in SARS-CoV-2 cases was seen in the pharmacy and grocery sector, with 4.12%.21 A large-scale study with data from 86 countries has shown that implementing lockdowns and reducing population mobility in workplaces, public transport, and retail-recreation sectors is very effective in preventing the spread of the epidemic.21 In Turkey, after the Ramadan Feast at the end of May 2020, housing mobility decreased, and other activities increased, but cases and deaths remained relatively stable. The fact that the increase in the number of cases and deaths was less than expected may have resulted from the mandatory mask application.
According to Durmus et al.,22 social distancing measures have reduced human mobility, and the effective reproduction number of COVID-19 has decreased significantly, preventing many people from becoming infected. Many factors affect the number of COVID-19 cases and deaths (screenings, contact tracing, social distance measures, vaccination studies, etc.). Our study determined that the change in community movements affected COVID-19 cases and deaths. Cases and deaths from COVID-19 increase, especially with indoor and public transport, and decrease with outdoor mobility.
5. Conclusions
Screening, contact tracing, quarantine, and isolation measures effectively prevent COVID-19 when applied together; adding vaccines to these measures after they were developed increased the effectiveness of epidemic management. Social distancing measures (such as reducing community mobility) and training on the agent and transmission routes in possible epidemics will save us time developing new diagnostic tests and vaccine studies. In our study, the mobility variables were insufficient to predict the number of cases and deaths. Also, some mobility categories may be interrelated (for example, a curfew can increase residential mobility while reducing park and outdoor mobility). However, we think that early and comprehensive social distancing practices and restriction of community movements are feasible and necessary to reduce COVID-19 cases and deaths.
Funding/support
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author statement
Conceptualization: MAG, OH.
Data curation: MAG, OH.
Formal analysis: MAG, OH.
Funding acquisition: None.
Methodology: MAG, OH.
Visualization: MAG, OH.
Writing - original draft: MAG, OH.
Writing - review & editing: MAG, OH.
Ethical approval statement
This study was approved by the Republic of Turkey Ministry of Health COVID-19 Scientific Research Evaluation Commission (Approval Number: 2021-12-17T14-30-48).
Declaration of competing interest
The author(s) received no financial support for this article's research, authorship, and/or publication. The authors have no conflict of interest associated with the material presented in this paper to declare.
Acknowledgments
None.
Footnotes
This study aimed to determine whether there is a correlation linking Covid-19 cases and deaths because of Covid-19 and community movements in Turkey and to develop a strategy for future outbreaks.
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