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Indonesia is known to be endemic for rabies in several areas, especially in Sumatra, Kalimantan, Sulawesi, and Flores Islands. Currently, vaccinating dogs has been shown to be the most cost-effective strategy for preventing rabies in humans. Postvaccination monitoring should be carried out to evaluate the success of vaccination by measuring antibody titers in serum of vaccinated dogs. Serological methods for monitoring rabies-specific antibody titers can be carried out using enzyme-linked immunosorbent assay (ELISA) methods as recommended by the World Organization for Animal Health (WOAH). Therefore, the development of the in-house ELISA (BukTi-Vet) that we have carried out in order to support postvaccination monitoring in dogs needs to be evaluated for its diagnostic performance compared to commercial ELISA kits. The diagnostic performance of each ELISA kit was evaluated using 111 known positive and 47 negative serums. Each known positive and negative serum will be tested using the three rabies ELISA kits used in this study. BukTi-Vet is an in-house ELISA for the detection of rabies-specific IgG antibodies that have been developed with sensitivity, specificity, and accuracy of 98.19%, 97.87%, and 98.1%, respectively. Based on the value of its positive and negative clinical utility index, BukTi-Vet is excellent for use in immunoassays directed for confirmatory (0.97) as well as screening (0.94) tests. BukTi-Vet shows a very good agreement with both Platelia II and RFFIT, so it is convincing to be further refined into a diagnostic kit. Tests of field sera from dogs vaccinated with various vaccines should be performed to provide more complete information on diagnostic performance. BukTi-Vet showed a very good agreement with RFFIT, while Pusvetma and Platelia II only showed good agreement. The average value of BukTi-Vet compatibility with RFFIT can reach 94%. [ABSTRACT FROM AUTHOR]

Rabies continues to pose a significant threat to human and animal health in regions of Indonesia. Indonesia has an extensive network of veterinary diagnostic laboratories and the 8 National laboratories are equipped to undertake diagnostic testing for rabies using the commercially-procured direct fluorescent antibody test (FAT), which is considered the reference (gold standard) test. However, many of the Indonesian Provincial diagnostic laboratories do not have a fluorescence microscope required to undertake the FAT. Instead, certain Provincial laboratories continue to screen samples using a chemical stain-based test (Seller’s stain test, SST). This test has low diagnostic sensitivity, with negative SST-tested samples being forwarded to the nearest National laboratory resulting in significant delays for completion of testing and considerable additional costs. This study sought to develop a cost-effective and diagnostically-accurate immunoperoxidase antigen detection (RIAD) test for rabies that can be readily and quickly performed by the resource-constrained Provincial laboratories. This would reduce the burden on the National laboratories and allow more rapid diagnoses and implementation of post-exposure prophylaxis. The RIAD test was evaluated using brain smears fixed with acetone or formalin and its performance was validated by comparison with established rabies diagnostic tests used in Indonesia, including the SST and FAT. A proficiency testing panel was distributed between Provincial laboratories to assess the reproducibility of the test. The performance of the RIAD test was improved by using acetone fixation of brain smears rather than formalin fixation such that it was of equivalent accuracy to that of the World Organisation for Animal Health (OIE)-recommended FAT, with both tests returning median diagnostic sensitivity and specificity values of 0.989 and 0.993, respectively. The RIAD test and FAT had higher diagnostic sensitivity than the SST (median = 0.562). Proficiency testing using a panel of 6 coded samples distributed to 16 laboratories showed that the RIAD test had good reproducibility with an overall agreement of 97%. This study describes the successful development, characterisation and use of a novel RIAD test and its fitness for purpose as a screening test for use in provincial Indonesian veterinary laboratories. [ABSTRACT FROM AUTHOR]

Background and Aim: Some Indonesian islands, including Sumatra, Kalimantan, Sulawesi, Java, and East Nusa Tenggara, have endemic rabies. Rabies outbreaks in Bali began from 2008 to 2011 and continue to occur sporadically. This study aimed to study the molecular analysis and geographical distribution of Indonesian rabies virus (RABV) from 2016 to 2021 and compare to previous periods. Materials and Methods: Virus isolates from 2016 to 2021 were extracted from dog brains and sequenced at the nucleoprotein gene locus. They were compared with data sequences available in the GenBank database. Indonesian RABV from the previous three periods (before 1989, 1997–2003, and 2008–2010) was extracted from the GenBank database. The genetic diversity in this study was based on the N gene of Indonesian RABV. Results: Asian RABV, which is genetically close to the Indonesian virus, is a virus from China (ASIA-3 cluster) and from the Southeast Asia region, namely, virus isolates from Sarawak and Malaysia and some Cambodian isolates. Rabies virus, which was isolated from the Bali islands, was the new cluster first detected and published in Bali, Indonesia, in 2008, while RABV from West Sumatra Province, which was isolated from 2016 to 2021, was also considered a new cluster that is genetically distant from other clusters in Indonesia. Conclusion: The RABV in Indonesia is divided into five clusters. The isolates from West Sumatra Province from 2016 to 2021 were a new cluster genetically distant from other Indonesian viruses. [ABSTRACT FROM AUTHOR]

Background and Aim: Lumpy skin disease (LSD) is an emerging epidemic in livestock in Indonesia. It was first reported in the Indragiri Hulu Regency of Riau Province, which has more cases than the surrounding regencies. This study aimed to identify the risk factors and generate a logistic regression model and vulnerability map of LSD in the Indragiri Hulu Regency. Materials and Methods: We used a structured questionnaire to interview the case and control farm owners to evaluate the risk factors. We evaluated 244 samples, consisting of 122 case and control farm samples each. At the cattle farm level, the risk factor data related to LSD were analyzed using descriptive statistics, bivariate analysis with Chi-square, and odds ratio, while the logistic model was derived using multivariate logistic regression analysis. Using variables, such as the number of cases and risk factor variables included in the model logistic, and the temperature, humidity, and rainfall data from the Meteorology, Climatology, and Geophysical Agency, we analyzed the vulnerability map of LSD in the regency using scoring, weighting, and overlay methods. Results: Ten significant risk factors were associated with LSD occurrence. The LSD model obtained from the logistic regression analysis was LSD (Y) = -3.92095 + 1.13107 (number of cattle >3) + 1.50070 (grazing cattle together with other farmers' cattle) + 1.03500 (poor management of farm waste/dirt) + 2.49242 (presence of livestock collectors/traders near the farm location) + 1.40543 (introduction of new livestock) + 2.15196 (lack of vector control measures on the farm). The LSD vulnerability map indicated that the villages with high vulnerability levels were Rantau Bakung, Kuantan Babu, and Sungai Lala in the Rengat Barat, Rengat, and Sungai Lala subdistricts, respectively. Conclusion: We found 10 significant risk factors associated with LSD occurrence. The LSD model included the number of cattle (>3), cograzing with other farmers' cattle, poor management of farm waste/dirt, the presence of livestock collectors/traders near the farm, introduction of new livestock, and lack of vector control measures on the farm. The LSD vulnerability map indicated that villages with high vulnerability levels included Rantau Bakung in the Rengat Barat subdistrict, Kuantan Babu in the Rengat subdistrict, and Sungai Lala in the Sungai Lala subdistrict. [ABSTRACT FROM AUTHOR]