In Indonesia, dengue has become one of the hyperendemic diseases. Dengue consists of three clinical phases—febrile phase, critical phase, and recovery phase. Many patients have died in the critical phase due to the lack of proper and timely treatment. Therefore, we developed models that can predict the severity level of dengue based on the laboratory test results of the corresponding patients using Artificial Neural Network (ANN) and Discriminant Analysis (DA). In developing the models, we used a very small dataset. It is shown that ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). This is the proposed model in this research. The proposed model will be able to help physicians in predicting the severity level of dengue patients before entering the critical phase. Furthermore, it will ease physicians in treating dengue patients early, so fatal cases or deaths can be avoided.

Background: Since the beginning of the coronavirus disease 2019 (COVID-19) outbreak, dynamic mutations in the receptor-binding domain (RBD) in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein have altered the pathogenicity of the variants of the virus circulating in Indonesia. This research analyzes the mutation trend in various RBD samples from Indonesia published in the Global Initiative on Sharing All Influenza Data (GISAID) database using genomic profiling. Method: Patients in Indonesia infected with SARS-CoV-2, whose samples have been published in genomic databases, were selected for this research. The collected data were processed for analysis following several bioinformatics protocols: visualization into phylogenetic trees, 3D rendering, and the assessment of mutational impact. Results: In Indonesia, there are 25 unique SARS-CoV-2 clades and 318 unique SARS-CoV-2 RBD mutations from the earliest COVID-19 sample to samples collected in 2022, with T478K being the most prevalent RBD mutation and 22B being the most abundant clade. The Omicron variant has a lower docking score, higher protein destabilization, and higher K_D than the Delta variant and the original virus. Conclusions: The study findings reveal a decreasing trend in virus pathogenicity as a potential trade-off to increase transmissibility via mutations in RBD over the years.

Dengue virus (DENV) infection is a significant public health concern in Indonesia, with increasing cases and severity posing challenges to the country’s healthcare systems. This study aims to develop and validate a machine learning-based prediction model for assessing dengue infection cases and their severity. The model incorporates epidemiological, clinical, and environmental factors to enhance early detection and resource allocation. Additionally, the model can be utilized to support logistics planning, such as the distribution of diagnostic kits and the preparation of health facilities in each region across Indonesia, ensuring timely and targeted responses to potential outbreaks. We applied various machine learning algorithms, including logistic regression, random forest, XGBoost, and SVM models, and evaluated them to determine the most effective predictive model. The results demonstrate the model’s efficacy in predicting dengue cases and severity, which can support public health interventions and clinical decision-making. Geospatial clustering and correlation matrices were generated to visualize risk patterns and support predictions. The XGBoost model demonstrated the highest performance, achieving an accuracy of 85%. Our findings suggest that integrating clinical and environmental data through machine learning (ML) techniques can significantly improve early detection and inform resource allocation strategies. The model offers a promising approach for public health surveillance and targeted interventions in dengue-endemic regions.

Background: Indonesia has experienced one of the highest burdens of highly pathogenic avian influenza (HPAI) H5N1 globally since 2003, making it a major public health concern in Southeast Asia. There are few updates on the factors influencing the spread of avian influenza cases in Indonesia, and updated information is needed to raise awareness. This literature review summarizes data on the epidemiology, genetic evolution of the virus, zoonotic transmission risks, control efforts, and the One Health framework in the Indonesian context. Persistent virus circulation, limited biosecurity, and sociocultural factors continue to challenge disease control. Strengthening genomic surveillance, public awareness, and multi-sectoral coordination is essential to reduce the threat of future pandemics. Addressing avian influenza requires robust, integrated One Health surveillance, investment in local diagnostic capacity, timely response systems, and community engagement. Surveillance in live bird markets, animal movement control, and real-time data integration are critical components of pandemic prevention strategies.

The Efficiency of viral replication in cells depends on the capability of supporting virus replication by the cells. We characterized the effectiveness of Dengue Virus Serotype 1 (DENV-1) replication in various cell lines and various multiplicity of infection (MOI) starting from 2 FFU/cell up to 0,3125 FFU/cell. We used HepG2 and Huh-7 human hepatocyte cell lines and in addition, we also used non-human kidney cells (Vero cells). DENV-1 strain IDS 11/2010 was isolated from DF patients and previously propagated in Huh7 and Vero cells as DENV-1-adapted Huh-7 and DENV-1-adapted Vero cells, respectively. Huh7 cells, Hep G2 cells, and Vero cells were infected with DENV-1 at various MOI and incubated for 48 hours at 37⁰C with 5% CO₂. DENV-infected cells were determined by indirect immuno-peroxidase staining using 3,3'-Diaminobenzidine (DAB). DENV-1 infected cells as foci were counted under inverted light microscopy and were used to determine the virus titer. The virus was adapted to Huh-7 and Vero cells, with results showing that Vero cells exhibited the highest replication efficiency, evidenced by significant viral titers. Among human hepatocyte cell lines, DENV-1 demonstrated greater replication in Huh-7 cells than in HepG2 cells. Notably, no foci formation was observed in HepG2 cells after 48 hours of infection. These findings underscore the suitability of Vero and Huh-7 cells as optimal environments for DENV-1 replication, offering valuable insights for enhancing laboratory diagnostics and advancing antiviral strategies and vaccine development against DENV-1.

SARS-CoV-2 virus undergoes mutation, leading to the virus's evolution and modifications in the characteristics of the virus. Omicron, including BA.2 subvariant, is currently the predominant variant in SARS-CoV-2 infection. There are no reports regarding its properties or the utilization of BA.2 Indonesian isolate for therapy and vaccine development. Therefore, this study evaluated appropriate host cells for Omicron BA.2 Indonesian isolate via susceptibility tests. The Omicron BA.2 from Indonesia was exposed to three mammalian-ACE2-expressing cell lines. Sharing amino acids between BA.2 from Indonesia and previous VOC Omicron subvariants was performed using a simple in silico comparison method. The results showed that the virus could not infect HepG2 and Huh-7D12 due to no foci forming on those cell lines. Moreover, we also found that BA.2 Indonesian isolate has a unique amino acid alteration on spike protein. According to the findings, the Omicron BA.2 from Indonesia could propagate on Vero E6 cell lines, and the mutations could play a role in the virus's changing infection mechanism. A deeper in vitro and in silico experiment could enhance the findings by comparing all BA.2 sequences from Indonesia and analyzing the infection mechanism by each single mutation using pseudovirus.