Smart City Smart City Volume 1 Issue 1 Article 4 November 2021

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Issue 1 Cities and Shifts: Socio-Economic Changes During and After the COVID-19 Pandemic

Article 4

November 2021

THE SPATIAL EFFECTS OF ELDERLY POPULATION PRESENCE ON THE SPATIAL EFFECTS OF ELDERLY POPULATION PRESENCE ON COVID-19 INCIDENCE IN DKI JAKARTA BEFORE, DURING, AND COVID-19 INCIDENCE IN DKI JAKARTA BEFORE, DURING, AND AFTER LARGE-SCALE SOCIAL RESTRICTION

AFTER LARGE-SCALE SOCIAL RESTRICTION

Chotib Chotib

Universitas Indonesia, Depok, Indonesia, chotib@hotmail.com I G A A Karishma Maharani Raijaya Mrs.

Master Program in Population and Labor Economics (MEKK), Economic and Business Faculty, Universitas Indonesia, Depok, Indonesia, iga.karishma@ui.ac.id

Ahmad Aki Aki Muhaimin Mr.

Department of Civil Engineering, The University of Tokyo, Tokyo, Japan, ahmadaki@iis.u-tokyo.ac.jp Novani Saputri

Research Cluster on Energy Modeling and Regional Economic Analysis, Economic and Business Faculty, Universitas Indonesia, Depok, Indonesia, novani.karina81@ui.ac.id

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Recommended Citation Recommended Citation

Chotib, Chotib; Raijaya, I G A A Karishma Maharani Mrs.; Aki Muhaimin, Ahmad Aki Mr.; and Saputri, Novani (2021) "THE SPATIAL EFFECTS OF ELDERLY POPULATION PRESENCE ON COVID-19 INCIDENCE IN DKI JAKARTA BEFORE, DURING, AND AFTER LARGE-SCALE SOCIAL RESTRICTION," Smart City: Vol. 1:

Iss. 1, Article 4.

DOI: 10.56940/sc.v1.i1.4

Available at: https://scholarhub.ui.ac.id/smartcity/vol1/iss1/4

This Article is brought to you for free and open access by the Universitas Indonesia at UI Scholars Hub. It has been accepted for inclusion in Smart City by an authorized editor of UI Scholars Hub.

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THE SPATIAL EFFECTS OF ELDERLY POPULATION PRESENCE

ON COVID-19 INCIDENCE IN DKI JAKARTA BEFORE, DURING, AND AFTER LARGE- SCALE SOCIAL RESTRICTION

ABSTRACT

Infected cases and suspect cases of covid-19 are increasing more and more daily. This increment happens either in whole regions of Indonesia and DKI Jakarta as a capital city. The purpose of this research is to seek the pattern in spatial of Covid-19 incidence with 3 different periods of before, during, and after large-scale social restriction, and to identify the influence of the presence of the elderly and other factors. One of the scopes of this study is the presence of the elderly because the elderly population is considered as influencing the increase of Covid-19 incidence. The analysis method used in this research is spatial analysis. Novel findings show that spatial pattern change in 3 periods of observation where clusterization of Covid-19 is more intensive, the presence of elderly is a more significant influence to the transmission of Covid-19. Also, there are spatial effects towards the influence of elderly to the spread of Covid-19. The other variables such as the number of traditional markets and population density initially insignificant turn out to be significant in the second and third period.

Keywords: Covid-19, Elderly Population, Spatial Analysis, social restriction INTRODUCTION

After the initial emergence in December 2019 in Wuhan, China (WHO, 2020a), and continued the outbreak of novel coronavirus disease (Covid-19) or SARS-CoV-2 becoming a global health concern (Lai, 2020). Currently, the number of cases has reached more than 177 million and exceeded 3 million deaths in over 200 countries as of 22 June 2021, in fact it has worsened and evolved into several variants (WHO, 2021). In Indonesia, the active cases and suspect cases of Covid-19 show a rapid increase particularly in the capital city, Jakarta (COVID- 19 Task Force, 2021). To tackle that, the government has implemented several countermeasures (Putri, 2020; Suraya et al, 2020).

One of the countermeasures is Pembatasan Sosial Berskala Besar or large-scale social restrictions, abbreviated as PSBB, started on 10 April 2020 in National (Ministry of Health, 2020) and in DKI Jakarta (Jakarta Government Communication, Informatics, and Statistics Department, 2020). The citizens in Jakarta should follow the governor regulation Number 33 year 2020 restricting activities in crowded areas including schools, hospitals, workplaces, worship places, markets, public transportations (PPID DKI Jakarta, 2020). In particular schools, workplaces, public transportations, markets and worship places were shutting down so that, students studied through online course, and people worked or prayed from their house; dine-in and public gatherings are prohibited so that take-away food and online shop were encouraged; people prohibited to go other cities by closing the toll road or border between neighboring cities; and government enforced a ‘stay at home’ policy (Suraya et al, 2020). However, this regulation achieved the undesirable result because it did not show the reduction of Covid-19 cases, but the incidence rates tend to increase in Indonesia (Dinas Kesehatan Pemerintah DKI Jakarta, 2021).

Thus, the influential factors causing the escalation of incidence rates during PSSB should be identified. Many studies have been done to seek what are the influential factors of Covid-19 globally including temperature (Tosepu et al, 2020; Briz-Redón & Serrano-Aroca, 2020; Prata et al, 2020; Mofijur et al, 2020; Ahmadi et al, 2020; Şahin, 2020; Ma et al, 2020; Sharma et al, 2020;

Xie & Zhu, 2020; Bashir et al,2020), age (Zhou et al, 2020; Liu et al, 2020; Lee, 2020; Iaccarino

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et al, 2020; Imam, 2020; Röhr & Reininghaus, 2020; Kivi et al, 2021; Lakhani, 2020), mobility (Tamagusko & Ferreira, 2020; Badr, 2020; Xiong et al, 2020; Lee et al, 2020). Also, the scope of study area varies from small area (number of patients in healthcare facilities) to large area (country or global).

DKI Jakarta, as the capital city of Indonesia, is the center of the outbreak of Covid-19 in Indonesia (Elfriede, 2020). On 27 March 2020, the confirmed cases of Covid-19 in Indonesia was over 1000 with more than 50% from DKI Jakarta. The active cases and deaths in Jakarta due to Covid-19 were higher than national cases with around 3% difference as shown in Table 1. After 1 months, the incidence rate increased tenfold resulting in the government acting to establish regulation called PSBB (Jakarta Government Communication, Informatics, and Statistics Department, 2020; Ministry of Health, 2020). During PSBB on 20 May 2020 ( see Table 2) and post PSBB on 8 September 2020 as shown in Table 3, although the incidence rates increased nationally, but the incidence rates inclined in Jakarta in which during PSSB (Table 2), over 30%

active cases in Indonesia came from Jakarta with mortality higher than nation cases (8% versus 6%) and the percentage of recovered cases is lower than nation cases (23% versus 24%) while after PSBB (Table 3), the incidence rate decreased about 6% together with the mortality is lower than nation cases (2.7% versus 4.1%) and the recovery rates is higher than nation cases (74.7%

versus 71.5%).

Table 1. Confirmed cases of Covid-19 Indonesia and Jakarta on March 27, 2020. Source:

corona.jakarta.go.id

Type of Cases Indonesia Jakarta

Total Percentage (%) Total Percentage (%)

Confirmed 1046 566 54.0

Active 864 82.6 478 84.5

Recovered 46 4.4 31 5.5

Death 136 13.0 57 10.0

Table 2. Confirmed cases of Covid-19 Indonesia and Jakarta on May 20, 2020. Source:

Confirmed 19189 6150 32.0

Active 13372 70.0 1969 32.0

Recovered 4574 24.0 1425 23.0

Death 1242 6.0 493 8.0

Table 3. Confirmed cases of Covid-19 Indonesia and Jakarta on Sept 8, 2020. Source:

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Confirmed 200035 48811 24.4

Active 48847 24.4 4432 9.1

Recovered 142958 71.5 36451 74.7

Death 8230 4.1 1330 2.7

The suspect, close contact, and confirmed cases of Covid-19 in Jakarta are almost equally in both genders as shown in Figure 1. Hence, Covid-19 spread in Jakarta is not single out in one gender. The number of suspects, close contacts, and confirmed cases in both genders seem higher in the age 19 to 38 years old because the number of people in age 24 to 39 years old (Millennial generation) is the most dominant in Jakarta (Badan Pusat Statistik, 2020). The elderly age (>59 years old) shows higher numbers compared to younger age (5 - 18 years old). The tendency seems that the older, the higher the possibility of suspect, close contact, or confirmed cases of Covid-19.

Moreover, there have been many clinical studies about relation between elderly age and Covid-19 incidence finding that elderly age patients have high positivity rate and mortality rate due to the weak physical condition and comorbid condition (Zhou et al, 2020; Liu et al, 2020; Lee, 2020).

However, the research about relation of elderly age population increase the Covid-19 incidence in one area to neighboring area is limited.Thus, the influence of age particularly between older and younger age to Covid-19 incidence rate particularly in spatial interests further study to obtain the information and knowledge about the Covid-19 incidence particularly during large-scale social restriction in Jakarta .

Figure 1. Confirmed Cases of Covid-19 in Jakarta by Gender and Age, 30 June 2021 Source: Jakarta Government Health Department

This research aims to provide the spatial design correlated to the COVID-19 incidence in DKI Jakarta during PSBB including before and after PSBB. Also, identification of spatial effect

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along with influences of elderly age population and other factors in the COVID-19 incidence in three different periods (before, during, and after PSBB). By knowing the spatial pattern of the spread of covid-19 over time, the next spatial spread of covid-19 is expected to be predictable.

LITERATURE REVIEW

There are many medical texts have not defined the definition of “Pandemics” even theoretically or quantitatively (Singer et al, 2021; Qiu et al, 2017). The Dictionary of Epidemiology defined the Pandemics as “an epidemic occurring worldwide, or over a very wide area, crossing international boundaries and usually affecting a large number of people” (Porta, 2008; Harris 2000), while a World Health Organization (WHO) determines the pandemic as “the worldwide spread of a new disease” (WHO, 2010a). Many pandemics or disease outbreaks recorded in human history, such as dengue, AIDS, influenza, smallpox, cholera, plague, severe acute respiratory syndrome (SARS) (Qiu et al, 2017). Each pandemic influenced human life and economic development such as the “Spanish flu” in 1918-1919 has been recorded as the most devastating epidemic in world history caused 20 million people died in the world (WHO, 2011). Due to this issue, World Health Organization provided the guidance for pandemic influenza preparedness and response (WHO, 1999; WHO, 2005; WHO, 2010b).

The guidelines revised several times by changing the number of phases or periods or revised and redefines the key principles of pandemic phases. The diseases are categorized as pandemics according some key features such as wide geographic extension, disease movement, novelty, severity, high attack rates and explosiveness, minimal population immunity, and infectiousness and contagiousness (Qiu et al, 2017).

Today, the current of Covid-19 pandemics surpass other pandemics in over the past 2 decades in several aspects. In terms of death toll, Covid-19 is the highest mortality compared to SARS (770 deaths), Swine Flu (200000 deaths), MERS (850 deaths), and Ebola (11300 deaths) (Pitlik, 2020). Although the case fatality of the current Covid-19 is lower than other coronavirus disease such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome MERS, but the number of Covid-19 cases have outnumbered the both SARS and MERS. Severe pneumonia is the salient clinical features in SARS, MERS, and Covid-19, but, Covid-19 have other clinical features such as silent anoxia, anosmia, ageusia, toe lesions mimicking chilblains, pediatric multisystem inflammatory syndrome (Pitlik, 2020). Moreover, the R0 or “R naught” as a mathematical term that determines the infectiousness and contagiousness, of current Covid-19 is similar to Spanish flu in (2-2.5) and surpass SARS (0.19-1.08) and MERS (0.3-0.8) (Liang, 2020;

Pitlik, 2020; WHO, 2020b).

There are also several similarities between Covid-19 and influenza pandemic such as disease presentations and transmission from viruses by contact, droplets and fomites (WHO, 2020b; Liang, 2020). However, the virus transmission in the community between two diseases are different where children are the main drivers of influenza virus while adults are the important drivers for Covid-19 even the preliminary data found that children are infected from adults, rather than vice versa. (WHO, 2020b). Moreover, children, pregnant women, elderly, and those with chronic condition are most at risk for severe influenza while elderly age is the most at risk for Covid-19 (WHO, 2020b).

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There are several researches about the relation between elderly age population and Covid- 19 incidence particularly studies clinical features in several countries. Not only elderly age as independent variables, but also relation of elderly age to mortality, comorbidity, and mental wellbeing during Covid-19 such as in China (Zhou et al, 2020; Liu et al,2020), Korea (Lee et al, 2020), Italy (Iaccarino et al, 2020), USA (Imam et al, 2020), Germany (Röhr & Reininghaus, 2020), Sweden (Kivi et al, 2021), and Australia (Lakhani, 2020) have been investigated. However, most studies seek the correlation using statistical analysis with small coverage area and number of samples while limited studies using spatial analysis (Lakhani, 2020).

Spatial data analysis is an analysis that always considers the effect of location on earth. In the spatial analysis, it can be seen that there is an interdependence of locations that are close to each other (neighboring). To test whether there is interdependence in this case, several methods of spatial analysis through Explanatory Spatial Data Analysis (ESDA) are used, such as spatial autocorrelation with Moran's I and LISA (Local Indicator Spatial Association).

Spatial autocorrelation is a fundamental technique in ESDA which aims to determine the location and location of spatial clusters of an area by taking into account spatial distribution patterns, and identification of dissimilar areas or outliers. In other words, spatial autocorrelation is a measure of the similarity of objects in space, both globally and locally (Anselin, 1999).

LISA is a statistical measure that is often used to determine the size of local spatial autocorrelation. LISA provides identification of cluster locations and their level of significance through the Global Moran's I (Anselin, 1995) indicator.

In terms of spatial analysis to Covid-19 pandemic, there are five current topics and methods of spatial subject related to Covid-19 pandemic (Pardo et al, 2020) such as spatio-temporal analysis [10], health and social geography (Lakhani, 2020), environmental variables (Tosepu et al, 2020), data mining (Allcott, 2020), and web-based mapping (Maged N.K.B & Estella, 2020). There also has been several studies about the effectiveness of the policy in several countries to counter measures the spread of Covid-19 such as the implementation of social distancing, strict health protocol, lockdown, and large-scale social restriction (Chang et al, 2021; Zhang et al, 2021;

Bonacin & Patriarca, 2021). Also, the after effect of the health protocol policy to prevent the spread of Covid-19 towards the human mobility (Chow et al, 2021) and mental well-being of the resident (Röhr & Reininghaus, 2020; Singh et al, 2020).

METHODS

This study aims to analyze the influence of elderly population toward the incidence rates of Covid-19 in DKI Jakarta particularly during PSBB. Using a spatial analysis method, the study investigated the Covid-19 data in three periods including pre-implementation, during

implementation, and post-implementation. The starting period began on 29 March 2020, then the mid-period which was the implementation of PSBB on 16 May 2020, and the end period or the post-period of PSBB on 29 August 2020.

The probability of death from being infected by corona virus is highest in the elderly population. Signorelli and Odone (2020) reported that the older a person is, the higher the case fatality rate. They proved the CFR pattern by age groups from three different times, April 16;

June 16; and 18 August, seems consistent over time. The lowest case fatality rate was at the age of 0-19 years (range at 0.0 - 0.1 percent%), then increased with increasing age. The highest case

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fatality rate is at the age of 80 years and over (ranging from 28 to 34 percent). However, this interesting finding has not shown spatial effect in their analysis. To fill this research gap, this study carried out the spatial effect of the presence of elderly people in the spread of COVID-19.

Spatial regression models are used to investigate how one region impacts surrounding region and/or neighborhood region and vice versa. spatial pattern is not able to explain if only using statistical analysis from this phenomenon (Cressie, 1991). In the econometrics test, there are several steps which should be followed. Before the spatial regression analysis, the first step is to test the classical regression models to determine which model is used in this study, it is better to use the classical regression model or spatial analysis regression seen from the Akaike Information Criterion (AIC) value and the Schwarz Criterion (SC) value. If the AIC and SC values are smaller in spatial regression analysis when compared to classical regression models, then the appropriate regression analysis model used in this study is spatial regression analysis, and vice versa. Based on Dona & Setiawan (2015), this model is applied to seek the relationship between dependent and independent variables in the area. The significant value for 𝛼 = 5 %, by hypothesis as follows:

𝐻0 ∶ βk = 0 (no relationship between independent variables and dependent variable)

𝐻1 ∶ βk ≠ 0 (there are relationship between independent variables and dependent variable)The classical regression model equation is as follows (LeSage, 1999)

𝑌𝑖 = 𝛽0 + 𝛽1𝑋1𝑖 + 𝛽2𝑋2𝑖+ … + 𝛽𝑝𝑋n𝑖+ u𝑖 (1)

Annotation:

𝑌 : Dependent variable

𝛽0 : Constant

X1, X2,...,𝑋n : Independent variable

𝛽1,𝛽2,...,𝛽p : Regression Coefficient Value

u𝑖 : Residual

Moran’s I Test

After classical regression model test, value from Moran’s I is generated. By hypothesis as follows:

𝐻0 ∶ I = 0 (no correlation between locations)

𝐻1 ∶ I ≠ 0 (there is autocorrelation between locations) Local Indicator of Spatial Association (LISA) Index

Local Indicator of Spatial Association index or LISA index is able to identify the existence of the spatial value category in a nearby area. Hypothesis of LISA index as follows:

H0: There is no local spatial autocorrelation H1: There is local spatial autocorrelation

In a statistical test of LISA index value, there are four (4) cluster values observed in neighboring areas. The four cluster values are the first quadrant (High-High), the second quadrant (Low-High), the third quadrant (High-Low), and the fourth quadrant (Low-Low). The first quadrant or High-High indicates that observation location has high observed value and close to the neighbor area having high observed value too. The second quadrant or Low-High indicates the observation location has low observed value and close to the neighbor area having high observed value. The third quadrant or High-Low indicates the observation location has high observed value and close to the neighboring area having low observed value. Lastly, the fourth quadrant or Low-

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Low indicates that observation location has low observed value and close to the neighbor area having low observed value too (Hamdan, 2019).

LM-Lag test and LM-Error test

p-value from LM (Lagrange Multiplier) Lag and LM-Error also will appear. LM-lag value might be compared to LM-Error value. The most significant value is determined if the p-value gets near to 1% or 0.01. However, the significant value generally used is 𝛼 = 5%. After the most significant value is known, then the method can be continued to choose either using spatial lag models or spatial error models.

A model is said to have a spatial error if there is a linear relationship between the error or is no homoscedasticity, and if a model is said to have a spatial type of spatial lag dependency if there is a linear relationship between the errors, and the dependence between the observed values is added to an area with the same value. in the nearest area. This is because there is a relationship between the independent variable and the dependent variable in adjacent areas.

The Spatial Effect

Spatial effect examines the presence of neighboring effect externality. The concept of neighboring effect area in geographical study initially introduced by Waldo R. Tobler (1970) stated that “everything is related to one another, but something close has more influence than something further” (Tobler, 1970). The steps for diagnosing spatial effects are depicted in the diagram below:

Figure 2. Framework Spatial Lag Model Diagnostics and Spatial Error Model Source: Anselin and Rey (2014).

To apply Figure 2 above, the analysis in this study uses GeoDa, a software that is very user friendly in applying spatial analysis. The decision-making process starts from the top box, namely the OLS

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regression test by considering standard statistical tests (for example in forms that are not robust) through the Lagrange Multiplier-Error (LM-Error) test and the LM-Lag test. If the null hypothesis is not rejected (meaning nothing is significant), then the decision remains on the results of the OLS regression. It is possible in this case, Moran's I statistical test will not reject the null hypothesis (meaning significant).

Spatial Error Model

𝑦 = 𝑋𝛽 + 𝜀 (2)

𝜀 = 𝜆𝑊𝜀 + 𝑢 (3)

If one of the LM tests rejects the null hypothesis, and the other does not, then the decision is easy, namely to estimate through an alternative spatial regression model that matches the statistical test that rejects the null hypothesis (which is significant). So, if the LM-Error rejects the null hypothesis, but the LM-Lag does not reject it, then estimation is conducted through the spatial error model, and vice versa.

If both LM tests reject the null hypothesis, then proceed to the bottom of the figure and consider the robustness test of both LMs. Usually only one of the two is significant (as in Figure 2), or one LM will be more significant than the other (eg p<0.000000 compared to p<0.03). If the LM-Error is more robust, then do a regression test with the Spatial-Error model. Likewise, if LM-Lag is more robust, then do a regression test using the Spatial-Lag model.

Spatial Lag Model

𝑦 = 𝜚𝑊𝑦 + 𝑋𝛽 + 𝜀 (4)

ANALYTICAL FRAMEWORK

As shown in Figure. 3, this study analyzes 3 (three) different observation periods to identify the spatial pattern on the spread of Covid-19 in DKI Jakarta, identify spatial propagation in 3 different periods, identify the influence of the elderly population (as the main independent variable) and other factors on the spread of Covid-19, and identify the spatial influence of the elderly population and other factors. 3 (three) periods of observation:

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Figure 3. Analytical Framework

The operational definitions of variables in this study are summarized in Table 4 below:

Table 4. Operational Definition of Variables No Variable

Name

Role in the Model Operational Definition Type of Data 1 Covid-

19_March

Dependent Variable for Period of March 2020

Number of people who have been confirmed positive for COVID-19 infected on 29 March 2020 in kelurahan

Inteval

2 Covid-19_May Dependent Variable for Period of may 2020

Number of people who have been confirmed positive for COVID-19 infected on 16 May 2020 in kelurahan

Inteval

3 Covid-19_Aug Dependent Variable for Period of August 2020

Number of people who have been confirmed positive for COVID-19 infected on 29 August 2020 in kelurahan

Inteval

4 The Elderly Population (60+)

Independent Variable for three periods of time

Number of elderly people in kelurahan for three periods of time

Interval

5 Market Independent

Variable for three periods of time

Number of markets in kelurahan for three periods of time

Interval

6 Population Density

Number of people per kelurahan area (Number of population/Ha)

Ratio

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7 Building Density

Number of buildings per kelurahan area (number of building/Ha)

Ratio

8 Productive Age Population

Number of population age 15-59 years old in kelurahan

Interval

RESULTS DAN DISCUSSION

The administrative city of West Jakarta became the area with the highest number of Covid- 19 distributions in the first observation period (29 March 2020), which was 604 cases, which was then followed by East Jakarta as the area with the highest number of Covid-19 distributions in the second observation period (16 May 2020) with a total of 604 cases. 988 cases and the third (29 August 2020) with 4971 cases. The largest increase in cases was in urban villages in the Main Jakarta area with changes in the addition of cases of 76.85% in the second observation period and 484.63% changes in the addition of cases in the third observation period (Table 4.).

In the first observation period, the villages of Kamal, Kebon Kelapa, Kuningan Barat, Rawa Terate, and Koja became urban villages with 0 additional cases. In observation period 2, the villages of Duri Selatan, Gambir, Kuningan Barat, Ceger, and Koja became the villages with the lowest number of additional cases with 0-4 new cases. While in observation period 3, the villages of Roa Malaka, Cikini, Melawai, Ceger, and Kamal Muara became the villages with the lowest number of additional cases, namely 4-21 cases.

In observation period 1, the Petamburan sub-district, Central Jakarta, became an urban village with the number of additional Covid-19 cases, followed by the Sunter Agung sub-district, North Jakarta, and Pondok Kelapa, East Jakarta. In observation period 2, Sunter Agung, North Jakarta became the villages with the highest number of additional Covid-19 cases of 135 new cases, followed by Petamburan, Central Jakarta, and Maphar, West Jakarta. While in observation period 3, Pademangan Barat, North Jakarta became the urban village with the highest number of additional Covid-19 cases of 417 new cases, followed by Cempaka Putih Barat, Central Jakarta, and Palmerah, West Jakarta.

Table 5. Statistics Summary of Covid-19 Distribution by Region and Time

CitY Statistics

Cov_

Mar

Cov_

Mei

Cov_A

ug Cov_Maret Cov_Mei Cov_Agust

JAKARTA BARAT

Sum 604 934 4440

Mean 10.79 16.68 79.29

Median 9.50 14.50 71.50

Minimum 0 0 4

Kamal/Kalideres Duri Selatan/Tambora Roa Malaka/Tambora

Maximum 39 55 192

Pegadungan/Kalideres Maphar/Tamansari Palmerah/Palmerah Std. Deviation 8.829 13.039 43.865

# of Kelurahans 56 56 56

JAKARTA PUSAT

Sum 549 823 4591

Mean 12.48 18.70 104.34

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Median 7.00 12.00 88.00

Minimum 0 1 20

Kebon Kelapa/Gambir Gambir/Gambir Cikini/Menteng

Maximum 88 123 271

Petamburan/Tanah Abang Petamburan/Tanah Abang

Cemp. Putih Brt/Cemp. Putih Std. Deviation 14.571 22.121 60.382

JAKARTA SELATAN

Sum 525 826 3880

Mean 8.08 12.71 59.69

Median 6.00 12.00 55.00

Minimum 0 0 6 Kuningan Brt/Mampang

Prapatan

Kuningan Brt/Mampang

Prapatan Melawai/Keb. Baru

Maximum 28 37 157

Pondok Pinang/Keb. Lama Pondok Pinang/Keb. Lama Tebet Barat/Tebet Std. Deviation 6.288 7.877 35.302

JAKARTA TIMUR

Sum 589 988 4971

Mean 9.06 15.20 76.48

Median 7.00 12.00 74.00

Minimum 0 1 12

Rawa Terate/Cakung Ceger,/Cipayung Ceger,/Cipayung

Maximum 41 49 196 Pondok Kelapa/Duren

Sawit Pondok Kelapa/Duren Sawit

Pondok Kelapa/Duren Sawit

Std. Deviation 8.170 10.396 37.481

JAKARTA UTARA

Sum 445 787 4601

Mean 14.35 25.39 148.42

Median 9.00 18.00 124.00

Minimum 1 4 21

Koja/Koja Koja/Koja

Kamal

Muara/Penjaringan

Maximum 58 135 417 Sunter Agung,/Tanjung

Priok Sunter Agung,/Tanjung Priok

Pademangan Brt/Pademangan Std. Deviation 14.291 28.750 88.417

DKI JAKARTA

Sum 2712 4358 22483

Mean 10.39 16.70 86.14

Median 7.00 13.00 74.00

Minimum 0 0 4 Kamal, Kb Kelapa,

Kuningan Brt, Rw Terate Duri Sel., Kuningan Brt Roa Malaka/Tambora

Maximum 88 135 417

Petamburan/Tanah Abang Sunter Agung/Tanjung Priok

Pademangan Brt/Pademangan Std. Deviation 10.284 16.382 57.371

Source: Author

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Figure 4. Comparison of the Distribution of Covid-19 by Village at Three Different Times Source: Author.

There has been an increase in the average number of Covid-19 distributions in each analysis area, especially in the post-PSBB period. Only a few areas still look safe, namely Roa Malaka, Melawai, Glodok, Karet Semanggi, Pulo, and so on. Most of the area is dominated by the North Jakarta area with an average distribution of more than 100 cases per day in the third observation period. This significant increase in distribution could be due to the occurrence of high internal mobility of the region, as well as the lack of compliance with health protocols which led to a high increase of Incidence rate of Covid-19.

The variation of summary statistics by cities in DKI Jakarta for each independent variable used in the analysis of this study is presented in Table 6 below:

Table 6. Statistics Summary of Independent Variables by City

City Statistics

The Elderly

Pop.

Market Population Density

Building Density

Productive Age Population

JAKARTA BARAT

Sum 117223 92 1781257

Mean 2093.27 1.64 26368.80 39.35 31808

Median 1973 1 21584.50 31.14 26489

Minimum 357 0 6218.00 19.29 2472

Maximum 4458 8 65649.00 94.07 123071

Std. Deviation 938.49 1.66 14483.83 17.99 21677.91

# of Kelurahan 56 56 56 56 56

Sum 65459 57 683492

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JAKARTA PUSAT

Mean 1487.70 1.30 26547.09 34.50 15534

Median 1530 1 22138.50 30.24 16022

Minimum 233 0 946.00 2.07 2161

Maximum 2804 5 77696.00 101.51 31348

Std. Deviation 593.76 1.00 18290.61 22.54 7196.37

# of Kelurahan 44 44 44 44 44

JAKARTA SELATAN

Sum 122255 43 1564458

Mean 1880.85 0.66 16731.15 26.96 24069

Median 1971 0 15466.00 26.05 23275

Minimum 242 0 2815.00 5.50 2800

Maximum 4650 3 43606.00 57.86 51625

Std. Deviation 956.80 0.78 8724.26 11.93 12761.94

# of Kelurahan 65 65 65 65 65

JAKARTA TIMUR

Sum 142825 55 2020545

Mean 2197.31 0.85 19841.22 34.98 31085

Median 2059 1 17618.00 33.46 26987

Minimum 833 0 2663.00 6.46 6711

Maximum 4219 5 76058.00 80.51 79783

Std. Deviation 968.37 1.05 11380.41 14.97 15700.00

# of Kelurahan 65 65 65 65 65

JAKARTA UTARA

Sum 81263 47 1231800

Mean 2621.39 1.52 20664.71 27.70 39735

Median 2133 1 14903.00 21.95 35726

Minimum 693 0 1396.00 4.98 11337

Maximum 5198 4 93479.00 80.26 89397

Std. Deviation 1228.44 1.26 18751.40 19.58 17008.17

# of Kelurahan 31 31 31 31 31

DKI Jakarta

Sum 529025 294 7281552

Mean 2026.92 1.13 21695.54 32.97 27899

Median 1912 1 17517.00 29.74 25148

Minimum 233 0 946.00 2.07 2161

Maximum 5198 8 93479.00 101.51 123071

Std. Deviation 989.85 1.23 14378.01 17.59 17152.06

# of Kelurahan 261 261 261 261 261

Source: Author

LISA and Moran’s I in Three Different Times Initial Period: March 29, 2020

The graph shows the existence of clusters because concentrated points are gathered in the middle of all quadrants.

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Figure 5. LISA Moran’s I : March 29, 2020 Source: Author.

In the first observation period (29 March 2020), the value of Moran's I was 0.15 and 212 villages were identified as insignificant. There are 14 urban villages with 4 clusters that fall into the quadrant I (high-high) category, which means that the 14 villages have a high number of Covid- 19 cases and are surrounded by areas with a high number of Covid-19 distributions. The four clusters are; The Kelapa Gading Cluster and its surroundings, the Pondok Bambu Cluster and its surroundings, the Palmerah Cluster and its surroundings, and the Lebak Bulus Cluster. The 20 sub- districts with 6 clusters fall into the quadrant IV (low-low) category, which means that the 20 sub- districts fall into areas with a low number of Covid-19 distributions and are surrounded by areas with a low number of Covid-19 distributions. These areas include; Duri Utara Cluster and its surroundings, Melawai Cluster and its surroundings, Rambutan Cluster and its surroundings, Bambu Apus Cluster, Cilangkap Cluster, and Kwitang. The 12 sub-districts with 8 other clusters fall into the quadrant II (low-high) category, which means that the 12 urban villages have a low number of Covid-19 distributions but are surrounded by areas with a high number of Covid-19 distributions. The eight clusters include; Cengkareng Barat, Ancol, Sungai Bambu and its surroundings, Sumur Batu, Rawa Sari, Bendungan Hilir and its surroundings, Malaka Sari and its surroundings, and Kota Bambu Selatan and its surroundings. Five sub-districts with 5 other clusters fall into the quadrant III (high-low) category, which means that the 5 sub-districts have a high number of Covid-19 cases but are surrounded by areas with a low number of Covid-19 cases.

These areas include; Kebon Bawang, Maphar, Senayan, Ciracas, and Kalisari.

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Figure 6. LISA Moran’s I : May 16, 2020 Source: Author.

In the second observation period (May 16 2020), the value of Moran's I was 0.13 and 221 villages were identified as insignificant. There are 8 urban villages with 4 clusters that fall into the quadrant I (high-high) category, which means that these 8 villages have a high number of Covid- 19 cases and are surrounded by areas with a high number of Covid-19 distributions. The four clusters are; Sunter Jaya, Kayu Putih, Kebon Melati and surrounding areas, and Palmerah. The 21 urban villages with 7 clusters are included in the quadrant IV (low-low) category, which means that the 21 urban villages are included in areas with a low number of Covid-19 distributions and are surrounded by areas with a low number of Covid-19 distributions. These areas include;

Cilincing, Tambora and surrounding areas, Gambir, Melawai and surrounding areas, Dukuh, Bambu Apus, Cilangkap. Nine villages with 4 other clusters fall into the quadrant II (low-high) category, which means that these 9 villages have a low number of Covid-19 distributions but are surrounded by regions/villages with a high number of Covid-19 distributions. The four clusters include; Ancol and its surroundings, South Bambu City and its surroundings, Bendungan Hilir, and Malaka Sari. Meanwhile, 4 urban villages with 4 other clusters fall into the quadrant III (high- low) category, which means that these 4 villages have a high number of Covid-19 cases but are surrounded by areas with a low number of Covid-19 cases. These areas include; Jembatan Besi, Senayan, Ciracas, and Kalisari.

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Figure 7. LISA Moran’s I : Augusts 29, 2020 Source: Author.

In the third observation period (August 29, 2020), the value of Moran's I was 0.25 and 207 villages were identified as insignificant. There are 25 urban villages with 4 clusters that fall into the quadrant I (high-high) category, which means that these 25 villages have a high number of Covid-19 cases and are surrounded by areas with a high number of Covid-19 distributions. The four clusters are; Koja and its surroundings, Ancol and its surroundings, Cempaka Putih Barat and its surroundings, and Kemanggisan and Slipi. 19 urban villages with 3 clusters fall into the category of quadrant IV (low-low) which means that 19 urban villages are included in the area with the number of Covid-19 distributions. low and surrounded by areas with a low number of Covid-19 distributions. These areas include; Glodok and its surroundings, Petogogan and its surroundings, and Dukuh. Six villages with 5 other clusters are included in the quadrant II (low- high) category, which means that the 6 villages have a low number of Covid-19 distributions but are surrounded by regions/villages with a high number of Covid-19 distributions. The five clusters include: Sungai Bambu, Gunung Sahari Utara, Serdang and Sumur Batu, Kampung Bali, and Malaka Sari. Meanwhile, 4 urban villages with 4 other clusters fall into the quadrant III (high-low) category, which means that these 4 villages have a high number of Covid-19 cases but are surrounded by areas with a low number of Covid-19 cases. These areas include; Maphar, Menteng Atas, Bidara Cina, and Cipayung.

The clustering of COVID-19 during the 2020 pandemic has changed over time. At the beginning of the pandemic, clustering was still low, at 0.15. Several kelurahans (212 kelurahans) appear insignificant in this clustering. Noteworthy in Table 7 are the H-H ratio (high-high ratio) and L-L ratio (Low-Low ratio) which describe the ratio between the number of kelurahan compared to the number of clusters formed. The higher the ratio, the greater the tendency for a case to cluster in a particular location. At the beginning of the pandemic (before PSBB) the H-H ratio reached 3.5 and the L-L ratio reached 3.3.

In the middle period (during the PSBB period, May 16, 2020), the intensity of clustering decreased to 0.13. Likewise, the parameters of H-H ratio and L-L ratio decreased to 2.0 and 3.0, respectively. From these three parameters, it appears that the PSBB is quite effective in

controlling the spread of Covid-19.

In the transition period (the period after the PSBB, August 29, 2020), the clustering increased very drastically to 0.25. Likewise, the H-H ratio and L-L ratio parameters increased sharply to 6.25 and 6.3, respectively. This shows that in the transition period, the spread of COVID-19 continues and forms clusters that are more intensive than in the first and second periods.

Table 7. The Changes of Clusterization of Covid-19 over Time

Parameters March 29, 2020 May 16, 2020 August 29, 2020

Moran’s I 0.15 0.13 0.25

# Not Significant 212 221 207

H-H ratio 14 kel./4 clusters = 3.5 8 kel./4 clusters = 2.0 25 kel./4 clusters = 6.25 L-L ratio 20 kel./6 clusters =3.3 21 kel./7 clusters = 3.0 19 kel./3 clusters = 6.3 L-H ratio 12 kel./ 8 clusters =1.5 9 kel./4 clusters = 2.25 6 kel./5 clusters = 1.2 H-L ratio 5 kel./ 5 clusters =1.0 4 kel./4 clusters = 1.0 4 kel./4 clusters = 1.0

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Note: ratio = number of kelurahans divided by number of clusters Kel. = Kelurahans

Bivariate Regression: Comparison Classic and Spatial Error/Spatial Lag Model

Table 8. shows the relationship between the elderly population (population aged 60 years old and over) and Covid-19 in term of classic regression test results. A significant positive relationship was identified between the elderly population and the spread of Covid-19, significant at α = 1%. The effect of the elderly population existence is getting bigger between periods. In the initial period (pre-PSBB), every additional 1 elderly person increases by 0.005 covid-19 cases. In the second period (during PSBB), every additional 1 elderly person increases by 0.009, and in the third period (after PSBB), it increases by 0.03 cases.

To determine the choice of the spatial dependence model, whether the spatial error or spatial lag is better to be applied, a diagnostic test for spatial dependence test is needed. The diagnostic test is shown on Table 8. In the initial period, spatial lag is more significant (with p- value less than 0.01%) than spatial error. Nevertheless, in the next two periods, spatial error is more significant (with p-value less than 0.01%) than spatial lag model.

Table 8. Bivariate Classic Regression Test and LM Test Result

Variable Pre-PSBB PSBB Post-PSBB

Coef.

Constant 0.54 -0.80 22.88^***)

The Elderly Population 0.005^***) 0.009^***) 0.030^***)

The diagnostic for spatial dependence

Test Value Value Value

Moran's I (error) 3.580^*** 3.792^*** 7.850^***

LM (Lag) 6.557^*** 4.140^** 31.149^***

Robust LM (Lag) 1.041 5.556^** 5.120^**

LM (Error) 11.474^*** 12.934^*** 57.591^***

Robust LM (Error) 5.958^** 14.350^*** 31.563^***

***p<0.01, ^**p<0.05, ^*p<0.10.

Source: Author.

Table 9. shows the result of spatial dependent regression with the eldery population is acting as independent variable. In the initial period (pre-PSBB), spatial lag is chosen as spatial dependent indicator because of its significance, and denoted as rho variable. As seen on table 9, the rho value is 0.224 with significant only at α = 10%. This value means that an additional one person of elderly people in any kelurahan (village) will increase 0.224 cases of covid-19 in surrounding the that kelurahan.

In the two last periods, the spatial error is more significant than the spatial lag, and is denoted by lambda variable. This variable has significant effect on spread of covid-19 in DKI Jakarta at α = 1% during last two periods and its value increases from 0.31 to 0.50. These values mean that an increase in one unit of variables other than the presence of elderly people in a

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kelurahan has an impact on the incidence of COVID-19 in the surrounding area of 0.31(during PSBB period) or 0.50 cases (after PSBB period).

Meanwhile, the effect of the presence of the elderly on the spread of COVID-19 is not much different between the classical regression model compared to the spatial regression model.

In the post-PSBB period, the effect of the presence of the elderly appears to be slightly larger in the spatial regression model than in the classical regression.

Table 9. Spatial Dependence Effect of Elderly Population on Spread of Cobid-19 Spatial Dependence

Coef.

Rho 0.224^*)

Constant -1.22 -2.18 17.56^**)

The Elderly Population 0.005^***) 0.009^***) 0.034^***)

LAMBDA 0.31^***) 0.50^***)

***p<0.01, ^**p<0.05, ^*p<0.10.

Source: Author.

This study also applies a spatial regression model involving other variables besides the presence of the elderly, namely the number of markets available in a kelurahan, population density, building density, and the number of productive age population (15-59 years). Except for the elderly and the lambda variable, the effect of the variables other than the elderly has an impact that varies over time.

The number of markets, for example, in the first two periods does not have a significant effect, but in the third period this variable changes to be significant at α = 5%. Likewise with population density, initially this variable did not have a significant impact, but in the second period this variable became significant at α = 5%, and increased to α = 1% in the third period. Building density can be said to be consistent during the observation period. This variable has no significant effect at α = 5% on the spread of covid-19 during the observation period. Similar to the existence of the market, the presence of the productive age population was initially insignificant in influencing the spread of COVID-19, especially in the first two periods. At the end of the period, this variable has a significant impact at α = 1%.

Table 10. Spatial Error Regression Full Model Result Spatial Error Regression

Coef.

Constant 1.38 -1.64 5.28

The Elderly Population 0.005***) 0.0009***) 0.017***

Market 0.455 1.12 7.19**)

Population Density -0.0013 0.00022**) 0.00106***)

Building Density -0.037 -0.16*) -0.32

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Productive Age Population 0.0007 0.000915 0.00088***)

LAMBDA 0.30***) 0.27***) 0.53***)

***p<0.01, **p<0.05, *p<0.10.

Source: Author.

CONCLUSIONS

The results of the analysis in this study resulted in 3 point of conclusion as follows: First, Even though it is relatively low, the clustering of the spread of COVID-19 in DKI Jakarta has increased since the initial period when it was announced the presence of COVID-19 in Jakarta until the post-PSBB period. The intensity of this clustering had dropped during the PSBB period.

It seems that the implementation of the PSBB has had enough of an impact on reducing the intensity of the spread of COVID-19.

Second, The existence of the elderly since the beginning of the pandemic until the post- PSBB period has an impact on the spread of COVID-19 in DKI Jakarta. The spatial effect of the presence of the elderly during the observation period was identified as significant in the spread of COVID-19, however in the last two periods of observation the effect of spatial error was more significant than the effect of spatial lag.

In a more complete model, generally there is no consistent variable that has a significant effect on the spread of COVID-19, except for the presence of the elderly. The existence and number of markets is only significant at the end of the observation period. population density has a significant effect in the second and third periods of observation. Building density can be said to have no statistically significant effect at α = 5% throughout the observation period. Similar to the existence of the market, the number of productive age population is only significant at the end of the observation period (post PSBB) on the spread of COVID-19 in DKI Jakarta.

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