EARLY DETECTION MODEL OF THE SPREAD OF ACUTE RESPIRATORY INFECTIONS DISEASE CASE STUDY JAKARTA

• Main Author: Yehezkiel Sitorus, Marli
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/73007
• Institution: Institut Teknologi Bandung

Acute Respiratory Infections (ARIs) is one of the health threats that arise due to climate change and is one of the most common diseases worldwide. Previous studies on the relationship between climate variability and ARIs or ARIs pathogens have reported inconsistent findings, and the impact of climate factors on ARIs is still not fully understood. On the other hand, traditional epidemic models often only focus on the temporal dimension, while ignoring spatial variations that can provide useful insights into disease transmission patterns. This Final Project aims to review the relationship between historical climate data and ARIs, review the construction of early detection models for ARIs using climate information, as well as spatiotemporal information. The ARIs case incidence data used in this study were obtained from Dinas Kesehatan DKI Jakarta, and the climate data were obtained from Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) DKI Jakarta. Visual analysis was conducted by combining diagrams of ARIs case incidence data with climate factor data to examine the behavior of ARIs data, using the 70th and 75th percentiles of climate factor data as references. Temporal analysis was further conducted by developing regression models using autoregressive (AR) models, Spearman's Rank Correlation, Poisson regression models, and Negative Binomial regression models. The epidemic forest was used as the main tool in the epidemiological model involving spatial information. Data processing, various mathematical methods, and factor weighting analysis need to be applied to manage the input data in the epidemic forest algorithm. The results of the temporal analysis showed a correlation between climate and ARIs case incidence. The regression models yielded ????2 determination coefficients above 50%, except for areas with fewer than 35 cases. The epidemic forest showed that there are many sub-districts in Jakarta that can be the root of Pneumonia disease spread, thus requiring a rapid and efficient Pneumonia case recording system and urging the public to promptly report ongoing Pneumonia cases.