Reefs at Ko Samae San (S), Khao Ma Cho (K), and Ko Tao Mo (T), in the Gulf of Thailand (GoT) represent a biodiversity hotspot, and bacteria play significant roles in maintaining the health of these coral reefs and their biogeochemical cycles. Therefore, this study analyzed bacterial communities (microbiota) from healthy corals and nearby seawater and sediment, using B-RISA and 16S rRNA gene sequencing. Sampling was done in one dry and one wet season to provide an initial assessment of variation with environmental conditions. The most prevalent coral species were Porites lutea, Platygyra sinensis, Acropora humilis, and Acropora millepora. The B-RISA and the sequencing results were correlated, which increased confidence the results. The microbiota varied among corals, seawater, and sediment and between the wet and dry seasons. Percentages of bacteria with known functions varied among sample types and seasons, and their relative abundances supported previously reported essential functions, such as prevention of disease (e.g., Pseudoalteromonas, Psychrobacter, and Cobetia were more abundant on corals in the dry season). Pearson's correlations and multiple factor regressions identified dissolved oxygen (DO), temperature, salinity, and density as significant influences on the microbiota. The equations estimated the relative abundance of a community comprising 147 bacterial genera, as well as the relative abundance of Pseudomonas, Clostridium, Verrucomicrobium, and Epulopiscium (R 2 ≥ 0.721). These results represent the first descriptions of microbiota from corals, and surrounding seawater and sediments in the upper GoT.

Background: COVID-19 vaccination hesitancy is a global issue. Many people are concerned about experiencing side effects from the vaccine. This study evaluated satisfaction with the COVID-19 vaccine in the general population (GP) and healthcare workers (HCWs) in Bangkok, Thailand. Methods: A cross-sectional online survey was distributed from September-December 2021. Independent sample t-tests were used to compare GP and HCW participants’ total vaccine satisfaction scores as well as their satisfaction with varying vaccine types. Multiple linear regression was used to identify predictors of satisfaction scores among GP and HCWs. Results: A total of 780 valid questionnaire responses were obtained. The majority of GP participants (n = 390) had received their first (93.3%) and second (88.5%) vaccination shots by viral vector vaccine; however, 90% had not received a third dose (booster). In contrast, the majority of HCW participants (n = 390) had received their first (92.8%) and second (82.8%) vaccination doses by the inactivated vaccine, and 83% had received a third vaccine dose. HCWs had significantly higher total satisfaction scores than GP participants (p = 0.034), and they were also significantly more satisfied with the mRNA vaccine as a third dose (p = 0.001). Multiple linear regression models found less association with vaccine satisfaction among GP participants who had not isolated following exposure to COVID-19 and those who have never been at risk of infection (ᵦ −0.159; 95% CI −12.867, −1.877; p = 0.009). Among HCWs, being married (ᵦ 0.157; 95% CI 0.794, 3.278; p = 0.001) or divorced (ᵦ 0.198; 95% CI 3.303, 9.596; p < 0.01) was more closely associated with vaccine satisfaction than being single. Conclusion: HCWs were more satisfied with the type and efficacy of inactivated, viral vector, and mRNA vaccines than GP participants, and the former were also more satisfied with the cost of vaccine boosters. Our results indicate that satisfaction with the COVID-19 vaccine is based on academic knowledge sharing and the government’s promotion efforts. Future research will explore strategies to raise awareness about the importance of vaccination.

PurposeThis paper aims to uncover new factors that influence the spread of malaria.Design/methodology/approachThe historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain the simplicity of these numerous factors, the first procedure involved in executing the factor analysis where factors' groups related to malaria distribution were determined. Therefore, machine learning was deployed, and the confusion matrices are computed. The results from machine learning techniques were further analyzed with logistic regression to study the relationship of variables affecting malaria distribution.FindingsThis research can detect 28 new noteworthy factors. With all the defined factors, the logistics model tree was constructed. The precision and recall of this tree are 78% and 82.1%, respectively. However, when considering the significance of all 28 factors under the logistic regression technique using forward stepwise, the indispensable factors have been found as the number of houses without electricity (houses), number of irrigation canals (canals), number of shallow wells (places) and number of migrated persons (persons). However, all 28 factors must be included to obtain high accuracy in the logistics model tree.Originality/valueThis paper may lead to highly-efficient government development plans, including proper financial management for malaria control sections. Consequently, the spread of malaria can be reduced naturally.