Outbreak In Thailand Research Articles

The Model of Nursing Administration and Quality of Working Life Management of Nurses after COVID-19 Pandemics, in Thailand

The research results showed that 1) Conditions and guidelines the nursing administration and quality of working life in during and after the pandemics COVID-19 were explored and found that executives understood their personnel and emphasized that relevant personnel must perform their duties according to the measures to prevent the spread of COVID-19. Problems found included insufficient personnel for the work and overlapping roles. Factors that supported personnel to manage their work quality after the COVID-19 outbreak included teamwork, participation in planning and solving work safety issues, having time for work and family, and job satisfaction. The guidelines for operations after the COVID-19 outbreak should include changes to the work system, building morale, participation, teamwork, and job satisfaction. Good time management could affect the quality of life of nurses. 2) In the study of measurement model, it was found that all sub-component latent variables were suitable as the observed variables of each main component, and all main components were suitable as the observed variables of the nursing administration model and the management of working life quality of nurses after the COVID-19 outbreak. When checking the consistency of the measurement model, the measurement model was consistent with the empirical data, thus confirming the development of the nursing administration model and the management of working life quality of nurses after the COVID-19 outbreak in Thailand, by the linear structural component analysis method. 3) the model of nursing administration and quality of working life management of nurses after the COVID-19 pandemic in Thailand or SWAP FECT MODEL; it was found that all experts confirmed that the model was a model with practical steps, consisting of 2 main components, 10 sub-components, and 66 indicators. The main components of nursing administration in the VUCA model had 3 sub-components: transparency, empowerment of subordinates, and collaboration. The main components of management of working life quality had 7 sub-components: agency support, work safety, work participation, work satisfaction, well-being, self-development, and free time management. There were 3 steps of good management: 1. Inputs 2. Processes and 3. Outputs.

One-size measures do not fit all areas: Evaluation of area-specific control of foot and mouth disease in Thailand using bioeconomic modelling

Information on the epidemiological and economic consequences of control measures is fundamental to design effective foot and mouth disease (FMD) control measures. One approach to obtaining this information is through bioeconomic modelling. In this study, a bioeconomic model was used to evaluate FMD control in two different study areas in Thailand: a high farm density area predominantly consisting of dairy farms and a low farm density area with mixed farm types. The bioeconomic model consists of an epidemiological part and an economic part. For the epidemiological part, a stochastic between-farm transmission model was constructed with transmission parameters estimated from FMD outbreaks in Thailand. The outputs from the epidemiological model, i.e. the number of infected farms, the number of affected farms and the outbreak duration, are used as inputs for economic model to calculate the economic consequences. We applied the simulation model with four FMD control measures: culling the animals of infected farms, ring vaccination, animal movement restrictions and isolation of infected farms. Furthermore, we included effect of farmers' compliance to asses its effect on control measures. The simulated FMD outbreaks in the low farm density area were small, thus control measures did not greatly affect the size of outbreaks and, therefore, did not have a positive economic return. In contrast, in the high farm density area, FMD outbreaks were large without control measures. All measures reduced the size of the outbreaks but resulted in different total costs. In terms of outbreak control, culling infectious farms was the best option, but its total cost was higher than ring vaccination or isolation of infected farms. In terms of cost-effectiveness, ring vaccination was the best measure. If farmers' compliance were low, all control measures would be ineffective, resulting in high total cost of the outbreak. The cost distribution between compliant and non-compliant farms showed that non-compliant farms paid more than compliant farms, except for the ring vaccination scenario. The results emphasize the economic significance to customize control measures specific to the area's conditions and highlight the importance of farmers' compliance when designing control measures.

The evolutionary and molecular history of a chikungunya virus outbreak lineage.

In 2018-2019, Thailand experienced a nationwide spread of chikungunya virus (CHIKV), with approximately 15,000 confirmed cases of disease reported. Here, we investigated the evolutionary and molecular history of the East/Central/South African (ECSA) genotype to determine the origins of the 2018-2019 CHIKV outbreak in Thailand. This was done using newly sequenced clinical samples from travellers returning to Sweden from Thailand in late 2018 and early 2019 and previously published genome sequences. Our phylogeographic analysis showed that before the outbreak in Thailand, the Indian Ocean lineage (IOL) found within the ESCA, had evolved and circulated in East Africa, South Asia, and Southeast Asia for about 15 years. In the first half of 2017, an introduction occurred into Thailand from another South Asian country, most likely Bangladesh, which subsequently developed into a large outbreak in Thailand with export to neighbouring countries. Based on comparative phylogenetic analyses of the complete CHIKV genome and protein modelling, we identified several mutations in the E1/E2 spike complex, such as E1 K211E and E2 V264A, which are highly relevant as they may lead to changes in vector competence, transmission efficiency and pathogenicity of the virus. A number of mutations (E2 G205S, Nsp3 D372E, Nsp2 V793A), that emerged shortly before the outbreak of the virus in Thailand in 2018 may have altered antibody binding and recognition due to their position. This study not only improves our understanding of the factors contributing to the epidemic in Southeast Asia, but also has implications for the development of effective response strategies and the potential development of new vaccines.

Incidence, diagnosis, and management of orofacial pain among new patients receiving tertiary care in Thailand: A 6-year retrospective study comparing before and during the COVID-19 outbreak.

There is insufficient data on orofacial pain related to the COVID-19 outbreak in Thailand. To investigate the incidence, diagnosis and management of orofacial pain among new patients over the last 6 years, before and during the COVID-19 outbreak. Medical records from new patients who first visited Chulalongkorn Dental Hospital between 2017 and 2022 were retrospectively investigated. The sample size was determined, and proportional stratified random sampling was employed to distribute the sample number proportionally across each year. The hospital number of each patient was randomised using online software as a sampling strategy. A data collection form was developed and used to gather information from the digital data system. At the first visit, 770 out of 1359 patients (56.7/100 people, 95% CI: 54.0-59.3) reported orofacial pain defined as acute, chronic and unclassified pain. The most common chief complaints based on the AAOP classification were odontogenic pain (90.00%) and temporomandibular disorders (6.10%). The top three provisional modified ICD-10-TM diagnoses among these patients were pulp diseases (21.95%), impacted teeth (20.65%) and dental caries (9.09%). 81.87% of dental students' provisional diagnoses matched the final diagnoses given by dental specialists. Only 63.38% of orofacial pain patients were completely managed during the COVID-19 pandemic with common procedures being surgical removal, extraction and root canal therapy. Half of Thai dental patients seeking tertiary care at a university dental hospital reported orofacial pain, which was not impacted by the COVID-19 pandemic. In contrast, dysfunction-related problems in the orofacial area significantly increased during the pandemic.

Prevalence and risk factors of cytomegalovirus reactivation in critically Ill patients with COVID-19 pneumonia.

In critically ill patients with COVID-19, secondary infections are potentially life-threatening complications. This study aimed to determine the prevalence, clinical characteristics, and risk factors of CMV reactivation among critically ill immunocompetent patients with COVID-19 pneumonia. A retrospective cohort study was conducted among adult patients who were admitted to ICU and screened for quantitative real-time PCR for CMV viral load in a tertiary-care hospital during the third wave of the COVID-19 outbreak in Thailand. Cox regression models were used to identify significant risk factors for developing CMV reactivation. A total of 185 patients were studied; 133 patients (71.9%) in the non-CMV group and 52 patients (28.1%) in the CMV group. Of all, the mean age was 64.7±13.3 years and 101 patients (54.6%) were males. The CMV group had received a significantly higher median cumulative dose of corticosteroids than the non-CMV group (301 vs 177 mg of dexamethasone, p<0.001). Other modalities of treatments for COVID-19 including anti-viral drugs, anti-cytokine drugs and hemoperfusion were not different between the two groups (p>0.05). The 90-day mortality rate for all patients was 29.1%, with a significant difference between the CMV group and the non-CMV group (42.3% vs. 24.1%, p = 0.014). Median length of stay was longer in the CMV group than non-CMV group (43 vs 24 days, p<0.001). The CMV group has detectable CMV DNA load with a median [IQR] of 4,977 [1,365-14,742] IU/mL and 24,570 [3,703-106,642] in plasma and bronchoalveolar fluid, respectively. In multivariate analysis, only a cumulative corticosteroids dose of dexamethasone ≥250 mg (HR = 2.042; 95%CI, 1.130-3.688; p = 0.018) was associated with developing CMV reactivation. In critically ill COVID-19 patients, CMV reactivation is frequent and a high cumulative corticosteroids dose is a significant risk factor for CMV reactivation, which is associated with poor outcomes. Further prospective studies are warranted to determine optimal management.

Characterization of antibody response to SARS-CoV-2 Orf8 from three waves of COVID-19 outbreak in Thailand.

A dynamic of virus adaptation and a mass vaccination campaign could significantly reduce the severity of clinical manifestations of COVID-19 and transmission. Hence, COVID-19 may become an endemic disease globally. Moreover, mass infection as the COVID-19 pandemic progressed affected the serology of the patients as a result of virus mutation and vaccination. Therefore, a need exists to acquire accurate serological testing to monitor the emergence of new outbreaks of COVID-19 to promptly prevent and control the disease spreading. In this study, the anti-Orf8 antibodies among samples collected in Thailand's first, fourth, and fifth waves of COVID-19 outbreaks compared with pre-epidemic sera were determined by indirect ELISA. The diagnostic sensitivity and specificity of the anti-Orf8 IgG ELISA for COVID-19 samples from the first, fourth, and fifth waves of outbreaks was found to be 100% compared with pre-epidemic sera. However, the diagnostic sensitivity and specificity of the anti-Orf8 IgG ELISA for a larger number of patient samples and controls from the fifth wave of outbreaks which were collected on day 7 and 14 after an RT-PCR positive result were 58.79 and 58.44% and 89.19 and 58.44%, respectively. Our data indicated that some of the controls might have antibodies from natural past infections. Our study highlighted the potential utility of anti-Orf8 IgG antibody testing for seroprevalence surveys but still warrants further investigations.

Predictive Analysis of COVID-19 Epidemic in Thailand: Evaluating Control Lockdown Measures using LSTM Networks

This study addresses the critical objective of evaluating the effectiveness of non-pharmaceutical lockdown measures implemented during COVID-19 outbreaks in Thailand. Assessing the outcome of these measures provides valuable insight that can inform and guide response to future outbreaks. Utilizing a closed-loop forecasting model built on Long Short-Term Memory (LSTM) networks, the research focuses on achieving precise daily forecasts of COVID-19 cases. The methodology involves optimizing hyperparameters through grid-search and incorporating training data from other countries that implemented similar measures. The LSTM, configured with an optimal number of hidden processing units, utilizes past lagged data of daily infected cases as predictors to generate multi-step-ahead predicted values, which are subsequently used as predictors in a recursive approach. As a result, the predicted cases closely align with measured data, facilitating the estimation of the effective reproduction number (Reff) to assess the performance of lockdown measures. The effectiveness of the lockdown measures is quantified at different time intervals: 51%, 41%, and 23% one day after implementation, increasing to 84%, 98%, and 34% after one week, and reaching 96%, 99%, and 73% at the endpoint of the first, second, and fourth waves of infection, respectively. Throughout these waves, the final Reff remains below 1, indicating ongoing but controllable COVID-19, demonstrating the efficacy of the implemented lockdown measures. It is noted that these results are based on specific LSTM model, as the effectiveness of lockdown measures may vary with alternative modeling approaches. Therefore, the findings should be interpreted in the context of this LSTM-framework.

Employee Experience While Working Remotely During the COVID-19 Pandemic in Thailand: A Qualitative Enquiry

The COVID-19 pandemic has brought massive changes to all aspects of human society and the economy. Recent outbreaks in Thailand lasted for a longer duration and have been more severe than the previous commencing epidemic, as evidenced by the increased counts of cases, hospitalizations, and, unfortunately, fatalities. Thereby, social distancing - the most powerful strategy to prevent the coronavirus spread - was more strictly imposed, as officials implemented vaccination and other mitigation strategies. Consequently, remote working has become obligatory for every firm, alternating working in person in most situations. This paper aims to reflect unique perceptions of employee experience and initially identify some contributing factors regarding the remote working situation in Thailand during the peak of the COVID-19 pandemic, thereby illuminating implications for both employees and employers in the possible long-term future of remote working. Semi-structured interviews were conducted with 12 participants who served different roles in various industries, yet all experienced remote working during the peak of the COVID-19 outbreak in Thailand. The subsequent analysis yielded four primary themes. First , the application of technology and its quality had a strong impact on employee experience, as this was the inevitable instrument to maintain all activities. Second, the discrepancy in role autonomy discriminated more distinctly the working pressure between managers and subordinates. Third, work-life balance was more difficult to achieve for people who have a family, with more blurs and complications. And finally, organizational changes were critical to employee experience, yet still limited and incomprehensible.

Assessment of COVID-19 Vaccination Services During the 5th Wave of the Outbreak in Thailand

Assessment of COVID-19 Vaccination Services During the 5th Wave of the Outbreak in Thailand

Molecular insights versus morphological traits: rethinking identification of the closely related Angiostrongylus cantonensis and Angiostrongylus malaysiensis

BackgroundThe closely related Angiostrongylus cantonensis and Angiostrongylus malaysiensis have been reported to coexist in Thailand and share similar hosts and life cycles. Recently, in an angiostrongyliasis outbreak in Thailand, both A. cantonensis and A. malaysiensis were found in the cerebrospinal fluid of affected patients. Morphological similarities, overlapping distribution, shared hosts and habitats, and the close genetics of the two Angiostrongylus species can complicate accurate species identification. Addressing these challenges, this study aims to evaluate whether a correlation between the morphological and genetic identities of A. cantonensis and A. malaysiensis can improve species identification accuracy.MethodsAngiostrongylus spp. specimens from five zoogeographical regions in Thailand were subjected to morphological and molecular identification using the mitochondrial cytochrome b gene and the nuclear internal transcribed spacer 2 region (ITS2). The morphological characters for males and females were then validated using the species identity obtained from the nuclear ITS2 region.ResultsThe results revealed that morphological misidentifications between these two closely related species are common due to overlapping morphological characters. Although certain male traits such as body length and width aided species differentiation, female traits were found to be less reliable. Furthermore, hybrid forms (8.2%) were revealed through the ITS2 results, which can further complicate morphological identification. Mito-nuclear discordance was also present in 1.9% of the Angiostrongylus specimens from Thailand, suggesting a complex historical interbreeding between the species.ConclusionsBased on our findings, we suggest that nuclear ITS2 is a reliable marker for species identification of A. cantonensis and A. malaysiensis, especially in regions where both species coexist. Additionally, the scope and consequences of hybridization between the two closely related Angiostrongylus species should be further investigated in Thailand.Graphical

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

BackgroundMalaria continues to pose a significant health threat. Rapid identification of malaria infections and the deployment of active surveillance tools are crucial for achieving malaria elimination in regions where malaria is endemic, such as certain areas of Thailand. In this study, an anomaly detection system is introduced as an early warning mechanism for potential malaria outbreaks in countries like Thailand.MethodsUnsupervised clustering-based, and time series-based anomaly detection algorithms are developed and compared to identify abnormal malaria activity in Thailand. Additionally, a user interface tailored for anomaly detection is designed, enabling the Thai malaria surveillance team to utilize these algorithms and visualize regions exhibiting unusual malaria patterns.ResultsNine distinct anomaly detection algorithms we developed. Their efficacy in pinpointing verified outbreaks was assessed using malaria case data from Thailand spanning 2012 to 2022. The historical average threshold-based anomaly detection method triggered three times fewer alerts, while correctly identifying the same number of verified outbreaks when compared to the current method used in Thailand. A limitation of this analysis is the small number of verified outbreaks; further consultation with the Division of Vector Borne Disease could help identify more verified outbreaks. The developed dashboard, designed specifically for anomaly detection, allows disease surveillance professionals to easily identify and visualize unusual malaria activity at a provincial level across Thailand.ConclusionAn enhanced early warning system is proposed to bolster malaria elimination efforts for countries with a similar malaria profile to Thailand. The developed anomaly detection algorithms, after thorough comparison, have been optimized for integration with the current malaria surveillance infrastructure. An anomaly detection dashboard for Thailand is built and supports early detection of abnormal malaria activity. In summary, the proposed early warning system enhances the identification process for provinces at risk of outbreaks and offers easy integration with Thailand’s established malaria surveillance framework.

The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis.

In 2021, Thailand reported the highest incidence of lumpy skin disease (LSD) outbreaks in Asia. In response to the widespread outbreaks in cattle herds, the government's livestock authorities initiated comprehensive intervention measures, encompassing control strategies and a national vaccination program. Yet, the efficacy of these interventions remained unevaluated. This research sought to assess the nationwide intervention's impact on the incidence of new LSD cases through causal impact analysis. Data on weekly new LSD cases in Thailand from March to September 2021 was analyzed. The Bayesian structural time series (BSTS) analysis was employed to evaluate the causal relationship between new LSD cases in the pre-intervention phase (prior to the vaccination campaign) and the post-intervention phase (following the vaccination campaign). The assessment involved two distinct scenarios, each determined by the estimated effective intervention dates. In both scenarios, a consistent decline in new LSD cases was observed after the mass vaccination initiative, while other control measures such as the restriction of animal movement, insect control, and the enhancement of the active surveillance approach remained operational throughout the pre-intervention and the post-intervention phases. According to the relative effect results obtained from scenario A and B, it was observed that the incidence of LSD cases exhibited reductions of 119% (95% Credible interval [CrI]: -121%, -38%) and 78% (95% CrI: -126, -41%), respectively. The BSTS results underscored the significant influence of these interventions, with a Bayesian one-sided tail-area probability of p < 0.05. This model-based study provides insight into the application of BSTS in evaluating the impact of nationwide LSD vaccination based on the national-level data. The present study is groundbreaking in two respects: it is the first study to quantify the causal effects of a mass vaccination intervention on the LSD outbreak in Thailand, and it stands as the only endeavor of its kind in the Asian context. The insights collected from this study hold potential value for policymakers in Thailand and other countries at risk of LSD outbreaks.

Social support, health literacy and anxiety among pregnant women during coronavirus 2019 pandemic in Thailand.

The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to have a significant impact. Pregnant women are particularly vulnerable to its effects, which may increase their anxiety levels. This study aims to investigate anxiety levels in pregnant women during the COVID-19 pandemic in Thailand and to identify factors predicting such anxiety. The researchers collected data through an online questionnaire from November 2021 to May 2022. The sample included 404 pregnant women. The questionnaire consisted of personal information, health literacy related to COVID-19, social support, and anxiety related to COVID-19. The content validity of the questionnaire were verified by three experts, with content validity indices of 0.87, 0.80, and 0.87 for each domain, respectively. The reliability of the questionnaire were 0.96 for health literacy, 0.95 for social support, and 0.96 for anxiety. Moreover, in-depth telephone interviews were also conducted with pregnant women. The data were analyzed using descriptive statistics, stepwise multiple regression, and content analysis. Group of 404 pregnant women were studied, and the results showed that pregnant women had a high level of health literacy regarding COVID-19 and pregnancy (mean = 96.36, SD = 14.23) and social support level on a high level (mean = 83.99, SD = 11.34). Most of them were concerned about anxiety related to COVID-19 infection and pregnancy on a moderate level (mean = 47.78, SD = 11.49). The factors predicting the anxiety of pregnant women during the COVID-19 outbreak in Thailand included health literacy related to COVID-19 (β = 0.468) and social support (β = 0.283), with a prediction rate of 32.80% (R2 = 0.328) with statistical significance (p < 0.05). This study revealed the anxiety level of pregnant women during the COVID-19 outbreak in Thailand, which was moderate. Health literacy about COVID-19 and social support can predict the anxiety level of pregnant women.

Characteristics of hair loss in COVID-19 patients in Thailand.

There is still a scarcity of data on hair loss caused by coronavirus disease 2019 (COVID-19) infection. This study aims to determine the characteristics of hair loss in Thai individuals after COVID-19 infection and to identify associated factors. From March to June 2022, a retrospective review of medical records and telephone interviews was conducted to determine the details of hair loss, the severity of infection, and the associated treatments of patients with an abrupt onset of hair loss after the diagnosis of COVID-19 infection at Siriraj Hospital in Bangkok, Thailand. This study included 43 patients who experienced hair loss within 4 months after COVID-19 infection. The mean age was 46.5 ± 14.5 years, predominantly women. Most had mild COVID-19 symptoms (59.3%), and 59.1% experienced weight loss, with a mean weight loss of 4.3 ± 2.0 kg per month. Preexisting hair loss was reported in 31.0% of participants, with approximately 3-quarters diagnosed with androgenetic alopecia. The median onset of hair loss after COVID-19 infection was 30 days (interquartile range 30-60). Telogen effluvium was the most common acute hair loss diagnosis, and topical minoxidil was the predominant treatment (95.3%). Female gender was correlated with a more severe shedding scale (adjusted odd ratio 24.76, 95% CI 1.67-168.86). Patients with a history of androgenetic alopecia tended to have a lower hair shedding scale (adjusted odd ratio 0.03, 95% CI 0.01-0.38). This study reviewed the characteristics of hair loss after COVID-19 infection during Omicron outbreaks in Thailand. The COVID-19-associated telogen effluvium, which is the primary cause in our patients, manifested with earlier onset at approximately 30 days.

Statistical modeling for identifying chikungunya high-risk areas of two large-scale outbreaks in Thailand's southernmost provinces

Chikungunya fever (CHIKF) has re-emerged in the southernmost Thailand and presents a significant threat to public health. The problem areas can be identified using appropriate statistical models. This study aimed to determine the geographic epidemic patterns and high-risk locations. Data on CHIKF’s case characteristics, including age, gender, and residence sub-district, were obtained from the Office of Disease Prevention and Control of Thailand from 2008 to 2020. A logistic model was applied to detect illness occurrences. After removing records with no cases, a log-linear regression model was used to determine the incidence rate. The results revealed that two large-scale infections occurred in the southernmost provinces of Thailand between 2008 and 2010, and again between 2018 and 2020, indicating a 10-year epidemic cycle. The CHIKF occurrence in the first and second outbreaks was 28.4% and 15.5%, respectively. In both outbreaks of occurrence CHIKF, adolescents and working-age groups were the most infected groups but the high incidence rate of CHIKF was elderly groups. The first outbreak had a high occurrence and incidence rate in 39 sub-districts, the majority of which were in Narathiwat province, whilst the second outbreak was identified in 15 sub-districts, the majority of which were in Pattani province. In conclusion, the CHIKF outbreak areas can be identified and addressed by combining logistic and log-linear models in a two-step process. The findings of this study can serve as a guide for developing a surveillance strategy or an earlier plan to manage or prevent the CHIKF outbreak.

Estimating the Transmission Kernel for Lumpy Skin Disease Virus from Data on Outbreaks in Thailand in 2021.

Nationwide outbreaks of lumpy skin disease (LSD) were observed in Thailand in 2021. A better understanding of its disease transmission is crucial. This study utilized a kernel-based approach to characterize the transmission of LSD between cattle herds. Outbreak data from the Khon Kaen and Lamphun provinces in Thailand were used to estimate transmission kernels for each province. The results showed that the majority of herd-to-herd transmission occurs over short distances. For Khon Kaen, the median transmission distance from the donor herd was estimated to be between 0.3 and 0.8 km, while for Lamphun, it ranged from 0.2 to 0.6 km. The results imply the critical role that insects may play as vectors in the transmission of LSD within the two study areas. This is the first study to estimate transmission kernels from data on LSD outbreaks in Thailand. The findings from this study offer valuable insights into the spatial transmission of this disease, which will be useful in developing prevention and control strategies.

Longitudinal humoral immune response and maternal immunity in horses after a single live-attenuated vaccination against African horse sickness during the disease outbreak in Thailand.

African horse sickness (AHS) has become a newly emerging disease after an outbreak in northeastern Thailand in March 2020. Mass vaccination in horses with live-attenuated AHS virus (AHSV) vaccine is essential for AHS control and prevention. This study aimed to monitor the longitudinal humoral immune response before and after a single vaccination using a live-attenuated vaccine against AHS in stallions, mares, and pregnant mares, including maternal immunity in foals born from pregnant mares during the outbreak in Thailand. A total of 13 stallions and 23 non-pregnant and 21 pregnant mares were vaccinated with live-attenuated AHSV vaccines. Serum samples from selected horses were collected on the day of vaccination and 1, 6, 8, 9, 10, and 12-months post-vaccination. Furthermore, seven serum samples of foals born from vaccinated pregnant mares were collected on parturition date and 1, 3, and 6-months old. The antibody titer against AHS in all collected serum samples was evaluated using a commercial enzyme-linked immunosorbent assay kit. All data were analyzed for mean and standard deviation for each group of samples using a spreadsheet program. Antibody titers between times were analyzed using a one-way analysis of variance as repeated measurement, and antibody titers between horse groups were analyzed using a general linear model for statistically significant differences when p < 0.05. In stallion and non-pregnant mare groups, there were no statistically significant differences in antibody titers in all 6 time periods after vaccination. The antibody titer in the pregnant mare group showed a non-statistically significant difference between each gestation stage, except at 8 months post-vaccination. Furthermore, increasing antibody titers on days 1 and 3 after receiving colostrum in foals indicate the major role of transcolostral antibody transfer for AHS. This study demonstrated that a single AHS vaccination using a live-attenuated vaccine could stimulate high antibody titers sufficient for AHS control and prevention during the outbreak in Thailand. Similarly, the antibody response of vaccinated horses of both genders, including various stages of pregnant mares, was statistically not different.

Acceptability of telemedicine for follow up after contraceptive implant initiation at an obstetrics and gynecologic training center

BackgroundDuring the COVID-19 pandemic, telemedicine has become a popular adjunct to in-person visits, including for family planning services. This study determined the proportion of clients participated in telemedicine services and the association between sociodemographic factors and telemedicine participation during the COVID-19 pandemic. The adverse effects within the first seven days post-insertion were also reported.MethodsThis retrospective cohort study considered data from all women initiating contraceptive implantation between June 2020 and August 2021 at King Chulalongkorn Memorial Hospital. Clients were offered the following two options for follow-up visits: in-person or communication via an online LINE® Official Account (LINE® OA), a free chat application widely used among the Thais. Logistic regression analyses were used to assess the association between socio-demographic factors and telehealth usage.ResultsIn total, 574 of 947 (60.6%) clients participated in telemedicine follow-up services during the period considered. A significant association between telemedicine usage and the following were observed: the peak of second wave COVID-19 outbreak in Thailand, using the period preceding the second wave as a reference [adjusted odds ratio (aOR) = 1.47 (95% confidence interval [CI]: 1.12–1.96)]; participants receiving governmental benefits for contraceptive implant payment (aOR: 3.23, 95% CI: 1.86–5.60), and timing of contraceptive implant(s) initiation, using interval insertion as a reference for which aORs of postpartum and immediate postpartum insertions were 0.62 (95% CI: 0.43–0.90) and 0.35 (95% CI: 0.24–0.52), respectively. Significant ecchymosis at the insertion site was observed in 13.1% of participants.ConclusionThis study emphasizes the significance of telemedicine during the COVID-19 epidemic, particularly in facilitating contraceptive implant initiation. Our data show a significant increase in the uptake and utilization of telemedicine during the pandemic’s peak. The data also shows that during the period of Thailand’s second COVID-19 epidemic, government benefits for contraceptive implant payment, and the timing of contraceptive implant initiation, are significantly associated with telemedicine use. This finding supports the continued use of telemedicine in healthcare, particularly for services like family planning, where remote follow-ups can provide safe, efficient, and timely care.

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period.

Lifestyles, Food Consumption Frequencies, and Eating Behaviors among Three Main Disciplines of Undergraduate Students during the Early COVID-19 Outbreak in Thailand.

University students' lifestyles and dietary habits have been considerably impacted by the coronavirus disease 2019 (COVID-19) outbreak and its related policies and restrictions. An online cross-sectional survey was conducted from March to May 2020 to compare lifestyles, food consumption frequencies, and eating behaviors among three main disciplines of undergraduate students during the early COVID-19 outbreak in Thailand. The study involved 584 participants from Mahidol University, with 45.2% from Health Sciences (HS), 29.1% from Sciences and Technologies (ST), and 25.7% from Social Sciences and Humanities (SH). The results showed that ST students had the highest proportion of overweight and obese (33.5%) individuals, followed by HS (23.9%) and SH (19.3%) students. ST students skipped breakfast the most (34.7%), followed by SH (34%) and HS (30%) students. Furthermore, 60% of SH students spent 7 h or more daily on social media, and they had the least exercise and the highest frequency of ordering home-delivery food. SH students (43.3%) reported a higher likelihood of making unhealthier food choices and consuming fast food, processed meat, bubble tea, boxed fruit and vegetable juice, and crunchy snacks more frequently than students from other disciplines. The findings show that undergraduate students had poor eating behaviors and lifestyles during the early COVID-19 outbreak, highlighting the urgent need to promote food and nutrition security among students during and after the pandemic.