Genotyping of non-polio enteroviruses associated with acute flaccid paralysis in Thailand in 2013 and 2014.

BACKGROUND: Acute flaccid paralysis (AFP) surveillance was conducted as part of the World Health Organization's strategy for completely eradicating poliomyelitis and leaving non-polio enteroviruses NPEVs as one of the main potential causes of AFP. We aimed to detect NPEV in association with AFP. METHODS: We used 459 isolates reported to be Negative Polio and some NPEVs by the World Health Organization Polio Regional Reference Laboratory (Thailand), which had been obtained during polio surveillance programmes conducted in Thailand in 2013-2014. Of 459 isolates, 35 belonged to the genus Enterovirus by RT-PCR and genotyping by DNA sequencing. RESULTS: This study found 17 NPEV genotypes, with 3, 13 and 1 belonging to enterovirus (EV) species A (EV-A), EV-B, and EV-C, respectively. The EV-A types identified included coxsackievirus A2 (CA2), CA4, and EV71, typically associated with hand, foot and mouth diseases. EV-B is the most prevalent cause of AFP in Thailand, while CA21 was the only type of EV-C detected. The EV-B species (13/35; 76.5%) constituted the largest proportion of isolates, followed by EV-A (3/35; 17.6%) and EV-C (1/35; 5.9%). For the EV-B species, Echovirus (E) 30 and CVB were the most frequent isolates. E30, CVB, E14, and E6 were considered endemic strains. CONCLUSION: NPEVs, e.g. CA4, are reported for the first time in Thailand. Despite some limitations to this study, this is the first report on the circulation patterns of NPEVs associated with AFP in Thailand. AFP surveillance has unearthed many unknown NPEVs and, the cases of death due to AFP occur annually. Therefore, it is important to study NPEVs in the wake of the eradication of poliovirus in the context of the continued incidence of paralysis.

Temporal patterns and forecast of dengue infection in Northeastern Thailand.

This study aimed to determine temporal patterns and develop a forecasting model for dengue incidence in northeastern Thailand. Reported cases were obtained from the Thailand national surveillance system. The temporal patterns were displayed by plotting monthly rates, the seasonal-trend decomposition procedure based on loess (STL) was performed using R 2.2.1 software, and the trend was assessed using Poisson regression. The forecasting model for dengue incidence was performed in R 2.2.1 and Intercooled Stata 9.2 using the seasonal Autoregressive Integrated Moving Average (ARIMA) model. The model was evaluated by comparing predicted versus actual rates of dengue for 1996 to 2005 and used to forecast monthly rates during January to December 2006. The results reveal that epidemics occurred every two years, with approximately three years per epidemic, and that the next epidemic will take place in 2006 to 2008. It was found that if a month increased, the rate ratio for dengue infection decreased by a factor 0.9919 for overall region and 0.9776 to 0.9984 for individual provinces. The amplitude of the peak, which was evident in June or July, was 11.32 to 88.08 times greater than the rest of the year. The seasonal ARIMA (2, 1, 0) (0, 1, 1)12 model was model with the best fit for regionwide data of total dengue incidence whereas the models with the best fit varied by province. The forecasted regional monthly rates during January to December 2006 should range from 0.27 to 17.89 per 100,000 population. The peak for 2006 should be much higher than the peak for 2005. The highest peaks in 2006 should be in Loei, Buri Ram, Surin, Nakhon Phanom, and Ubon Ratchathani Provinces.