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THE DISTRIBUTION OF COVID-19 CASES USING SPATIAL ANALYSIS TO SUPPORT SURVEILLANCE PROGRAM IN CITY

Agus Fitriangga1*, Widi Rahardjo1, Alex1, Muhammad Pramulya2

Department of Community Medicine, Faculty of Medicine, Universitas Tanjungpura, Indonesia, PO Box 781241 Department of Regional Planning, Faculty of Agriculture, Universitas Tanjungpura, Indonesia, PO Box 781242

Corresponding author: 1*

Keywords: ABSTRACT Pontianak City, COVID-19, The Health Office of Pontianak City, West Kalimantan Province Geographic Information reported that there were 387 confirmed cases of COVID-19 per July System 2020 and 4 of them died. Unfortunately, the use of Geographic Information System (GIS) for planning, monitoring, and decision- making to support surveillance program by local- health managers has

not been well documented, particularly in the distribution of COVID-19. The utilization of GIS is required as a method for public health surveillance and monitoring. This study aimed to analyze the distribution of COVID-19 cases with a spatial approach to support the epidemiological surveillance program in Pontianak City between March 2020 to July 2020. This research was a cross- sectional study. The dependent variable was cases COVID-19 (suspect and confirmation) and the independent variables included ages, sex, and patient’s status. A total of 332 cases of COVID-19 in Pontianak City were collected from six districts. The results showed that males were more likely to suffer from COVID-19 than females, the age group of 31-40 years was more vulnerable, and some patients were cured. The study also revealed the spread of one district with a Clustered type of COVID-19. The presence of spatial autocorrelation was explored using global and local Moran's I statistics. The global Moran's I revealed a negative but statistically significant spatial autocorrelation COVID-19 incident rate (I = 0,000). The mapping of COVID-19 cases using GIS can facilitate the epidemiology programmer in Pontianak City Health Office and Public Health Centre in intervening the social determinant of health to identify the spread of COVID-19 disease.

This work is licensed under a Creative Commons Attribution Non-Commercial 4.0 International License.

1. INTRODUCTION 585

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Coronavirus Disease 2019 (COVID-19) is a new type of disease that has never been previously identified in humans. The virus that causes the COVID-19 is called Sars-CoV-2. The coronavirus itself is zoonotic (transmitted between animals and humans). Research states that there are 2 other types of coronaviruses. The first type is named SARS which is transmitted from civet cats to humans and the second type is MERS, from camels to humans. Meanwhile, the animal that is the source of the COVID-19 transmission is still unknown. As of July 31, 2020, there were 17,106,007 confirmed cases of COVID-19 worldwide with 668,910 deaths [1]. Of the 6 WHO regions, the American region recorded the highest cases, which contributed 9,152,173 cases and 351,121 deaths. Meanwhile, in the Southeast Asia region, there were 2,009,963 confirmed cases and 44,031 deaths. Indonesia recorded 108.376 confirmed cases, 5.131 deaths, 65,907 recovered patients [1] Meanwhile, in West Kalimantan alone, there were 387 confirmed cases of COVID-19 per July 31, 2020, and 4 of them died [2]. Of the 14 districts/cities in West Kalimantan Province, Pontianak City is categorized as a local transmission group [3]. In Pontianak City, as of July 31, 2020, there were 122 positive cases, 210 suspected cases, and 5 deaths. The case data is spread across 6 sub-districts in Pontianak City [3]. Regarding the use of Geographic Information System (GIS) in the case of the COVID-19, previous studies have described the spatial spread of severe acute respiratory syndrome (SARS) in Beijing and mainland China [4] One study also considered different types of connections between cities in order to calculate spatial associations [5]. Other studies have analyzed epidemic data from the Middle East coronavirus syndrome (MERS-CoV) in Saudi Arabia using various spatial approaches [6], [7], [8]. To control infectious diseases, one of the activities carried out by the government is epidemiological surveillance. According to WHO, surveillance is the process of collecting, processing, analyzing, and interpreting data systematically and continuously as well as disseminating information to units that need to take action. Surveillance activities are an important instrument in managing outbreaks and developing rapid responses to control the spread of infectious diseases. Spatial analysis is a visual inference to a map which is a combination of spatial data and attribute data. Spatial data refers to a location or position on the surface of the earth, which covers coordinates, raster, or regional administrative boundaries. Meanwhile, attribute data refers to the in situ characteristics, which include abiotic (all the physical elements of the existing land, namely soil, geology, climate, and water), biotic (flora and fauna), and culture (socioeconomic). With spatial analysis, spatial data and attribute data are processed into information spatial, which can be used as a tool in policy formulation, decision making, and/or implementation of activities related to space earth. In epidemiology, spatial analysis is highly useful, especially for evaluating occurrence differences in incidence by geographic area and identifying disease clustering [9], [28]. Clustering is a spatial or space-time analysis for statistically significant disease. Clustering analysis has several benefits. First, it shows the geographical surveillance of disease and identify disease clusters spatially or space-time. In addition, it can find out if the cluster is statistically significant and verify whether a disease is randomly distributed according to place, time, and place and time. Finally, the analysis can evaluate the statistical significance of a disease cluster alarm and display prospective real-time or real- periodic from disease surveillance for early detection of outbreaks. The clustering analysis works by placing the circular window on the study map according to the analysis and the specified model. Nevertheless, the use of spatial analysis which identifies the distribution pattern of COVID-19 cases in Pontianak City is still limited. For that reason, this study aims to analyze the distribution of COVID-19 cases with a spatial approach to support the epidemiological surveillance program in Pontianak City.

2. METHODS This study used an ecological design with a cross-sectional approach. The location of this research was in Pontianak City, West Kalimantan Province. This research was conducted from March to July 2020. Demographic data on COVID-19 cases reported at the Pontianak Health Office from March to July 2020 contained (1) the address of the patient, (2) the number of suspected and confirmed cases, (3) the number of 586

ISSN: 03875547 Volume 44, Issue 01, February, 2021 deaths, and (4) the number of recovered cases. Latitude and longitude coordinates of the patient's location were extracted from data collection to perform spatial distribution and analysis. Statistical analysis is used to identify and confirm spatial patterns, such as the center of a feature group, directional trends, and whether features form clusters. It also classifies and symbolizes data that can obscure or overemphasize patterns. The statistical function analyzes the underlying data and measures that can be used to confirm the existence and strength of the pattern [10]. Thus, statistical analysis in this study will help provide answers to link the pattern of distribution of COVID-19 cases. Geographical distribution is to measure a set of features for the calculation of values that represent distribution characteristics, such as center, orientation, or cohesiveness [10]. This study focused on two methods: spatial distribution and the analysis of spatial distribution patterns. The spatial distribution included a spatial mean center, standard distance, and directed distribution analysis. Analysis of the spatial distribution pattern is the mean nearest neighbor method. Mapping trends in the distribution of the epidemiology of COVID-19 cases to identify relationships with certain physical characteristics. The details of the pattern distribution were minimized using pattern analysis. The cluster pattern showed that some factors caused the epidemiology of COVID-19 cases in that area.

3. Spatial Mean Center The center of the spatial mean is the x and y average coordinates of all features in the study area. It is useful for detecting any change in the distribution or for comparing distribution features. This analysis showed a lean-centered phenomenon, in particular, to visualize the means of the COVID-19 cases reported in Pontianak City. The center of the spatial mean aims to study changes in the detected distribution to compare the distribution types and features. The spatial mean center can make a new feature point classification where each feature represents the average center. The x and y mean represented the mean center values which means that the dimension plane was entered as a featured product.

The mean center was calculated as follows:

∑푛 푥 푋 = 푖=1 푖 (1) 푛 ∑푛 푦 푌 = 푖=1 푖 (2) 푛 where xi and yi were the coordinates for i, and n was the total number of features.

The weighted means were as follows:

푛 ∑푖=1 푊푖푥푖 푋푤 = 푛 (3) ∑푖=1 푊푖

푛 ∑푖=1 푊푖푦푖 푌푤 = 푛 (4) ∑푖=1 푊푖

where wi was the weight on characteristic i.

The method for calculating the centers for the three dimensions was the z attribute for each feature:

∑푛 푧 푍 = 푖=1 푖 (5) 푛

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푛 ∑푖=1 푊푖푧푖 푍푤 = 푛 (6) ∑푖=1 푊푖

4. Standard Distant The distribution of Standard Distant's density size which provides a single value represents a distribution around the center of the mean. The spread of points around the center needs to be measured. The value is the distance represented by the deviation. Therefore, spatial dispersion measures provide information on COVID-19 cases around the center. The expression used in this research was the standard distance, which was equivalent to the statistician's standard deviation. Standard Distant is a tool used to create a round polygon of points scattered around the center. In this study, one polygon turns represented the scattered COVID-19 cases reported monthly from the average center [10]. Standard Distant denotes a new feature class that includes round, centered polygons at a center average (single average and circle for each case). Each round polygon is created with a radius that has the same value as the Standard Distant value. The attribute values for each circular polygon are the mean-centered x-coordinate, y-centered average coordinate, and standard distance (radius circle).

Standard Distant calculation was as follows:

2 2 ∑푛 (푥푖−푋) ∑푛 (푦푖−푌) 푆퐷 = √ 푖=1 + 푖=1 (7) 푛 푛 where xi and yi were the coordinates for feature i, {푋, 푌}denoted the middle feature means, and n equaled to the total number of features.

The weighted standard distance was calculated as follows:

푛 2 푛 2 ∑푖=1 푊푖(푥푖−푋) ∑푖=1 푊푖(푦푖−푌) 푆퐷푤 = √ 푛 + 푛 (8) ∑푖=1 푊푖 ∑푖=1 푊푖 where wi was the weight on feature i and {푋푤, 푌푤}denoted the mean center.

5. Standard Deviational Ellipse As GIS is a tool for describing spatial point data, the standard deviation Ellipse (SDE) is used to summarize the spatial characteristics of geographic features, covering central trends, dispersions, and directional trends [10]. A common method for measuring the trend of a single defined location is by calculating standard distances separately on the x and y axes. Both steps determine the axis of the ellipse while entering the distribution characteristics. An ellipse is also known as the standard deviation of an ellipse because it is calculated by the standard deviation of the x coordinate and the y coordinate of the mean center to determine the ellipse's axis. Ellipse allows the distribution characteristics to run longitudinally and have a certain orientation. In disease surveillance studies, Elliptical charts were used to predict trends in spatial distribution because trend and central distribution were two main aspects of concern for epidemiologists. The calculation is illustrated in Equations 9 and 10 below:

∑푛 (푥 −푋)2 푆퐷퐸푥 = √ 푖=1 푖 (9) 푛 ∑푛 (푦 −푦)2 푆퐷퐸푦 = √ 푖=1 푖 (10) 푛 588

ISSN: 03875547 Volume 44, Issue 01, February, 2021 where xi and yi were the coordinates for feature i, {푋, 푌}denoted the mean center, and n was the total number of features.

The rotation angle was calculated below:

퐴+퐵 푡푎푛 푡푎푛 휃 = (11) 퐶 푛 2 푛 2 퐴 = ∑푖=1 푥푖 − ∑푖=1 푦푖 (12)

푛 2 2 푛 2 2 퐵 = √(∑푖=1 푥푖 ) − (∑푖=1 푦푖 ) (13) 푛 퐶 = ∑푖=1 푥푖푦푖 (14) where the deviation xi and yi came from the xy coordinate of the mean center.

The standard deviation for the x and y axes was presented as follows:

푛 2 √∑푖=1 (푥푖푐표푠푐표푠 휃 −(푦푠푖푛푠푖푛 휃) 푎푥 = √2 (15) 푛

푛 2 √∑푖=1 (푥푖푠푖푛푠푖푛 휃 −(푦푐표푠푐표푠 휃) 푎푦 = √2 (16) 푛

The standard deviational ellipse (SDE) creates a new feature class that includes ellipses centered on the mean center for all characteristics (or for the case when values are defined) [10]. The attribute values of an elliptical polygon include the standard distance (long and short axes) and orientation of the ellipse. Orientation is the rotation of the long axis measured clockwise from noon. In this study, SDE was carried out to quantitatively analyze the orientation of COVID-19 cases to identify the extent to which cases of COVID-19 had spread. On the other hand, it also identifies the number of standard deviations (1, 2, or 3). When a feature has a normal spatial distribution (which means the density is in the middle and becomes less dense toward the edges), one standard deviation reaches 68% of all centroid input features. Two standard deviations will make up about 95% of all features, and three standard deviations will cover about 99% of all centroid features. In this research, the mapping of spatial distribution patterns was carried out using pattern analysis with the average nearest neighbor (ANN). The ANN measured the average distance from each point in the study area to its closest point. The average distance was compared to the expected average distance. Thus, the ANN ratio was created, which, in simple terms, was the observed/expected ratio. If the ratio is less than 1, we can say that the data shows a clustered pattern. Meanwhile, if the values are greater than 1, it indicates a scattered pattern [10]. In this study, the mapping of the distribution pattern of COVID- 19 cases was observed. Further, the shape of the ANN curve as a function of the nearest neighbors could provide insight into the spatial arrangement of points relative to each other to determine whether the patterns were clustered, random, or scattered.

6. Data Processing Devices This research used ArcMap 10.5 software to perform statistical analysis. The application formulas in ArcMap 10.5 used mathematical equations to develop each analysis with different functions.

7. Spatial autocorrelation analysis

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Spatial statistics help describe the spatial variations exhibited by the response process of a given phenomenon [11]. In this study, local Moran’s I statistics (LISA) was calculated since the interest was in identifying specific observations or districts that exhibited spatial autocorrelation with their neighbors. Two tests existed in GeoDa to do this: Moran’s I, and Lagrange Multiplier test. In this study, Moran’s I statistics [11] were performed to identify specific observations or districts that exhibited spatial autocorrelation with their neighbors. Moran’s I have been extended to the diagnosis of spatial dependence in the presence of covariates. Moran’s I measured autocorrelation in regression residuals.

Moran’s I was calculated as:

where N was the number of areas (districts), i referred to a particular district with j referring to that district’s neighbors, wij was an element of a row-standardized weight spatial matrix (i.e., elements of a row sum to 1) corresponding to the observation pair i and j. The sample mean was subtracted from Xi and Xj to calculate deviations from the sample mean for areas i and j.

The expected value of Moran’s I under the null hypothesis was -1/ (N - 1). If observed Moran’s I was greater than expected, neighboring districts had similar incidence rates of COVID-19 incidents. If observed Moran’s I was smaller than expected, neighboring districts might have dissimilar rates.

Since spatial dependence was identified via local tests of spatial autocorrelation given that spatial heterogeneity in parameters did not account fully for the observed univariate spatial dependence, the next step involved modeling this spatial autocorrelation via covariates (independent variables). It is important to define what we mean by spatial dependence at the outset. Spatial dependence exists when the value associated with one location is dependent on those of other locations. This can result from spatial interaction effects (e.g., externalities or spill-over effects) or from measurement errors. Because it is quite challenging to distinguish whether a location is impacted by its neighboring values or it is just different, it can be difficult to separate spatial dependence from spatial heterogeneity. On the other hand, a spatial lag is a variable that essentially averages the neighboring values of a location (the value of each neighboring location is multiplied by the spatial weight and then the products are summed). Spatial lags are used in the computation of global and local Moran’s I, as well as in spatial lag (Wy) and spatial error models (We). They can also be computed as separate variables (e.g., WX) in GeoDa. A classical ordinary least square (OLS) regression was run with diagnostics for spatial lag to determine whether the covariates observed fully modeled the observed spatial dependence. Diagnostics for spatial dependence indicated spatial lag dependence in the presence of covariates, evidence consistent with a spatial contagious diffusion process.

8. RESULTS From the results of document tracing, the researchers obtained data from 122 confirmed cases of COVID- 19 and 210 suspected cases. Of the six districts in Pontianak City, the district that recorded the highest confirmed cases was South Pontianak District, while the district that had the lowest cases was Southeast Pontianak District.

Table 1. The Distribution of Suspect Cases and COVID-19 Confirmation in Each District Numbe District Coverage Population Population Cases Total 590

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r Area Density Suspect Confirmatio 2 2 (km ) per km n 1 City of 15.98 125,936 7,880.85 42 27 69 Pontianak 2 16.47 141,083 8,566.06 42 26 68 3 8.78 94,701 10,785.99 31 20 51 4 South Pontianak 15.14 95,858 6,331.44 35 29 64 5 North 37.22 128,542 3,453.57 47 8 55 Pontianak 6 Southeast 14.22 51,603 3,628.90 13 12 25 Pontianak Total 107.82 637,723 5,915 210 122 332

Meanwhile, in terms of individual characteristics, males were reported to have the highest suspected and confirmed cases, as many as 111 suspected and 68 confirmed positive COVID-19 cases. Furthermore, based on the age group, the largest proportion was the 31-40 years’ age group, with 35 confirmed cases and 22 suspected cases. For patient status, 289 were declared cured and 43 people died.

Table 2. Regression Linear Analysis Variable Cases (N=332) Total Coefficients p-value Suspect % Confirmation % 95% CI Sex Men 111 33.43 68 20.48 179 0.050 0.345 Women 99 29.82 54 16.27 153 (-0.053-0.153) Age Group 0-10 37 11.14 5 1.50 42 0.030 0.018 11-20 15 4.51 0 0 15 (0.005 – 0.055) 21-30 24 7.23 18 5.42 42 31-40 22 6.6 35 10.54 57 41-50 22 6.6 27 8.13 49 51-60 38 11.45 14 4.21 52 61-70 36 10.84 16 4.81 52 71-80 13 3.91 6 1.80 19 81-90 3 0.90 1 0.30 4 Patient’s Status 0.352 0.000 Cured 172 51.81 117 35.24 289 (0.194 – 0.511) Death 38 11.44 5 1.51 43

As can be seen from the table (above), the linear regression results reported that the patient's status was positively related to the case. The p-value was 0,000 with 95% CI 0.194-0.511.

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Figure 1. The Distribution of Suspect Cases and COVID-19 Confirmation in Pontianak City, from March to July 2020

Meanwhile, from the ANN analysis, the distribution of COVID-19 in each sub-district in Pontianak City can be seen in the following table:

Table 3. The Average Nearest Neighborhood COVID-19 Each District in Pontianak City No District Observed Expected ANN z-score p-value Cases Mean Mean Spread Distance Distance (meters) (meters) 1 City of 183,7937 167,6469 1.096314 1.530547 0.12588 Random Pontianak 1 2 West 175,5977 199,3173 0.880996 -1.877359 0.06046 Clustered Pontianak 9 3 East Pontianak 255,9060 213,3176 1.199648 2.700726 0.00691 Dispersed 9 4 South 203,7256 198,2406 1.027669 0.420136 0.67438 Random Pontianak 6 5 North 221,7861 245,3400 0.903995 -1.362093 0.17316 Random Pontianak 9 6 Southeast 304,1459 208,4507 1.459078 4.391243 0.00001 Dispersed Pontianak 1

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Figure 2. The Scatter plot of autocorrelation Moran’s I

Table 4. The Type of Spatial Local Relation among District in Pontianak City Spasial lag Variable (WX) High Low Variable High Quadrant I: High High Quadrant IV: High Low (X) Including City of Pontianak, West South-east Pontianak Pontianak Low Quadrant II: Low High Quadrant III: Low-Low Including South Pontianak, East Pontianak

In quadrant I, HH (High-High) showed that areas that had high confirmation and suspect cases were surrounded by areas that had high confirmation and suspect cases as well. Districts that were grouped in quadrant I including the City of Pontianak District and West Pontianak District. In quadrant II, LH (Low- High) showed that areas with low confirmation and suspect cases were surrounded by areas that had high confirmation and suspect cases. Districts that were located in quadrant II included South Pontianak District and East Pontianak District. In quadrant III, LL (Low-Low) revealed that areas that had low confirmation and suspect cases were surrounded by areas that had low confirmation and suspect cases. This quadrant included North Pontianak District. Whereas in quadrant IV, HL (High Low), it was reported that areas that had high confirmation and suspect cases were surrounded by areas that had low confirmation and suspect cases, which included Southeast Pontianak District.

9. DISCUSSION The results of this study indicated that more men were confirmed and suspected of COVID-19 compared to women. The results of this study were in line with earlier research [12] that revealed 58.1% of patients in China were male. Other researchers in Italy [13] also highlighted the same findings where the COVID-19 deaths were mainly observed among older and male patients who also had multiple comorbidities. In addition, Bwire [14] argued that the biological differences in the immune systems between men and women might impact our ability to fight an infection including SARS-2-CoV-2. Generally, females are more resistant to infections than men. This is possibly mediated by several factors, not only sex hormones and high expression of coronavirus receptors (ACE 2) in men but also lifestyle, such as higher levels of smoking and drinking among men as compared to women. Additionally, women have a more responsible attitude toward Covid-19 pandemic than men. This may reversibly affect the undertaking of preventive measures such as frequent hand washing, wearing a face mask, and staying at home orders [27]. Besides 593

Fitriangga, et.al, 2021 Teikyo Medical Journal that, another interesting finding that we obtained in this research was the fact that the 31-40 year- old group had more confirmed and suspected COVID-19 diagnoses than the elderly group. These findings have important clinical and public health implications. First, occupational and behavioral factors might put younger adults at higher risk for exposure to SARS-CoV-2. Younger adults make up a large proportion of workers in frontline occupations (eg, retail stores, public transit, child care, and social services) and highly exposed industries such as restaurants/bars, entertainment, and personal services [15]. In this case, being consistent to implement prevention strategies might be difficult or even impossible. In addition, younger adults might also be less likely to follow community mitigation strategies, such as social distancing and avoiding group gatherings [16], [30]. Further, younger adults, who are more likely to have mild or no symptoms, can unknowingly contribute to presymptomatic or asymptomatic transmission to others [17], including persons at higher risk for severe illness. Finally, SARS-CoV-2 infection is not benign in younger adults, especially among those with underlying medical conditions, who are at risk for hospitalization, severe illness, and death [18], [23- 26]. Meanwhile, from the ANN analysis, it should be noted that West Pontianak District was one sub-district that had a clustered case distribution pattern. When viewed from the total population and population density, the district has the highest population in Pontianak City with a high population density as well. This is in accordance with the research conducted by Kadi who argued that the higher the population density, the closer the people in public places, which eventually will increase the spread of the current virus. Bhadra's research supported this argument having obtained a moderate association between Covid-19 spread and population density [19]. The current study also reported another interesting finding. When viewed from the first cases found in March 2020, the highest increase in confirmed and suspected cases occurred in April and May 2020. This is interesting because the Indonesian government did not seem to take the spread of COVID- seriously so that the cases increased in the following months. Furthermore, in April and May 2020 the City of Pontianak began to implement massive and intensive 3 T (Trace, Test, and Treatment) so that many confirmed and suspected cases were found in these months. Our study also found that age and patient status were associated with confirmed and suspected cases of COVID-19. From the results of linear regression analysis, it was noted that age had a coefficient value of 0.30 (95% CI 0.005 - 0.055) with a p-value of 0.018. Meanwhile, the status of recovery and death had a coefficient value of 0.352 (95% CI 0.194 - 0.511) with a p-value of 0.000. This finding is in line with the research results revealed by [20], [21] who stated that both the age-expected and age- standardized Case Fatality Rates (CFR) make the additional assumption that the age-specific CFR among individuals who are identified as having COVID-19 are the same as those who had an undiagnosed disease.

10. Limitations Although this research reports several important findings, it still has several limitations, Firstly, the data available on the website was different from those recorded in the Pontianak City Health Office, for example, the absence of a complete address. Thus, we need to double-check the quality of the data. Secondly, we cannot analyze the mobilization of the citizen in Pontianak City, so we cannot capture the pattern of COVID- 19 incidence.

11. Conclusion This paper used GIS to model the spatial distribution of COVID-19 incidence rates in Pontianak City from a scanty point surveillance data set from March to July 2020. We illustrate the power of geography to generate the COVID-19 rates at any geographic unit, in this case, at the district level. We then further used advanced spatial statistical techniques in GeoDa to analyze the presence or absence of spatial autocorrelation in space using a set of independent variables that helped explain the spatial variation of COVID-19 in Pontianak City. The results confirmed the fact that the COVID-19 rate was very high in urban areas and provincial headquarter districts. We conclude that these results can be validated with more refined 594

ISSN: 03875547 Volume 44, Issue 01, February, 2021 data such as the data that are currently being collected by demographic health surveys. Data on COVID-19 incidence among different age groups as well as locales would be extremely useful and provide a more nuanced GIS analysis to validate the findings in this research. In addition to investigating incidence in different subpopulations, it would be useful to evaluate the role of human migration within and in between the districts. Analysis of the patterns of human migration in relation to COVID-19 transmission would assist us in explaining the high incidence rates in urban and provincial districts. Specifically, the nature and scale of high COVID-19 incidence rates around border districts would better be explained using both provincial and district level people mobilization patterns.

12. Ethics approval and consent to participate No human or animal samples were included in the research presented in this article; therefore, ethical approval was not necessary.

13. Availability of data and materials The datasets used and analyzed during the current study are available from the websites https://covid19.pontianakkota.go.id

14. Competing interests The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

15. Funding This work was funded by the Faculty of Medicine Tanjungpura University grant number SP-DIPA- 023.17.2.677517/2020.

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