Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005-2022): Multiple Change Point Analysis and Time Series Forecast

Infection, dissemination, and transmission of lumpy skin disease virus in Aedes aegypti (Linnaeus), Culex tritaeniorhynchus (Giles), and Culex quinquefasciatus (Say) mosquitoes

Lumpy skin disease virus (LSDV) is a transboundary viral disease in cattle and water buffaloes. Although this Poxvirus is supposedly transmitted by mechanical vectors, only a few studies have investigated the role of local vectors in the transmission of LSDV. This study examined the infection, dissemination, and transmission rates of LSDV in Aedes aegypti, Culex tritaeniorhynchus, and Culex quinquefasciatus following artificial membrane feeding of 102.7, 103.7, 104.7 TCID50/mL LSDV in sheep blood. The results demonstrated that these mosquito species were susceptible to LSDV, with Cx tritaeniorhynchus exhibiting significantly different characteristics from Ae. aegypti and Cx. quinquefasciatus. These three mosquito species were susceptible to LSDV. Ae. aegypti showed it as early as 2 days post-infection (dpi), indicating swift dissemination in this particular species. The extrinsic incubation period (EIP) of LSDV in Cx. tritaeniorhynchus and Cx. quinquefasciatus was 8 and 14 dpi, respectively. Ingestion of different viral titers in blood did not affect the infection, dissemination, or transmission rates of Cx. tritaeniorhynchus and Cx. quinquefasciatus. All rates remained consistently high at 8-14 dpi for Cx. tritaeniorhynchus. In all three species, LSDV remained detectable until 14 dpi. The present findings indicate that, Ae. aegypti, Cx. tritaeniorhynchus, and Cx. quinquefasciatus may act as vectors during the LSDV outbreak; their involvement may extend beyond being solely mechanical vectors.

Surveillance and screening of Stomoxyinae flies from Mallorca Island (Spain) reveal the absence of selected pathogens but confirm the presence of the endosymbiotic bacterium Wolbachia pipientis

Adult brachycera biting flies can significantly impact livestock through both direct effects (reduction of food intake, disturbance, painful bites, and blood loss) and indirect effects (pathogen transmission), leading to substantial economic losses and production damage. This study aimed to assess the presence of blood-sucking flies in six mixed-animal farm environments on the island of Mallorca (Balearic Islands, Spain) by employing multiple trapping methods. Additionally, distribution maps of brachycera biting fly species recorded in Spain were created, based on data extracted thorough review of scientific literature and citizen digital databases. Investigation of several pathogens, including equine infectious anemia virus (EIAV), Anaplasmataceae bacteria, and piroplasm protozoa, was carried out using different PCR targets (18S rRNA, 16S rRNA, groESL, and tat genes). Citizen science databases and literature review corroborated the consistent distribution trend for two Stomoxyinae species, underscoring the importance of citizen collaboration as a complement to traditional entomological surveillance. Our study confirmed the presence of two biting Stomoxyinae species: the prevalent stable fly Stomoxys calcitrans across all sampled farms, and the horn fly Haematobia irritans, which turned out to be less abundant. DNA barcoding techniques validated the identification of the two species. Neither EIAV nor bacterial/protozoan pathogens were detected using the selected PCR targets in either fly species. However, Wolbachia pipientis (clustered in the supergroup A together with the only sequence of W. pipientis from the USA) was identified through PCR targeting 16S rRNA, groESL and wsp genes in all pools of H. irritans (n = 13) collected from two of the examined farms. This study represents the first attempt to investigate pathogens in Stomoxyinae biting flies in Spain. The discovery of the endosymbiotic Wolbachia organism in H. irritans represents the first record in Spain and the second from Europe. This finding holds significant implications for future research on the applications of this bacterium in biocontrol programs.

Cross-correlation and time series analysis of rabies in different animal species in Nepal from 2005 to 2018

Rabies is a fatal zoonotic disease, resulting in human and livestock deaths. In Nepal, animal rabies has posed a significant challenge to public health. Because animals are the primary source of rabies in humans, a better understanding of rabies epidemiology in animals is necessary. The objectives of this study were to determine the correlation between rabies occurrences in dogs and livestock animals and to detect the trends and change points of the disease using longitudinal data. The nationwide rabies dataset from 2005 to 2018 was analyzed using cross-correlation, multiple change points, and time series methods. Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) were applied to the time series data. The results show a positive correlation between canine rabies and livestock rabies occurrences. Three significant change points were detected in the time series data, demonstrating that the occurrences were high in the initial years but stabilized before peaking to an upward trend in the final years of the study period. Nonetheless, there was no seasonality pattern in rabies occurrences. The most suitable models were ARIMA (2,1,2) and NNAR (5,1,4) (12). Based on the study findings, both locals and tourists in Nepal need to have enhanced awareness of the potential dangers posed by rabies in canines and livestock. This study offers much-needed insight into the patterns and epidemiology of animal rabies which will be helpful for policymakers in drafting rabies control plans for Nepal.

Hepatitis B time series in Xinjiang, China (2006-2021): change point detection based on the Mann-Kendall-Sneyers test

Hepatitis B is a major global challenge, but there is a lack of epidemiological research on hepatitis B incidence from a change point perspective. This study aimed to fill this gap by identifying significant change points and trends in hepatitis time series in Xinjiang, China. The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. The Mann-Kendall-Sneyers (MKS) test was used to detect change points and trend changes on the hepatitis B time series of 14 regions in Xinjiang, and the effectiveness of this method was validated by comparing it with the binary segmentation (BS) and segment regression (SR) methods. Based on the results of change point analysis, the prevention and control policies and measures of hepatitis in Xinjiang were discussed. The results showed that 8 regions (57.1%) with at least one change fell within the 95% confidence interval (CI) in all 14 regions by the MKS test, where five regions (Turpan (TP), Hami (HM), Bayingolin (BG), Kyzylsu Kirgiz (KK), Altai (AT)) were identified at one change point, two change points existed for two regions (Aksu (AK), Hotan (HT)) and three change points was detected in 1 region (Bortala (BT)). Most of the change points occurred at both ends of the sequence. More change points indicated an upward trend in the front half of the sequence, while in the latter half, many change points indicated a downward trend prominently. Finally, in comparing the results of the three change point tests, the MKS test showed a 61.5% agreement (8/13) with the BS and SR.

The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis

Introduction

In 2021, Thailand reported the highest incidence of lumpy skin disease (LSD) outbreaks in Asia. In response to the widespread outbreaks in cattle herds, the government's livestock authorities initiated comprehensive intervention measures, encompassing control strategies and a national vaccination program. Yet, the efficacy of these interventions remained unevaluated. This research sought to assess the nationwide intervention's impact on the incidence of new LSD cases through causal impact analysis.

Methods

Data on weekly new LSD cases in Thailand from March to September 2021 was analyzed. The Bayesian structural time series (BSTS) analysis was employed to evaluate the causal relationship between new LSD cases in the pre-intervention phase (prior to the vaccination campaign) and the post-intervention phase (following the vaccination campaign). The assessment involved two distinct scenarios, each determined by the estimated effective intervention dates. In both scenarios, a consistent decline in new LSD cases was observed after the mass vaccination initiative, while other control measures such as the restriction of animal movement, insect control, and the enhancement of the active surveillance approach remained operational throughout the pre-intervention and the post-intervention phases.

Results and discussion

According to the relative effect results obtained from scenario A and B, it was observed that the incidence of LSD cases exhibited reductions of 119% (95% Credible interval [CrI]: -121%, -38%) and 78% (95% CrI: -126, -41%), respectively. The BSTS results underscored the significant influence of these interventions, with a Bayesian one-sided tail-area probability of p < 0.05. This model-based study provides insight into the application of BSTS in evaluating the impact of nationwide LSD vaccination based on the national-level data. The present study is groundbreaking in two respects: it is the first study to quantify the causal effects of a mass vaccination intervention on the LSD outbreak in Thailand, and it stands as the only endeavor of its kind in the Asian context. The insights collected from this study hold potential value for policymakers in Thailand and other countries at risk of LSD outbreaks.

Lumpy skin disease: Insights into current status and geographical expansion of a transboundary viral disease

Lumpy skin disease (LSD) is an emerging transboundary viral disease of livestock animals which was first reported in 1929 in Zambia. Although LSD is a neglected disease of economic importance, it extends a direct impact on the international trade and economy in livestock-dependent countries. Lumpy skin disease virus (LSDV) has been endemic in African countries, where several outbreaks have been reported previously. However, the virus has spread rapidly across the Middle East in the past two decades, reaching Russia and, recently, the Asian subcontinent. With unprecedented cluster outbreaks being reported across Asian countries like India, China, Nepal, Bangladesh, and Pakistan, LSDV is certainly undergoing an epidemiological shift and expanding its geographical footprint worldwide. Due to high mortality among livestock animals, the recent LSD outbreaks have gained attention from global regulatory authorities and raised serious concerns among epidemiologists and veterinary researchers. Despite networked global surveillance of the disease, recurrent LSD cases pose a threat to the livestock industry. Hence, this review provides recent insights into the LSDV biology by augmenting the latest literature associated with its pathogenesis, transmission, current intervention strategies, and economic implications. The review critically examines the changing epidemiological footprint of LSDV globally, especially in relation to developing countries of the Asian subcontinent. We also speculate the possible reasons contributing to the ongoing LSD outbreaks, including illegal animal trade, climate change, genetic recombination events between wild-type and vaccine strains, reversion of vaccine strains to virulent phenotype, and deficiencies in active monitoring during the COVID-19 pandemic.

Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands

Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time-series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008-2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)12 time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.

Multiple change point analysis of hepatitis B reports in Xinjiang, China from 2006 to 2021

Objective

Hepatitis B (HB) is a major global challenge, but there has been a lack of epidemiological studies on HB incidence in Xinjiang from a change-point perspective. This study aims to bridge this gap by identifying significant change points and trends.

Method

The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. Change points were identified using binary segmentation for full datasets and a segmented regression model for five age groups.

Results

The results showed four change points for the quarterly HB time series, with the period between the first change point (March 2007) and the second change point (March 2010) having the highest mean number of HB reports. In the subsequent segments, there was a clear downward trend in reported cases. The segmented regression model showed different numbers of change points for each age group, with the 30-50, 51-80, and 15-29 age groups having higher growth rates.

Conclusion

Change point analysis has valuable applications in epidemiology. These findings provide important information for future epidemiological studies and early warning systems for HB.

Estimating the Transmission Kernel for Lumpy Skin Disease Virus from Data on Outbreaks in Thailand in 2021

Nationwide outbreaks of lumpy skin disease (LSD) were observed in Thailand in 2021. A better understanding of its disease transmission is crucial. This study utilized a kernel-based approach to characterize the transmission of LSD between cattle herds. Outbreak data from the Khon Kaen and Lamphun provinces in Thailand were used to estimate transmission kernels for each province. The results showed that the majority of herd-to-herd transmission occurs over short distances. For Khon Kaen, the median transmission distance from the donor herd was estimated to be between 0.3 and 0.8 km, while for Lamphun, it ranged from 0.2 to 0.6 km. The results imply the critical role that insects may play as vectors in the transmission of LSD within the two study areas. This is the first study to estimate transmission kernels from data on LSD outbreaks in Thailand. The findings from this study offer valuable insights into the spatial transmission of this disease, which will be useful in developing prevention and control strategies.

Isolation, adaptation, and characterization of lumpy skin disease virus from cattle in Bangladesh

Objective

The research aimed to isolate, adapt to cell culture, and characterize the lumpy skin disease virus (LSDV) from clinically infected cattle in Bangladesh.

Materials and Methods

From September 2019 to June 2020, 37 skin nodules and skin swabs were aseptically collected from afflicted cattle in the outbreak regions of Jhenaidah and Kishoreganj in Bangladesh. The LSDV was isolated from embryonated specific pathogen-free (SPF) chicken eggs along the chorioallantoic membrane (CAM) route and the Vero cell line after several blind passages. The viral attachment protein was targeted for molecular detection using polymerase chain reactions (PCR). For phylogenetic analysis, PCR-positive products were partially sequenced.

Results

The virus was evident in the cell line, showed cytopathic effects after the 13 blind passage, and on the CAM of SPF chicken eggs, exhibited thickening of the CAM with pock-like lesions. A total of 12 samples (32.43%) tested positive for LSDV by PCR. Phylogenetic analysis of the present isolates (accession numbers MN792649 and MN792650) revealed 100% similarity with strains from India (MN295064), Kenya (AF325528, MN072619, KX683219), Greece (KY829023), Serbia (KY702007), and Kazakhstan (MN642592); moreover, 99.43% to 100% similarity to the sheep pox virus.

Conclusion

Partially sequenced LSDV was developed as a vaccine seed and was first isolated in Bangladesh and characterized at the molecular level.

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.

Lumpy skin disease as an emerging infectious disease

Lumpy skin disease (LSD) is one of the most important emerging transboundary diseases. Recently, LSD has emerged in many countries in the northern hemisphere. The LSD virus has a huge genome and is highly resistant to environmental conditions. The virus is also host-specific and large ruminants, such as cattle and domestic water buffalo, are particularly susceptible. In addition, wild ruminants can serve as potential reservoirs for spreading the LSD virus. The emergence might be related to climate change in various regions because LSD is an arthropod-borne infectious disease. This disease causes enormous economic losses, such as leather damage, decreased milk production, abortion, and death in infected ruminants. The economic importance of LSD in the bovine industry has forced countries to develop and implement control strategies against the disease. With the recent global spread and the economic impact, LSD will be discussed intensively. In addition, effective preventive measures are suggested based on the presence or absence of LSD outbreaks.

Poxvirus Infections in Dairy Farms and Transhumance Cattle Herds in Nigeria

Lumpy Skin disease (LSD) is an economically important disease in cattle caused by the LSD virus (LSDV) of the genus Capripoxvirus, while pseudocowpox (PCP) is a widely distributed zoonotic cattle disease caused by the PCP virus (PCPV) of the genus Parapoxvirus. Though both viral pox infections are reportedly present in Nigeria, similarities in their clinical presentation and limited access to laboratories often lead to misdiagnosis in the field. This study investigated suspected LSD outbreaks in organized and transhumance cattle herds in Nigeria in 2020. A total of 42 scab/skin biopsy samples were collected from 16 outbreaks of suspected LSD in five northern States of Nigeria. The samples were analyzed using a high-resolution multiplex melting (HRM) assay to differentiate poxviruses belonging to Orthopoxvirus, Capripoxvirus, and Parapoxvirus genera. LSDV was characterized using four gene segments, namely the RNA polymerase 30 kDa subunit (RPO30), G-protein-coupled receptor (GPCR), the extracellular enveloped virus (EEV) glycoprotein and CaPV homolog of the variola virus B22R. Likewise, the partial B2L gene of PCPV was also analyzed. Nineteen samples (45.2%) were positive according to the HRM assay for LSDV, and five (11.9%) were co-infected with LSDV and PCPV. The multiple sequence alignments of the GPCR, EEV, and B22R showed 100% similarity among the Nigerian LSDV samples, unlike the RPO30 phylogeny, which showed two clusters. Some of the Nigerian LSDVs clustered within LSDV SG II were with commonly circulating LSDV field isolates in Africa, the Middle East, and Europe, while the remaining Nigerian LSDVs produced a unique sub-group. The B2L sequences of Nigerian PCPVs were 100% identical and clustered within the PCPV group containing cattle/Reindeer isolates, close to PCPVs from Zambia and Botswana. The results show the diversity of Nigerian LSDV strains. This paper also reports the first documented co-infection of LSDV and PCPV in Nigeria.

Modelling epidemic growth models for lumpy skin disease cases in Thailand using nationwide outbreak data, 2021-2022

Lumpy skin disease (LSD) is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa, Europe and Asia. In 2021, LSD outbreaks have been reported in almost all of Thailand's provinces. Indeed, fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling. Thus, the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models. The first- and second-order models in the forms of Lorentzian, Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth, Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases. Based on the root-mean-squared error (RMSE) and Akaike information criterion (AIC), results showed that both first and second orders of Pearson-type VII models and Richard's growth model (RGM) were fit to the data better than other models used in the present study. The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand. For disease preparedness purposes, we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down. Furthermore, the period when the growth rate changes at a slower rate, known as the inflection time, obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized. This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand. This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.