Spatial model of foot-and-mouth disease outbreak in an endemic area of Thailand

Characterization of T-cell subsets in response to foot-and-mouth disease bivalent inactivated vaccine in Chinese Holstein cows

IMPORTANCE

Vaccination plays a crucial role in the prevention and control of FMD; however, outbreaks persist occurring worldwide. Assessing the immune response to FMD vaccines is essential for effective prevention of FMD. In this study, a seven-color flow cytometry protocol was developed to systematically evaluate the T-cell response of Chinese Holstein cows vaccinated with FMD bivalent inactivated vaccine. Our findings showed that while most T-cell subsets (%) decreased post-vaccination, a significant increase was observed in CD4+CD8+ DP T cells, which was consistent with the levels of specific foot-and-mouth disease virus (FMDV) antibodies. These findings suggested that CD4+CD8+ DP T cells could serve as a potential biomarker for the evaluation of cellular and humoral responses to FMDV vaccination. Additionally, we should be aware of the potential decline in cellular immunity among cattle during FMD vaccination, as this may increase the risk of other pathogen-related issues.

Evaluation of foot and mouth disease control measures: Simulating two endemic areas of Thailand

Foot and mouth disease (FMD) is an important livestock disease in Thailand, with outbreaks occurring every year. However, the effects of FMD control measures in Thailand have received little research attention. Epidemiological models have been widely used to evaluate FMD outbreak control, but such a model has never been developed for Thailand. We constructed a stochastic between-farm transmission model to evaluate FMD control measures. The epidemiological unit of the model was the farm, which could be in different states: susceptible, latent, undetected infectious, detected infectious and recovered. The between-farm transmission was calculated by the sum of distance-dependent transmission and trade network transmission using parameters derived from FMD outbreaks in 2016-2017. We used this model to simulate the outbreaks with and without the implementation of the following control measures: culling all animals on infected farms, ring vaccination, animal movement restrictions and isolation of infected farms. The control measures were evaluated by estimating the number of secondarily infected farms and the outbreak duration for each scenario. The model was simulated in two study areas located in the Lamphaya Klang subdistrict (high farm density) and the Bo Phloi district (low farm density). The effects of control measures differed between the two study areas. When farm density was high, rigid control measures were required to prevent a major outbreak. Among all options, culling the animals on infected farms resulted in the lowest number of infected farms and the shortest outbreak duration. In contrast, for an area with a low farm density, less stringent control measures were sufficient to control the usually minor outbreaks. The results indicate that different areas require a different approach to control an outbreak of FMD.

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted [Formula: see text] of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.

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.