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Background:
Spatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH...
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... the purpose of this study, it was decided to work at a local spatial scale in the Province of Leyte, due to the localized nature of the surveys and the high endemicity of the disease [28]. For the analysis, we identified a small area surrounding surveyed points (Fig. 1). This was done in order to select only surveyed barangays and to include information of all risk factors, avoiding areas without survey information (Fig. ...
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... in the Province of Leyte, due to the localized nature of the surveys and the high endemicity of the disease [28]. For the analysis, we identified a small area surrounding surveyed points (Fig. 1). This was done in order to select only surveyed barangays and to include information of all risk factors, avoiding areas without survey information (Fig. ...
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... of exposure and percentage of human cases within the groups (linear correlation, R 2 = 0.3). For the first three groups (A, B and C) the probability of ex- posure increases while the percentage of human cases also increases. For Group D, the group with more posi- tive cases, a minor decrease in the probability of expos- ure can be observed (Fig. 10). This could be explained by the distance to water bodies that has a negative cor- relation (Group C: -0.3, Group D: -0.02) with the probability of exposure values (0.47-0.55) calculated from our sBN for groups C (R 2 = 0.98) and D (R 2 = 0.96) (Fig. 11). For instance, for Groups A and B with one and two positive cases respectively, the ...
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... with more posi- tive cases, a minor decrease in the probability of expos- ure can be observed (Fig. 10). This could be explained by the distance to water bodies that has a negative cor- relation (Group C: -0.3, Group D: -0.02) with the probability of exposure values (0.47-0.55) calculated from our sBN for groups C (R 2 = 0.98) and D (R 2 = 0.96) (Fig. 11). For instance, for Groups A and B with one and two positive cases respectively, the distance to water bodies is higher for Group A (~980 m) than for Group B (~177 m), with an average exposure value of approximately 0.47 and 0.48, respectively (Fig. 10). Like- wise, for Groups C and D, the distance to water bodies is higher for Group D ...
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... values (0.47-0.55) calculated from our sBN for groups C (R 2 = 0.98) and D (R 2 = 0.96) (Fig. 11). For instance, for Groups A and B with one and two positive cases respectively, the distance to water bodies is higher for Group A (~980 m) than for Group B (~177 m), with an average exposure value of approximately 0.47 and 0.48, respectively (Fig. 10). Like- wise, for Groups C and D, the distance to water bodies is higher for Group D (~1100 m) than for Group C (~490 m), with an average probability of exposure values equal to 0.55 and 0.49, respectively (Fig. ...
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... is higher for Group A (~980 m) than for Group B (~177 m), with an average exposure value of approximately 0.47 and 0.48, respectively (Fig. 10). Like- wise, for Groups C and D, the distance to water bodies is higher for Group D (~1100 m) than for Group C (~490 m), with an average probability of exposure values equal to 0.55 and 0.49, respectively (Fig. ...
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... the purpose of this study, it was decided to work at a local spatial scale in the Province of Leyte, due to the localized nature of the surveys and the high endemicity of the disease [28]. For the analysis, we identified a small area surrounding surveyed points (Fig. 1). This was done in order to select only surveyed barangays and to include information of all risk factors, avoiding areas without survey information (Fig. ...
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... the purpose of this study, it was decided to work at a local spatial scale in the Province of Leyte, due to the localized nature of the surveys and the high endemicity of the disease [28]. For the analysis, we identified a small area surrounding surveyed points (Fig. 1). This was done in order to select only surveyed barangays and to include information of all risk factors, avoiding areas without survey information (Fig. ...
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... the first validation, the results show an increase in the probability of exposure as the proportion of human cases also increases, except for 17% of human cases where a reduction in the probability of exposure of 35.8% can be observed (Table 3). For the second valid- ation, four groups of buffers were observed: Group A with one positive case, Group B with two positive cases and Groups C and D with four and five positive cases, respectively (Fig. 9). A low correlation was found be- tween probability of exposure and percentage of human cases within the groups (linear correlation, R 2 = 0.3). For the first three groups (A, B and C) the probability of ex- posure increases while the percentage of human cases also increases. For Group D, the group with more posi- tive cases, a minor decrease in the probability of expos- ure can be observed (Fig. 10). This could be explained by the distance to water bodies that has a negative cor- relation (Group C: -0.3, Group D: -0.02) with the probability of exposure values (0.47-0.55) calculated from our sBN for groups C (R 2 = 0.98) and D (R 2 = 0.96) (Fig. 11). For instance, for Groups A and B with one and two positive cases respectively, the distance to water bodies is higher for Group A (~980 m) than for Group B (~177 m), with an average exposure value of approximately 0.47 and 0.48, respectively (Fig. 10). Like- wise, for Groups C and D, the distance to water bodies is higher for Group D (~1100 m) than for Group C (~490 m), with an average probability of exposure values equal to 0.55 and 0.49, respectively (Fig. ...
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... the first validation, the results show an increase in the probability of exposure as the proportion of human cases also increases, except for 17% of human cases where a reduction in the probability of exposure of 35.8% can be observed (Table 3). For the second valid- ation, four groups of buffers were observed: Group A with one positive case, Group B with two positive cases and Groups C and D with four and five positive cases, respectively (Fig. 9). A low correlation was found be- tween probability of exposure and percentage of human cases within the groups (linear correlation, R 2 = 0.3). For the first three groups (A, B and C) the probability of ex- posure increases while the percentage of human cases also increases. For Group D, the group with more posi- tive cases, a minor decrease in the probability of expos- ure can be observed (Fig. 10). This could be explained by the distance to water bodies that has a negative cor- relation (Group C: -0.3, Group D: -0.02) with the probability of exposure values (0.47-0.55) calculated from our sBN for groups C (R 2 = 0.98) and D (R 2 = 0.96) (Fig. 11). For instance, for Groups A and B with one and two positive cases respectively, the distance to water bodies is higher for Group A (~980 m) than for Group B (~177 m), with an average exposure value of approximately 0.47 and 0.48, respectively (Fig. 10). Like- wise, for Groups C and D, the distance to water bodies is higher for Group D (~1100 m) than for Group C (~490 m), with an average probability of exposure values equal to 0.55 and 0.49, respectively (Fig. ...
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... the first validation, the results show an increase in the probability of exposure as the proportion of human cases also increases, except for 17% of human cases where a reduction in the probability of exposure of 35.8% can be observed (Table 3). For the second valid- ation, four groups of buffers were observed: Group A with one positive case, Group B with two positive cases and Groups C and D with four and five positive cases, respectively (Fig. 9). A low correlation was found be- tween probability of exposure and percentage of human cases within the groups (linear correlation, R 2 = 0.3). For the first three groups (A, B and C) the probability of ex- posure increases while the percentage of human cases also increases. For Group D, the group with more posi- tive cases, a minor decrease in the probability of expos- ure can be observed (Fig. 10). This could be explained by the distance to water bodies that has a negative cor- relation (Group C: -0.3, Group D: -0.02) with the probability of exposure values (0.47-0.55) calculated from our sBN for groups C (R 2 = 0.98) and D (R 2 = 0.96) (Fig. 11). For instance, for Groups A and B with one and two positive cases respectively, the distance to water bodies is higher for Group A (~980 m) than for Group B (~177 m), with an average exposure value of approximately 0.47 and 0.48, respectively (Fig. 10). Like- wise, for Groups C and D, the distance to water bodies is higher for Group D (~1100 m) than for Group C (~490 m), with an average probability of exposure values equal to 0.55 and 0.49, respectively (Fig. ...
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... the first validation, the results show an increase in the probability of exposure as the proportion of human cases also increases, except for 17% of human cases where a reduction in the probability of exposure of 35.8% can be observed (Table 3). For the second valid- ation, four groups of buffers were observed: Group A with one positive case, Group B with two positive cases and Groups C and D with four and five positive cases, respectively (Fig. 9). A low correlation was found be- tween probability of exposure and percentage of human cases within the groups (linear correlation, R 2 = 0.3). For the first three groups (A, B and C) the probability of ex- posure increases while the percentage of human cases also increases. For Group D, the group with more posi- tive cases, a minor decrease in the probability of expos- ure can be observed (Fig. 10). This could be explained by the distance to water bodies that has a negative cor- relation (Group C: -0.3, Group D: -0.02) with the probability of exposure values (0.47-0.55) calculated from our sBN for groups C (R 2 = 0.98) and D (R 2 = 0.96) (Fig. 11). For instance, for Groups A and B with one and two positive cases respectively, the distance to water bodies is higher for Group A (~980 m) than for Group B (~177 m), with an average exposure value of approximately 0.47 and 0.48, respectively (Fig. 10). Like- wise, for Groups C and D, the distance to water bodies is higher for Group D (~1100 m) than for Group C (~490 m), with an average probability of exposure values equal to 0.55 and 0.49, respectively (Fig. ...
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Citations
... The PBCM could be the basis to develop a similar model for S. mekongi. The schistosomiasis exposure sBN could be used by local disease control teams to identify areas of exposure and improve the efficiency of mass drug administration [71]. A study to address how snail hosts and their interaction with S. mansoni influence model predictions indicates that model outputs, such as schistosome prevalence in human and snail populations, respond to the inclusion of snail age structure [72]. ...
In 1971, scientists from Mahidol University in Thailand and the Smithsonian Institution in the USA formed a research team to study a new species of Schistosoma in the Mekong River in Thailand and Laos. The studies, completed during 1971–1973, prior to the construction of any dams or restrictions to the natural flow regime of the Mekong River, provide a unique description of the natural ecological state of the river that can serve as a baseline for current research. The natural transmission of Schistosoma japonicum, Mekong Strain, was first reported on Khong Island, Laos in 1973 using sentinel mice. The first detailed description of the habitat ecology of the snail vector Neotricula aperta was done on-site in 1971 simultaneously with that research and is unique in providing the only description of the river shoreline habitat before any dams were built and any alteration of the natural flow regime was in place. Aggregating current information in a Place-Based Conceptual Model (PBCM) as an organizing template, along with current habitat models that combine ecological data with e-flows, can be developed and used as a tool to predict suitable habitats for snails. The natural flow regime of the Mekong River prior to any impoundments is described with current updates on the potential impacts of climate change and dams with flow-related snail habitat characteristics, including sediment drift and water quality. The application of the PBCM to describe and compare descriptive information on current and potential future N. aperta/S. mekongi habitat is discussed.
... The intermediate host, which is the vector of transmission of schistosomiasis in Central Sulawesi, is the snail Oncomelania hupensis lindoensis (Araujo et al. 2018). The presence of the O. h. ...
Sutrisnawati, Ramadhan A, Trianto M. 2022. Molecular identification of Oncomelania hupensis lindoensis, snail intermediate hosts of Schistosoma japonicum from Central Sulawesi, Indonesia. Biodiversitas 23: 5989-5994. Schistosomiasis is a zoonotic parasitic disease with Oncomelania hupensis lindoensis, intermediate snail hosts of Schistosoma japonicum. The spread of O. hupensis lindoensis snail habitat was found in the three areas of the Napu, Bada, and Lindu Highlands with an infection rate above 1%. Oncomelania hupensis lindoensis is primarily cryptic species that are morphologically difficult to identify and distinguish from other species. Consequently, it can be confused with the naming of species. One of the molecular approaches that can be used to identify the Oncomelania spp. quickly and accurately is DNA barcoding using the COI mitochondrial gene. However, the research on identifying Oncomelania spp. in Indonesia is still very limited. Therefore, this study aimed to identify six Oncomelania spp. from Napu and Lindu, Central Sulawesi using COI mitochondrial gene as a molecular marker for DNA barcoding. The method used in this study was a Polymerase Chain Reaction (PCR) method with universal primers, LCO-F and HCO-R. The data obtained were then analyzed using GeneStudio, DNASTAR, BLAST, Identification Engine, Mesquite, MEGAX, and BEAST. The analysis was conducted to obtain similarity, genetic distance and reconstruct a phylogenetic tree. The result revealed that all six samples of Oncomelania spp. collected from Napu and Lindu were identified in one species, namely Oncomelania hupensis lindoensis. This research is very important to be carried out regularly periodically so that it can be used as a basis for Schistosomiasis control program data and related sectors to eradicate snails effectively, efficiently, and on target.
... Within this field of study, Bayesian networks have been used to predict wildfire occurrence [107] and resulting land cover impacts [127], earthquake risk [82], urban flood risk [9], and risk of damage due to avalanches [53]. The field of epidemiology likewise has produced several new studies generating spatially explicit predictive risk maps [6,54,91] and identifying or predicting disease outbreaks [11,65]. ...
Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.
... Schistosomiasis or bilharziasis is a chronic debilitating parasitic infection caused by Schistosoma japonicum, a trematode that is still highly prevalent as intestinal schistosomiasis in Asian countries [1]. It is also considered as the second most socioeconomically devastating condition after malaria by the World Health organization because of its high prevalence in Africa and the far east causing considerable mortality and morbidity [2,3]. Especially in China, S. japonicum has reached a criterion of elimination, as well as, transmission interruption statuses with greater efforts [4], yet it remains a major health problem in the endemic villages of many provinces connected along Yellow river and Yangtze-Jiang basins [5,6]. ...
Purpose
To clarify unique non-contrast CT (NCCT) characteristics for early recognition of Schistosomal associated appendicitis (SAA) differentiating from Non-schistosomal associated appendicitis (NSA).
Material and methods
Clinical and pathological data of 50 cases with SAA and 60 cases with NSA who underwent emergency appendectomy were retrospectively compared to pre-surgical NCCT features such as direct and indirect signs of acute appendicitis as well as appendicoliths, colon calcifications as diagnostic criteria. Statistical methods such as Chi-square (χ²), t-tests, Principal component analysis (PCA), Binary Logistic regression (LR) and Factor Analysis (FA) were utilized to observe differences and isolate recognizable CT features of SAA. Pre and post hoc diagnostic performance of all criteria was calculated as sensitivity, specificity, and the Odds Ratio (OR).
Results
Age > 50 years, diameter > 13 mm, pneumatosis, peri appendiceal abscess, focal wall defect, perforation; Orbital, linear and point types of appendicular wall calcifications; sigmoid colon and cecal curvilinear calcifications were observed as unique characteristics with a sensitivity of 84–95% and specificity of 91–98% in predicting SAA by OR of 6.2 times. Pre and post hoc hypothetical analysis did not show any significance for all other factors.
Conclusion
Factors such as elderly age, CT features such as larger appendicular diameter, appendicular wall calcifications along with sigmoid colon, and cecal calcifications, signs of perforation or abscess are characteristic for early recognition of SAA.
... Complementing the eDNA approach is the utilization of probabilistic methods to identify potential risk areas for humans against exposure to S. japonicum. One such method, developed by Araujo Navas et al. (Araujo Navas et al., 2018), was based on a spatial Bayesian Network (sBN) analysis and used exposure risk factors to humans that include potential snail sites, geographical distribution of snail sites with different snail infection rates, and the cost for communities to access water bodies. They used data on schistosomiasis cases, which include actual household locations, and snail infection prevalence collected from three barangays from Alangalang, Leyte in 2015 and 2016. ...
The time is passing, and the worms are still a major struggle for local people in Asian countries, especially the less empowered and in a situation of social vulnerability. We are working in the field in Laos, Thailand, and the Philippines where the usual control programs based only on human treatment are partially effective. Areas with mass drug administration could diminish, but not eliminate STHs of endemic areas. The persistence of helminthic NTDs in the environment and animal hosts makes the eradication a very difficult task. Great changes in the landscapes of endemic areas, such as construction of dams, can change the fauna and the lifestyle of local people. Those changes can improve infrastructure, but it can also lead to social vulnerability. The challenge, then, is to conceive new and directed control programs for helminthiasis based on multi- and transdisciplinary approaches diminishing the health gap in a globalized world. In this short review, we summarize the actual scenario concerning the main helminths in Southeast Asia and how an environmental DNA approach and the use of GIS could contribute to surveillance and control programs.
Schistosomiasis is a zoonotic health problem caused by infection with the schistosoma class parasite, which lives in human blood vessels and around the intestines and bladder. Intermediate hosts in the form of snails and definitive hosts in animals and humans can be influenced by physical, chemical and biological environmental conditions. Schistosomiasis in Indonesia is caused by the trematode worm Schistosoma japonica (S. japonicum) with the intermediate host being the snail Oncomelania hupensis lindoensis. Schistosomiasis is still a public health problem in endemic areas. In Indonesia, schistosomiasis is only found in Central Sulawesi Province, namely the Napu Plateau and Bada Plateau, Poso Regency and Lindu Plateau, Sigi Regency. S. japonica is currently endemic in three very remote areas in Central Sulawesi Province, Indonesia. An integrated schistosomiasis control program has been implemented, however, the reported prevalence data shows a trend of increasing schistosomiasis prevalence in three endemic areas in Central Sulawesi. Eliminating schistosomiasis in Indonesia is not easy. This review will examine several challenges hindering the implementation and sustainability of schistosomiasis elimination programs, including the S. japonica intermediate snail control program and the use of medicinal plants for the treatment of schistosomiasis.
Schistosomiasis in Indonesia is endemic only in Napu and Bada highlands in Poso District and Lindu highlands in Sigi District, Central Sulawesi. Schistosomiasis control program has been done since 1982; however, it is not successful yet. The objective of this study was to re-identify the active focus area of O.h. lindoensis and the schistosomiasis control program by multi-sector and community. This study mapped the foci area and designed an action plan for schistosomiasis control by multi-sector in provincial level, Poso District, and Sigi District. The sectors involved are Agency for Regional Development, Regional Institute of Research and Development, Health Services, Agriculture Office, Plantation, and Animal Health Office, Maritime and Fisheries Office, Public Works Office, and Village Empowerment Office. The foci area of O.h. lindoensis were distributed in 16 villages in Napu, with a total of 242 foci area. The schistosomiasis control program by multi-sectors was making water catchment, making new paddy field, irrigation, molluscicide, cleaning foci area, draining, re-use of abandoned paddy field and plantation. There is a need for a regulation about budgeting and environmental management in sub-district and village level to support community participation in cleaning foci area, mass drug treatment, and stool survey.
Abstrak
Schistosomiasis di Indonesia hanya ditemukan di Dataran Tinggi Napu dan Dataran Tinggi Bada, Kabupaten Poso serta Dataran Tinggi Lindu, Kabupaten Sigi, Sulawesi Tengah. Sejak tahun 1982 telah dilakukan upaya pemberantasan tetapi sampai saat ini belum berhasil. Tujuan penulisan adalah mengidentifikasi kembali fokus keong perantara schistosomiasis yang masih aktif dan menyusun rencana aksi lintas sektor serta peran serta masyarakat dalam penanganan fokus keong. Kegiatan meliputi pemetaan kembali dan melakukan pertemuan menyusun rencana aksi pengendalian schistosomiasis dengan lintas sektor terkait di tingkat Provinsi Sulawesi Tengah, Kabupaten Poso dan Kab. Sigi. Organisasi Perangkat Daerah (OPD) yang terlibat antara lain Badan Perencanaan Pembangunan Daerah (Bappeda), Badan Penelitian dan Pengembangan Daerah (Balitbangda), Dinas Kesehatan, Dinas Pertanian, Perkebunan dan Kesehatan Hewan, Dinas Kelautan dan Perikanan, Dinas Pekerjaan Umum dan Dinas Pemberdayaan Masyarakat Desa (PMD). Fokus keong Oncomelania hupensis lindoensis tersebar pada 16 desa di Dataran Tinggi Napu. Jumlah fokus keong O. hupensis lindoensis 242 fokus. Rencana aksi lintas sektor dengan pembuatan bak penangkap air, pencetakan sawah, pembuatan saluran air permanen dan penyemprotan moluskisida sedangkan peran serta masyarakat berupa pembersihan, pengeringan, pengaktifan sawah dan kebun. Perlu ada regulasi pembiayaan untuk pengembangan manajeman lingkungan dan regulasi di tingkat kecamatan atau desa untuk peningkatan peran serta masyarakat dalam pelaksanaan pembersihan fokus keong, pengobatan massal dan survei tinja.
