The development and advancement of information and communication technologies have led to major changes in industry and the labor system in Indonesia. In the context of the digital economy, Indonesia needs to immediately improve digital labor policies based on research results. However, studies on Indonesian digital workforces mostly come from global nonacademic publications, which acknowledge the limitation of the workforces. This study addresses the gaps between the supply and demand of digital workforces in 2021–2025 by conducting a Bayesian analysis on the data from the 2018 Indonesian Statistics Bureau and the 2020 ILO ICT job demand forecast. According to the findings, the supply of digital workforces will outnumber the demand, which is expected to be 600,000 workers per year. This surplus number poses a new challenge for the government if the available workforce lacks the competencies needed in the industry. According to the study, IT system programmer/developer/administrator/system analyst and IT web designer/developer will still be popular job roles during this time. It is suggested that improving these digital skills in the current and future workforces should be a top priority for the government.

The coronavirus disease (COVID‐19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space–time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID‐19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county‐level data of West Java Province, Indonesia.

Dengue disease has serious health and socioeconomic consequences. Preventive actions are needed to avoid outbreaks. Bayesian spatiotemporal models with conditional autoregressive (CAR) and random walk (RW) priors are two common smoothing approaches used in disease mapping to develop early warning systems and action plans. However, this approach can lead to over‐smoothing, such that discontinuities in the risk surface are “averaged” out. Moreover, local variation in the relationship between disease risk and spatiotemporal risk factors can be concealed by the assumption of homogeneous regression effects. To overcome these problems, we propose a two‐stage spatiotemporal clustering approach. In the first stage, an agglomerative hierarchical clustering algorithm is applied to map out the complete set of cluster configurations. In the second stage, the optimal cluster configuration is selected using a spatiotemporal model with spatiotemporally varying coefficients. The methodology is applied to data for 30 districts in the city of Bandung, Indonesia, for the period January 2012 to December 2016. Weather factors are used as the risk factors. High‐risk and low‐risk clusters are identified that strongly vary across space and time, with sunshine duration, precipitation, evaporation, and humidity as the most important risk factors.

Digital disparities remain a significant challenge in Indonesia, particularly across its diverse regions, with uneven access to digital infrastructure, skills, and economic opportunities. This study aims to map these digital disparities at the district level, analyze the spatial distribution and clustering of digital transformation using the Digital Society Index of Indonesia (IMDI), and investigate the key drivers of digital inequality across four core pillars: Infrastructure and Ecosystem, Digital Skills, Empowerment, and Jobs. To measure the IMDI, primary data were collected from the industrial sector and the general population over three years (2022–2024), combined with secondary data on internet usage and service standards. A multistage random sampling approach ensured representativeness, considering demographic variations and industrial segments. The analysis employed spatiotemporal methods to capture temporal trends and spatial clustering. The results revealed a significant IMDI increase from 37.80 in 2022 to 43.18 in 2023, followed by stability at 43.34 in 2024. The hotspots of digital transformation remain concentrated on Java Island, while low spots persist in eastern Indonesia. This study provides critical insights into Indonesia’s digital readiness, identifying priority areas for targeted interventions to bridge the digital divide and foster equitable digital development.

Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the “big n” problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January–July and a decrease in the period August–December.