Province clustering based on the percentage of communicable disease using the BCBimax biclustering algorithm.

Indonesia needs to lower its high infectious disease rate. This requires reliable data and following their temporal changes across provinces. We investigated the benefits of surveying the epidemiological situation with the imax biclustering algorithm using secondary data from a recent national scale survey of main infectious diseases from the National Basic Health Research (Riskesdas) covering 34 provinces in Indonesia. Hierarchical and k-means clustering can only handle one data source, but BCBimax biclustering can cluster rows and columns in a data matrix. Several experiments determined the best row and column threshold values, which is crucial for a useful result. The percentages of Indonesia's seven most common infectious diseases (ARI, pneumonia, diarrhoea, tuberculosis (TB), hepatitis, malaria, and filariasis) were ordered by province to form groups without considering proximity because clusters are usually far apart. ARI, pneumonia, and diarrhoea were divided into toddler and adult infections, making 10 target diseases instead of seven. The set of biclusters formed based on the presence and level of these diseases included 7 diseases with moderate to high disease levels, 5 diseases (formed by 2 clusters), 3 diseases, 2 diseases, and a final order that only included adult diarrhoea. In 6 of 8 clusters, diarrhea was the most prevalent infectious disease in Indonesia, making its eradication a priority. Direct person-to-person infections like ARI, pneumonia, TB, and diarrhoea were found in 4-6 of 8 clusters. These diseases are more common and spread faster than vector-borne diseases like malaria and filariasis, making them more important.

Factors Affecting Public Non-compliance With Large-scale Social Restrictions to Control COVID-19 Transmission in Greater Jakarta, Indonesia.

OBJECTIVES: The Indonesian government issued large-scale social restrictions (called Pembatasan Sosial Berskala Besar, or PSBB) at the beginning of the coronavirus disease 2019 (COVID-19) pandemic to control the spread of COVID-19 in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Greater Jakarta). Public compliance poses a challenge when implementing large-scale social restrictions, and various factors have contributed to public non-compliance with the regulation. This study aimed to determine the degree of non-compliance and identify the factors that contributed to public non-compliance with the PSBB in Greater Jakarta, Indonesia. METHODS: This was a quantitative study with a cross-sectional design. A total of 839 residents of Greater Jakarta participated in this study. Data were collected online using a Google Form, and convenience sampling was undertaken. Univariate and multivariate analyses were performed to explore the relationships between public non-compliance with the PSBB regulation and socio-demographic variables, respondents' opinion of the PSBB, and social capital. RESULTS: A total of 22.6% of subjects reported participating in activities that did not comply with the PSBB. The variables that most affected non-compliance with the PSBB were age, gender, income, opinion of the PSBB, and social capital. CONCLUSIONS: Strengthening social capital and providing information about COVID-19 prevention measures, such as washing one's hands with soap, wearing masks properly, and maintaining social distancing, is essential. Robust public understanding will foster trust and cooperation with regard to COVID-19 prevention efforts and provide a basis for mutual agreement regarding rules/penalties.