Greater Risk Area with COVID-19 at DKI Jakarta

People who are aged over 60 years, and people who have underlying medical conditions had greater risk of developing severe or critical illness if infected with the virus Covid-19…

Mapping more vulnerable sub-district area from Covid-19 based on information:

1. Distribution number of Death Patient from Covid-19

2. Distribution number of Disease Record in Hospital

3. Distribution number People Aged of 45–75 years old and more

4. Facts and Information about covid-19 patient medical historical records

POWER BI: Local Distribution of Disease Map at DKI Jakarta
Patient who have underlying medical condition from Covid-19 victims records

People who are aged over 60 years, and people who have underlying medical conditions such as diabetes, heart disease, respiratory disease or hypertension are among those who are at greater risk of developing severe or critical illness if infected with the virus. There are some medical condition record based on covid-19 data information (diagram above):

  1. Hipertension (13,2 %)
  2. Diabetes Melitus (11,7 %)
  3. Heart Disease (7,6 %)
  4. Kidney Disease (3,1 %)
Patient aged (yrs) records from Covid-19 victims

Another information about patient covid-19 that died from the illness majorly came from Elderly aged group of people (45–65 yrs old and more):

1. Aged > 60 yrs old (38 %)

2. Aged 46–59 yrs old (39,8 %)

3. Aged 31–45 yrs old (15,3 %)

4. Aged 19–30 yrs old (4,7 %)

Disclaimer*:

1. Available Data is only based on Sub-District region level (Kecamatan)

2. Data that displayed was based on Hospital at Sub-District region level (Puskesmas)

3. The data that has been displayed still need further verification step at the field (PE) to improve the integrity of the data.

4. The misinformation data will be corrected from the result of PE

5. The main focus of these available data is providing early stage of information to support medical staff work at the filed.

Geographically Weighted Regression (GWR) Result: F10 — Pneumonia

Local Distribution of Pneumonia at DKI Jakarta

According to the map of Pneumonia distribution at DKI Jakarta, the higher number (red) is located at Kebayoran Lama (39) & Pesanggarahan (38).

POWER BI: Pneumonia — Distribution Map

F10: Pneumonia

Infection that inflames air sacs in one or both lungs, which may fill with fluid. With pneumonia, the air sacs may fill with fluid or pus. The infection can be life-threatening to anyone, but particularly to infants, children and people over 65. Symptoms include a cough with phlegm or pus, fever, chills and difficulty breathing. Antibiotics can treat many forms of pneumonia. Some forms of pneumonia can be prevented by vaccines.

Std_Residual:

High number of coefficient are showed with spread out of red color and Low number are represented with blue color. The high number coefficient are located at Tanjung Priok (6.47), Duren Sawit (5.01), and Tanah Abang (2.45). It showed strong correlation at these regions; Higher number of Death patients from Covid-19 are proportional to the Number of Pneumonia patient and Number of population with age over 65 years old. However, the other regions have low number of coefficient near 0.00.

Coefficient variable of Pneumonia (C1_Pen):

Based on the map information above, it can be seen that High number Variable Coefficient (red) is found in the Pademangan (0.50), Tanjung Priok (0.33), and Sawah Besar (0.31) areas. These shows that the relationship between the number of Pneumonia patients is directly proportional to the number of Covid-19 victims. While other areas tend to have low value (blue)

Coefficient variable of Aged 65–75 (C2_Aged65–75):

Based on the map information above, it can be seen that a high positive correlation (red) is found in most of the areas on the right side of DKI Jakarta. Positive high values ​​(red) are found in the Ciracas (1.52) and Cipayun (1.51) areas. In addition, the value on the east side has an average value of above 1.00. Another contrast can be seen from the opposite side, the western region has average value below 1.00. It tends to a difference in the correlation value between the number of people aged 65–75 and the number of Covid-19 victims.

LocalR2:

Through the distribution of the value LocalR2 we can determine the “fitness” factor between the model and the actual value. The range of positive high values ​​(red) indicates the over fitted model condition. On the other hand, the range of low (blue) values ​​is close to 0.00, this shows that the data correlation between the model and the actual valued is very good.

Geographically Weighted Regression (GWR) Result: F06 — Dengue Fever (DBD)

Local Distribution of Dengue Fever (DBD) at DKI Jakarta
POWER BI: DBD— Distribution Map

F06: Dengue Fever (DBD)

A mosquito-borne viral disease occurring in tropical and subtropical areas. Those who become infected with the virus a second time are at a significantly greater risk of developing severe disease. Symptoms are high fever, rash and muscle and joint pain. In severe cases there is serious bleeding and shock, which can be life threatening. Treatment includes fluids and pain relievers. Severe cases require hospital care.

Std_Residual:

The spread of Dengue Fever (Dengue Fever) was recorded at its maximum point in the period of June, 2020. High positive coefficients indicated by red distribution are found in Tanjung Priok (9.61), Duren Sawit (9.45) and Tanah Abang (3.47) districts. It indicates a strong correlation in the area; The high number of Covid-19 Deaths is followed by the number of dengue patients and the population aged> 65 years. Meanwhile, other areas have a small correlation close to zero. There are two sub-districts that have contradicting values (High Negative values): Pasar Minggu (9.44) and Cipayung (3.74) districts. This indicates a mismatch in the correlation values ​​for the area.

Coefficient variable of DBD (C1_Pen):

Based on the map information above, it can be seen that High number Variable Coefficient (red) are found in the North East DKI Jakarta areas. The Averaging number 1.40 (Cilincing, Koja, and Cakung). These shows that the relationship between the number of DBD patients is directly proportional to the number of Covid-19 victims. While other areas tend to have low value (blue)

Coefficient variable of Aged 65–75 (C2_Aged65–75):

Based on the map information above, it can be seen that a high positive correlation (red) is found in most areas on the West side of the DKI Jakarta area. Positive high values ​​(red) are found in the Ciracas (1.57) and Cipayung (1.58) areas. In addition, the value on the east side has an average value of> 1.40. Another contrast can be seen from the opposite side, the western region tends to have a difference in the correlation value between the number of people aged 65–75 and the number of Covid-19 patients who died.

Koefisien LocalR2:

Through the distribution of the value LocalR2 we can determine the “fitness” factor between the model and the actual value. The range of positive high values ​​(red) indicates the over fitted model condition. On the other hand, the range of low (blue) values ​​is close to 0.00, this shows that the data correlation between the model and the actual valued is very good.

Geographically Weighted Regression (GWR) Result: F15 — Febris

Local Distribution of Febris at DKI Jakarta
POWER BI: Febris — Distribution Map

F15: Febris / High Fever

Febris is a medical terminology as High fever with body temperature more than 37 C degree. It is not kind of disease but it could be condition where our antibody has been working to fight the infection caused by virus, bacteria, or parasite. These condition could become symptom that lead into kind of disease from the patient

Std_Residual:

The distribution of Febris conditions was recorded at its maximum point in the period of June, 2020. The high positive coefficient is indicated by the red distribution in Tanjung Priok (4.67) and Duren Sawit (5.42) Districts, this indicates a strong correlation in these areas; The high number of Covid-19 Deaths was followed by the number of Febris patients and the total population aged> 65 years. Meanwhile, other kecamatan areas have a small correlation close to zero. There are two sub-districts that have contradicting values, namely the Cipayung (-2.06) district. This indicates a mismatch in the correlation values ​​for the area.

Coefficient variable of Febris (C1_Pen):

The average distribution of the component coefficient values ​​is close to 0.00. This shows that the data correlation between the components of the spread of Febris’ disease was not followed by an increase in the number of Covid-19 patients who died.

Coefficient variable of Aged 65–75 (C2_Aged65–75):

Based on the map information above, it can be seen that a high positive correlation (red) is found in most areas on the East side of the DKI Jakarta area. Positive high values ​​(red) are found in the Ciracas (1.52) and Cipayung (1.53) areas. In addition, the value that is on the east side has an average value of 0.00. Another contrast can be seen from the opposite side, these areas tend not to have the expected correlation.

LocalR2:

Through the distribution of the value LocalR2 we can determine the “fitness” factor between the model and the actual value. The range of positive high values ​​(red) indicates the over fitted model condition. On the other hand, the range of low (blue) values ​​is close to 0.00, this shows that the data correlation between the model and the actual valued is very good.

Conclusion

  1. Jakarta City more vulnerable areas located at Tanjung Priok (North Jakarta) and Duren Sawit (East Jakarta), where the correlation between number of victims Covid-19 has strong relationship with patient aged over 60 years with underlying medical condition.
  2. Spatial Analytics methods (Hot Spot Analysis and Geographically Weighted Regression (GWR)) are able to give better understanding about relationship between Covid-19 information and patient medical historical records.

Recommendation

  1. Since the data still not represent the patient medical condition directly, it could lead into false conclusion result. In order to get better result, more detailed information about patient could lead into better result.
  2. For better understanding the results are only based on statistically number of cases. It would be better and strong judgement if we could add several Quality condition that support or against the relationship condition on the result.
  3. For better understanding the results should followed with News or Cases that announced at the media that could able to support or against the statistical result.

References:

1. https://covid19.go.id

2. https://corona.jakarta.go.id

3. https://surveilans-dinkesdki.net

3. https://data.jakarta.go.id

4. https://kawalcovid19.id/

Closing Remark

This publication is produced for educational or information only, if there are any mistake in data, judgement, or methodology that i used to produce this publication.

  • * Please consider to contact the writer using contact information at Profile. I would like to discuss and sharing more about the topic. Thank you.

Best Regards,

Andriyan Saputra

Just an ordinary person curious about the world.