Spatiotemporal analyses of foot and mouth disease outbreaks in cattle farms in Chiang Mai and Lamphun, Thailand

Spatial-temporal distribution and risk factors of foot and mouth disease outbreaks in Java Island, Indonesia from 2022 to 2023

BACKGROUND

Indonesia faced new outbreaks of foot and mouth disease in 2022 after being officially free from the disease for several decades. The outbreaks were first reported in East Java in April 2022 and subsequently spread to many regions in Indonesia. This study investigated the epidemiology and risk factors of foot and mouth disease outbreaks in Java, Indonesia, from 2022 to 2023. Descriptive, spatial, spatiotemporal, and risk factor analyses were conducted to investigate the patterns and risk factors associated with the outbreaks in Java.

RESULTS

Results showed that the outbreaks were distributed across the island. East Java was the most affected region. The outbreaks peaked in June 2022, followed by a downward trend until 2023. Positive spatial autocorrelations were found in both years, indicating that the outbreaks clustered in several areas. The spatiotemporal analysis found a total of 16 clusters in both years, with 11 clusters in 2022 and 5 clusters in 2023. The temporal distribution of clusters indicated a peak period from May to July, with 12 out of 16 clusters occurring during this time. Risk factor analysis found that environmental and agricultural-related factors, including annual precipitation, the presence of livestock markets, the presence of slaughterhouses, the presence of animal health centres, cattle population, and goat population, are significant risk factors for the occurrence of outbreaks in Java. Probability risk mapping found higher risk areas primarily distributed in the eastern and central parts of Java.

CONCLUSIONS

The outbreaks predominantly clustered in eastern and central parts of Java. The outbreaks peaked in June 2022, followed by a downward trend until the end of 2023. Environmental and agricultural-related factors significantly increased the risk of outbreak occurrence.

Evaluation and comparison of performances of six commercial NSP ELISA assays for foot and mouth disease virus in Thailand

ELISA kits that detect antibodies to the non-structural protein (NSP) of the foot-and-mouth disease virus (FMDV), commonly referred to as NSP-ELISA, can distinguish between vaccinated and naturally infected animals. They can play an essential role in demonstrating 'proof-of-freedom' during the control of FMD. Although various NSP-ELISA kits are available in Thailand, information regarding their performance is lacking. To select the most appropriate NSP-ELISA kit for our specific purpose, we must compare their performance using carefully characterized sera. This will ensure that we maximize the benefits of our testing. In this study, six NSP-ELISA kits sold in Thailand-Biovet, ID Screen, VDPro, IDEXX, PrioCHECK, and KUcheck-F-were evaluated and compared. A total of 800 serum samples were examined, including samples from 357 cattle and 29 buffaloes in outbreak areas, as well as 14 swine serum samples from the Vaccine Quality Control Unit of the Bureau of Veterinary Biologics, Ministry of Agriculture and Cooperation, Thailand. Four hundred samples were confirmed to originate from animals infected with FMDV through ELISA typing (n = 11, tested as representative samples in each farm) and/or RT-PCR (n = 400, all samples), serving as positive control sera. Additionally, 400 negative control sera were obtained from Japan (97 cattle and 300 pigs) and Australia (3 goats), certified by the World Organisation for Animal Health as 'free of FMD'. The sensitivity and specificity of the six tests were determined based on the results obtained from two-by-two tables. Cohen's kappa statistics were calculated for the six tests to assess their concordance, and the diagnostic accuracy of the assays was also determined. For all six NSP-ELISA kits, the sensitivity ranged from 97.75 to 99.50%, and the specificity ranged from 97.25 to 100%. Cohen's kappa statistics ranged from 0.96 to 1.00, and diagnostic accuracy ranged from 98.13 to 99.75%. The study results indicated that the test kits have statistically similar sensitivity, specificity, concordance, and diagnostic accuracy, suggesting they can be used interchangeably. However, ID Screen demonstrated the highest sensitivity and specificity among all kits tested. Therefore, if a single kit were to be selected from the six evaluated, ID Screen would be the most appropriate choice. These findings can aid in selecting the most suitable test kit. Therefore, it is recommended to consider purchasing a diverse range of effective test kits. Furthermore, these findings can provide guidance for expanding the use of test kits, particularly with the growing availability of NSP-ELISA kits in the market.

Spatiotemporal analysis of foot and mouth disease outbreaks in cattle and small ruminants in Türkiye between 2010 and 2019

Determining the dynamics associated with foot-and-mouth disease (FMD) outbreaks is important for being able to develop effective strategic plans against the disease. In this direction, spatiotemporal analysis of FMD virus (FMDV) epidemic data that occurred in Türkiye between 2010 and 2019 was carried out. Spatiotemporal analysis was performed by the space-time scan statistic using data from a total of 7,796 FMD outbreaks. Standard deviational ellipse analysis (SDE) was performed to analyse the directional trend of FMD. Five, six, and three significant and high-risk clusters were identified by the space-time cluster analysis for serotypes A, O, and Asia-1, respectively. The SDE analysis indicated that direction of FMD transmission was northeast to southwest. A significant decrease in the number of outbreaks and cases were observed between 2014 and 2019 compared to 2010-2013 (p = 0.010). Most of the serotype A, serotype O, and serotype Asia-1 associated FMD outbreaks were observed during the dry season (April to September). Among FMD cases, cattle and small ruminants accounted for 80.75% (180,932 cases) and 19.25% (43,116 cases), respectively. Among the serotypes detected in the cases, the most frequently detected serotype was serotype O (50.84%), followed by serotypes A (35.67%) and Asia-1 (13.49%). The results obtained in this study may contribute to when and where control programs could be implemented more efficiently for the prevention and control of FMD. Developing risk-defined regional control plans by taking into account the current livestock production including uncontrolled animal movements in border regions, rural livestock, livestock trade between provinces are recommended.

Identifying the patterns and sizes of the first lumpy skin disease outbreak clusters in Northern Thailand with a high degree of dairy farm aggregation using spatio-temporal models

Lumpy skin disease (LSD) is one of the most important notifiable transboundary diseases affecting cattle in many parts of the world. In Thailand, LSD outbreaks in cattle farming areas have been reported in 69 out of 77 provinces, indicating a serious nationwide situation. Understanding the dynamics of spatial and temporal LSD epidemic patterns can provide important information on disease transmission and control. This study aims to identify spatial and temporal clusters in the first LSD outbreaks in dairy farming areas with a high degree of aggregation in Northern Thailand using spatio-temporal models. The data were obtained from an official LSD outbreak investigation conducted between June and August 2021 on dairy farms (n = 202). The outbreak of LSD was confirmed by employing clinical observations and laboratory analysis. The spatio-temporal models including space-time permutation (STP), Poisson, and Bernoulli were applied to the outbreak data with the settings of 10%, 25%, and 50%, respectively, for the maximum reported cluster size (MRCS). Overall, the number of most likely and secondary clusters varied depending on the model and MRCS settings. All MRCS settings in the STP model detected the most likely clusters in the same area and the Poisson models in different areas, with the largest being defined by a 50% MRCS. Although the sizes of the most likely clusters identified by the Bernoulli models were different, they all had the same cluster period. Based on the sizes of the detected clusters, strict LSD insect-vector control should be undertaken within one kilometer of the outbreak farm in areas where no LSD vaccination has been administered. This study determines the sizes and patterns of LSD outbreak clusters in the dairy farming area with a high degree of farm aggregation. The spatio-temporal study models used in this study, along with multiple adjusted MRCS, provide critical epidemiological information. These models also expand the options for assisting livestock authorities in facilitating effective LSD prevention and control programs. By prioritizing areas for resource allocation, these models can help improve the efficiency of such programs.

Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods

Lumpy skin disease (LSD) is an important transboundary disease affecting cattle in numerous countries in various continents. In Thailand, LSD is regarded as a serious threat to the cattle industry. Disease forecasting can assist authorities in formulating prevention and control policies. Therefore, the objective of this study was to compare the performance of time series models in forecasting a potential LSD epidemic in Thailand using nationwide data. For the forecasting of daily new cases, fuzzy time series (FTS), neural network auto-regressive (NNAR), and auto-regressive integrated moving average (ARIMA) models were applied to various datasets representing the different stages of the epidemic. Non-overlapping sliding and expanding window approaches were also employed to train the forecasting models. The results showed that the FTS outperformed other models in five of the seven validation datasets based on various error metrics. The predictive performance of the NNAR and ARIMA models was comparable, with NNAR outperforming ARIMA in some datasets and vice versa. Furthermore, the performance of models built from sliding and expanding window techniques was different. This is the first study to compare the forecasting abilities of the FTS, NNAR, and ARIMA models across multiple phases of the LSD epidemic. Livestock authorities and decision-makers may incorporate the forecasting techniques demonstrated herein into the LSD surveillance system to enhance its functionality and utility.

Spatio-temporal patterns of lumpy skin disease outbreaks in dairy farms in northeastern Thailand

In 2021–2022, there were numerous outbreaks of lumpy skin disease (LSD) affecting cattle farms across Thailand. This circumstance was the country's first encounter with an LSD outbreak. Thus, a better understanding of LSD epidemiology is necessary. The aim of this study was to determine the spatio-temporal patterns of the LSD outbreaks in dairy farming areas. Data from LSD outbreak investigations collected from dairy farms in Khon Kean province, northeastern Thailand, were analyzed using spatio-temporal models including space-time permutation, Poisson, and Bernoulli models. LSD outbreaks were found in 133 out of 152 dairy farms from May to July, 2021. The majority of dairy farms (n = 102) were affected by the LSD outbreaks in June. The overall herd attack, morbidity and mortality rates were 87, 31, and 0.9%, respectively. According to the results of all models, the most likely clusters were found in the northern part of the study area. The space-time permutation and Poisson model identified 15 and 6 spatio-temporal outbreak clusters, respectively, while the Bernoulli model detected only one cluster. The most likely clusters from those models cover radii of 1.59, 4.51, and 4.44 km, respectively. All farms included in the cluster identified by the space-time permutation model were also included in the cluster identified by the Poisson model, implying that both models detected the same outbreak area. Furthermore, the study results suggested that farmers who own farms within a one km radius of the LSD outbreak farm should be advised to implement more stringent insect vector control measures to prevent disease spread. This study provides better insights into the spatio-temporal pattern of clusters of LSD in the outbreak area. The findings of this study can support authorities in formulating strategies to prevent and control future outbreaks as well as prioritizing resource allocation to high-risk areas.

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.

The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis

The first outbreak of lumpy skin disease (LSD) in Thailand was reported in March 2021, but information on the epidemiological characteristics of the outbreak is very limited. The objectives of this study were to describe the epidemiological features of LSD outbreaks and to identify the outbreak spatio-temporal clusters. The LSD-affected farms located in Roi Et province were investigated by veterinary authorities under the outbreak response program. A designed questionnaire was used to obtain the data. Space-time permutation (STP) and Poisson space-time (Poisson ST) models were used to detect areas of high LSD incidence. The authorities identified 293 LSD outbreak farms located in four different districts during the period of March and the first week of April 2021. The overall morbidity and mortality of the affected cattle were 40.5 and 1.2%, respectively. The STP defined seven statistically significant clusters whereas only one cluster was identified by the Poisson ST model. Most of the clusters (n = 6) from the STP had a radius <7 km, and the number of LSD cases in those clusters varied in range of 3-51. On the other hand, the most likely cluster from the Poisson ST included LSD cases (n = 361) from 198 cattle farms with a radius of 17.07 km. This is the first report to provide an epidemiological overview and determine spatio-temporal clusters of the first LSD outbreak in cattle farms in Thailand. The findings from this study may serve as a baseline information for future epidemiological studies and support authorities to establish effective control programs for LSD in Thailand.

Subdistrict-Level Reproductive Number for Foot and Mouth Disease in Cattle in Northern Thailand

Foot and mouth disease (FMD) is an important contagious transboundary disease that causes a significant economic loss for several countries. The FMD virus (FMDV) can spread very rapidly by direct and indirect transmission among susceptible animals. The complexity and magnitude of FMDV transmission at the initial stages of the epidemic can be expressed by the basic reproductive number (R ₀), and furthermore, control strategies can be assessed by the estimation of the effective reproductive number. In this study, we aimed to describe FMD outbreaks among smallholder cattle farms by subdistricts in the northern Thailand and compute the effective reproductive number for outbreaks caused by FMDV serotype O and overall serotypes, including serotype O, serotype A, and unidentified serotype, at the subdistrict level (R _sd ) using an epidemic doubling time method. Field data of FMD outbreaks during 2015-2017 that affected 94 subdistricts in northern Thailand were assessed to estimate the R _sd . Results showed that 63.38% (90/142) of the FMD outbreak episodes in cattle were caused by FMDV serotype O. The average doubling time and the R _sd estimated of the outbreaks caused by FMDV serotype O and overall serotype were 2.80 and 4.67 months, and 1.06 and 1.04, respectively. Our results indicated that transmission of FMD in cattle at the subdistrict level in northern Thailand was not controlled (R _sd > 1), which indicates the endemicity of the disease in the region. Although control measures are in place, the results from this study highlighted the need for enhancing FMD monitoring and control strategies in northern Thailand.

Epidemiology and National Surveillance System for Foot and Mouth Disease in Cattle in Thailand during 2008-2019

Foot and mouth disease (FMD) is a prominent transboundary disease that threatens livestock production and can disrupt the trade in animals and animal products at both regional and international levels. The aims of this study were: (1) to analyze the distribution of FMD in Thailand during the period of 2008 to 2019, (2) to outline a national surveillance approach, and (3) to identify the existing knowledge gap that is associated with this disease in relation to cattle production. We analyzed FMD outbreak data in order to determine the existing spatial and temporal trends and reviewed relevant publications and official documents that helped us outline a national surveillance program. There were 1209 FMD outbreaks in cattle farms during the study period. FMD outbreaks occurred every year throughout the study period in several regions. Notably, FMD serotype O and A were considered the predominant types. The FMD National Strategic Plan (2008–2015) and the national FMD control program (2016–2023) have been implemented in order to control this disease. The surveillance approach employed by livestock authorities included both active and passive surveillance techniques. The vaccination program was applied to herds of cattle 2–3 times per year. Additionally, numerous control measures have been implemented across the country. We have identified the need for a study on the assessment of an applicable surveillance program, the evaluation of an appropriate vaccination strategy and an assessment of the effectiveness of a measured control policy. In conclusion, this study provided much needed knowledge on the epidemiology of FMD outbreaks across Thailand from 2008 to 2019. Additionally, we identified the need for future studies to address the existing knowledge gaps. The findings from this study may also be useful for livestock authorities and stakeholders to establish an enhanced control strategy and to implement an effective surveillance system that would control and eradicate FMD throughout the country.