A spatial time series forecasting for mapping the risk of COVID-19 pandemic over Bandung Metropolitan Area, West Java, Indonesia

Masita Dwi Mandini Manessa, Ridwan Kamil, Setiaji Setiaji, Ida Ningrum, Weling Suseno, Ira Rahmayanti, Faris Zulkarnain, Ardiansyah Ardiansyah, Indah Lesmini, Rahmat Hidayatulloh Tasdiq, Idham Riyando Moe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

West Java is in the five line on the list of provinces in Indonesia with the most COVID-19 cases, as Bandung Metropolitan Area (BMA) is the second most densely populated showing the highest number after Jakarta Greater Area. Bandung Metropolitan Area consist of Bandung City, Cimahi City, Bandung Regency, and West Bandung Regency. Then, an intense movement of people created between the connected city and regency. Bandung City became the epicenter of movement BMA, since it is the province capital city, business, and education center. This fact, putting BMA at the highest risk not only for the pandemic but also socioeconomic issues. The spatial time series risk forecasting information is an essential for the decision-maker to develop a day by day policy aimed for combating the COVID-19 pandemic issue. In this study, the pandemic risk is calculated by combining vulnerability, hazard, and geodemography information. Infimap provides the People in Pixels geodemographic data, added not only the exposure of population distribution to COVID-19 but also the ratio of age. Beside those data, the daily distribution of COVID-19 cases, network data, business point, health facility point, residentials area, geodemographic (People in Pixels), and daily COVID-19 Community Mobility Reports is also been used in this study. The daily vulnerability and hazard data created since the first case on March 4th until August 21st. The hazard area is create based on the expected travel area of positive COVID-19 patient. While the vulnerability area is create using Spatial Multi Criteria Analysis (SMCA) of following data: service area of hospital, groceries (local market), and workspace. Further, the time series data of hazard and vulnerability area was inputted to develop the forecasting model based on the machine learning pipeline of Gaussian algorithm. As a result, this study shows the possibility to predict the future risk area of COVID-19 until the next 100 days condition, based on spatial timeseries forecasting model.

Original languageEnglish
Title of host publicationEarth Resources and Environmental Remote Sensing/GIS Applications XI
EditorsKarsten Schulz, Ulrich Michel, Konstantinos G. Nikolakopoulos
PublisherSPIE
ISBN (Electronic)9781510638815
DOIs
Publication statusPublished - 2020
EventEarth Resources and Environmental Remote Sensing/GIS Applications XI 2020 - Virtual, Online, United Kingdom
Duration: 21 Sep 202025 Sep 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11534
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceEarth Resources and Environmental Remote Sensing/GIS Applications XI 2020
Country/TerritoryUnited Kingdom
CityVirtual, Online
Period21/09/2025/09/20

Keywords

  • COVID-19
  • Geodemographic
  • GIS
  • Hazard
  • Risk
  • Spatial time series
  • Vulnerability

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