ABSTRACT
The rapid spread of COVID-19 requires rapid management. Prompt treatment is needed to prevent the spread of this disease, which could be minimized or isolated in one place so that it does not spread to other places. This study was conducted to discover a model of the surveillance system in real time and to analyze the change in its distribution pattern. This study was conducted in the city of Makassar, South Sulawesi, Indonesia, involving 30 volunteers. Two devices were used, the Internet reverse transcription loop-mediated isothermal amplification (iRTLAMP) and IoT button application, to provide spatial data in the form of patient points exposed to COVID-19. Furthermore, three scenarios were applied to see the pattern of data distribution. The data recorded in the cloud database were retrieved with a created application and then analyzed using Kernel Density Estimation (KDE) and Point Pattern Analysis (PPA) to observe the distribution of patterns in real time. The analysis utilizing KDE with the Gaussian kernel function as the kernel revealed significant changes in the probability distribution, which could be seen from color changes in the map. The centrographic analysis revealed that the mean and median points of the three scenarios changed in various ways within approximately 700 m to 1.7 km. Meanwhile, the radius of minimal bounding circle behaved similarly and appeared to change depending on the scenario, from a radius of 5.57 (initial) km to 6.55 km (scenario 1), 5.57 km (scenario 2) and 6.22 km (scenario 3). The standard distance also showed a change from 4.53 km to 4.60 km (scenario 1), 4.70 km (scenario 2) and 5.40 km (scenario 3). Simulations carried out using the developed system showed that the use of internet devices could help monitor people exposed to COVID-19 by changing patterns and distribution points. Therefore, decision makers could take preventive actions earlier so that this disease does not spread quickly. © 2023, Mahidol University - ASEAN Institute for Health Development. All rights reserved.