ABSTRACT
This study sought to determine the anti-SARS-CoV-2 antibody status of 4111 Thai people from May 2020 to April 2021, a period which spanned the first two and part of the third epidemic wave of the COVID-19 in Thailand. Participants comprised 142 COVID-19 patients, 2113 individuals at risk due to their occupations [health personnel, airport officers, public transport drivers, and workers in entertainment venues (pubs, bars and massage parlors)], 1856 individuals at risk due to sharing workplaces or living communities with COVID-19 patients, and 553 Thai citizens returning after extended periods in countries with a high disease prevalence. All sera were tested in a microneutralization assay and a chemiluminescence immunoassay (CLIA) for IgG against the N protein. Furthermore, we performed an immunofluorescence assay to resolve discordant results between the two assays. Antibody responses developed in 88% (15 of 17) of COVID-19 patients at 8 days and in 94-100% between 15 and 60 days after disease onset. Neutralizing antibodies persisted for at least 8 months, longer than the IgG did, against the N protein. None of the health providers, airport officers, and public transport drivers were seropositive, while the antibodies were present in 0.44% of entertainment workers. This study showed the seropositivity of 1.9, 1.5, and 7.5% during the 3 epidemic waves, respectively, in Bangkok residents who were at risk due to sharing workplaces or communities with COVID-19 patients. Also, antibody prevalence was 1.3% in Chiang Mai people during the first epidemic wave, and varied between 6.5 and 47.0% in Thais returning from high-risk countries. This serosurveillance study found a low infection rate of SARS-CoV-2 in Thailand before the emergence of the Delta variant in late May 2021. The findings support the Ministry of Public Healths data, which are based on numbers of patients and contact tracing.
ABSTRACT
Background: Developing a quantitative understanding of pandemic influenza dynamics in SouthEast Asia is important for informing future pandemic planning. Hence, transmission dynamics ofinfluenza A/H1N1 were determined across space and time in Thailand.Methods: Dates of symptom onset were obtained for all daily laboratory-confirmed cases of influenzaA/H1N1pdm in Thailand from 3 May 2009 to 26 December 2010 for four different geographicregions (Central, North, North-East, and South). These data were analysed using a probabilisticepidemic reconstruction, and estimates of the effective reproduction number, R(t), were derivedby region and over time.Results: Estimated R(t) values for the first wave peaked at 1.54 (95% CI: 1.42-1.71) in the Centralregion and 1.64 (95% CI: 1.38-1.92) in the North, whilst the corresponding values in the North-Eastand the South were 1.30 (95% CI: 1.17-1.46) and 1.39 (95% CI: 1.32-1.45) respectively. As theR(t) in the Central region fell below one, the value of R(t) in the rest of Thailand increased aboveone. R(t) was above one for 30 days continuously through the first wave in all regions of Thailand.During the second wave R(t) was only marginally above one in all regions except the South.Conclusions: In Thailand, the value of R(t) varied by region in the two pandemic waves. HigherR(t) estimates were found in Central and Northern regions in the first wave. Knowledge of regionalvariation in transmission potential is needed for predicting the course of future pandemics and foranalysing the potential impact of control measures
Subject(s)
Pandemics , Influenza, Human , ThailandABSTRACT
OBJECTIVE@#To study the number of leptospirosis cases in relations to the seasonal pattern, and its association with climate factors.@*METHODS@#Time series analysis was used to study the time variations in the number of leptospirosis cases. The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases.@*RESULTS@#We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest, namely the northern and northeastern region of Thailand, while the temperature played a role in the northeastern region only. The use of multivariate ARIMA (ARIMAX) model showed that factoring in rainfall (with an 8 months lag) yields the best model for the northern region while the model, which factors in rainfall (with a 10 months lag) and temperature (with an 8 months lag) was the best for the northeastern region.@*CONCLUSION@#The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions. The models can also be used to predict the next seasonal peak quite accurately.
