Communicating the High Susceptible Zone of COVID-19 and its Exposure to Population Number through a Web-GIS Dashboard for Indonesia Cases

The Medical Geographic Information System (Medical GIS) application during the COVID-19 pandemic crisis has become influential in communicating disease surveillance for health practitioners and society. The Johns Hopkins University has extensively used a well-known Web-GIS dashboard to track the COVID-19 cases since January 22 and illustrates the location and number of confirmed COVID-19 cases. Unfortunately, the dashboard particularly for Indonesian cases is only represented by one point (dot map) placed on the centroid of the Indonesian archipelago. Further research can fill the gap in downscaling the geographical location data of COVID-19 cases to the cities or even the village level in Indonesia and communicating the susceptible zoning to society. We uplift the point COVID-19 cases data to susceptible zoning gathered from official COVID-19 government websites, process it using Geographic Information System analysis, and communicate it to society through a Web-GIS dashboard. Five datasets, i.e., population data, administrative boundary, Landsat 8 OLI satellite imagery, COVID-19 cases geographic location, transportation infrastructure, and crowded places location, are used to analyze the susceptible area. Due to different standard data sources from each province in Indonesia, we only present provinces in Java Island with complete COVID-19 cases data on villages-scale. The technical challenges and future improvement in developing the national dashboard of Web-GIS-based susceptibility dashboard are also discussed. The dashboard information would further add some essential information for society to explore their zone status in adapting to the “New Normal” using the SICOVID-19 dashboard from their computers or gadgets during the pandemic crisis.

COVID-19; Population exposure; Susceptible area; Web-GIS dashboard

    World Health Organization (WHO) officially announced Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2) or COVID-19 as a pandemic on March 11, 2020. In response to this pandemic event, the health authority and many world health researchers made several web-based dashboards to monitor the spreading of COVID-19 in near-real-time (Dong et al., 2020; Morettini et al., 2020; Zhou et al., 2020; Wissel et al., 2020; Bernasconi & Grandi, 2021). Location-based data of confirmed cases, deaths, and recovered patients are urgently needed to accurately measure the extent and severity of COVID-19 and assess the response’s effectiveness (Franch-Pardo et al., 2020; Rosenkrantz et al., 2021). 
    During a pandemic event, the open geo-location COVID-19 database supplies information about the addition of new cases, the rate of death, and the recovery rate that understand the pandemic dynamics. It also provides reliable spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control (Zhao et al., 2020; Bogoch et al., 2020). Some research also shows that geographical location data of pandemic cases can play a role in communicating the risk of transmission and even in evaluating the policy to deal with the outbreak, especially when the data is in near-real-time (Fang et al., 2008; Tatem et al., 2012; Kraemer et al., 2020; Kamel-Boulos & Geraghty, 2020). A device tracking and managing the spread of COVID-19 also needs innovation and contribution from the engineering perspective (Berawi et al., 2020). Moreover, the combination of geographical information big data with other relevant data sets, such as transportation hubs and the area where people gather, will provide additional insight into the potential local transmission region (Zhou et al., 2020; Costa et al., 2020).
    A web-based map dashboard of world COVID-19 that was launched on January 22 by the Center for Systems Science and Engineering (CSSE) at The Johns Hopkins University illustrates rapid visualization of the confirmed case location of pandemic information (Dong et al., 2020). Unfortunately, it only shows Indonesian cases on the country centroid which is unavailing information for Indonesian. In early March, the Indonesia Institute of Science survey revealed that more than 60% of the respondent needed at least sub-district level infected case information, and 97% agreed that the government should expose 14 days of history mobilization of infected patients (Cahyadi, 2020). Considering patients’ privacy while revealing the spatial data can be manageable by aggregating it and anonymizing it in the right way to protect privacy would take some burden off.
    On the national level, the Indonesian government, through its National COVID-19 Task Force, develops a web-based COVID-19 dashboard with data visualization on the provincial level. While several local governments have been visualizing different administration boundary levels and formats, some local governments share detailed data and deliver interactive web mapping. However, some local governments also visualize the data on a static map. It is unfavorable information for a population that lives and travels inter-administrative boundaries. They must visit multiple websites to be well informed about their neighbourhood's susceptibility. One compact open web-based map dashboard that compiles the COVID-19 case on the village level is needed to supply the information. To address this issue, we develop a Web-GIS dashboard that integrates all of the COVID-19 case data from various provincial governments. To fill the gap of this issue, we develop a dashboard with sensitive zone information to inform the user directly from their smartphone using the geo-location application. 

The development of a Web-GIS dashboard that can provide detailed information on COVID-19 case data is urgent because of the very dynamic nature of the disease every day. The dynamic nature of COVID-19 requires the government to be able to take policy quickly and strategically. This research is the first study on a COVID-19 information system application that integrates a COVID-19 dataset with different standards and formats into one complete data. The development of SICOVID-19 Dashboard is able to find use of the different formats of COVID-19 statistics and population estimation model to deliver new information which, is the prone area that is not available in other COVID-19 dashboards. The SICOVID-19 dashboard seeks to present the distribution of village-based cases and model the estimated number of people who have the potential to be exposed to COVID-19. The result for the spatial pattern of the susceptibility shows that high susceptibility to COVID-19 is higher closer to the city center with high road density and vice versa. The access option of the SICOVID-19 dashboard through the web interface and mobile interface makes it easier for the public to find information about their region’s vulnerability to COVID-19. It is hoped that the data presented in the SICOVID-19 dashboard could provide a more in-depth insight into the threat of COVID-19 in Indonesia and as a reference for society to adapting the “New Normal” way of life. 

    This study was funded by the Consortium of Ministry of Research and Technology/National Research and Innovation Agency and Indonesia Endowment Fund for Education, Ministry of Finance, The Republic of Indonesia. We want to thank PT Infimap Geospatial Sistem for providing the People in Pixels data and undergraduate students from the Department of Geography, Faculty of Mathematics and Natural Science, Universitas Indonesia, for their work in updating the location of COVID-19 cases daily, ESRI Indonesia for providing us ArcGIS Online facility, and Badan Nasional Penanggulangan Bencana for reviewing our dashboard.

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