Occurrence of Cryptosporidium oocysts in commercial oysters in southern Thailand.

The enteric parasite Cryptosporidium is spread through the fecal-oral pathway, most commonly by the consumption of contaminated water but also through food. Because eating raw or barely cooked shellfish might put consumers at risk for cryptosporidiosis, identifying the parasite in oysters is important for public health. A total of 240 oysters, collected from two shellfish aquaculture sites in Thailand's Gulf coast, Nakhon Si Thammarat and Surat Thani, were tested for the presence of Cryptosporidium. Escherichia coli, enterococci, and thermotolerant coliform total levels were measured to assess seawater quality in the shellfish production regions. Oocysts of Cryptosporidium spp. were detected in 13.8% of the samples processed by immunofluorescence analyses. The detection of Cryptosporidium spp. oocysts in oysters obtained from Surat Thani (17.5%) was higher than in those obtained from Nakhon Si Thammarat (9.2%). The difference in detection of positive samples obtained from Nakhon Si Thammarat and those obtained from Surat Thani may be attributed to the effects of physical, ecological, and anthropogenic conditions, resulting in an increased level of marine water contamination by Cryptosporidium spp. oocysts. These findings demonstrate that native commercial oysters obtained from Thailand's southern Gulf coast contained Cryptosporidium spp. oocysts which might serve as a source of human infection. Consequently, these findings pose a serious public health concern and suggest that more quality control measures need to be implemented by the oyster aquaculture business to ensure the safety of seafood.

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010-2020.

Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state−space model with Box−Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and hybrid methods. These methods were applied to monthly n-FMD episodes (n = 1209) from January 2010 to December 2020. Results showed that the n-FMD episodes had a stable trend from 2010 to 2020, but they appeared to increase from 2014 to 2020. The outbreak episodes followed a seasonal pattern, with a predominant peak occurring from September to November annually. The single-technique methods yielded the best-fitting time-series models, including SARIMA(1,0,1)(0,1,1)12, NNAR(3,1,2)12,ETS(A,N,A), and TBATS(1,{0,0},0.8,{<12,5>}. Moreover, SARIMA-NNAR and NNAR-TBATS were the hybrid models that performed the best on the validation datasets. The models that incorporate seasonality and a non-linear trend performed better than others. The forecasts highlighted the rising trend of n-FMD episodes in Thailand, which shares borders with several FMD endemic countries in which cross-border trading of cattle is found common. Thus, control strategies and effective measures to prevent FMD outbreaks should be strengthened not only in Thailand but also in neighboring countries.