Risk analysis of African swine fever in Poland based on spatio-temporal pattern and Latin hypercube sampling, 2014-2017

Geographical Distribution, Spatial Directional Trends, and Spatio-Temporal Clusters of the First Rapid and Widespread Lumpy Skin Disease Outbreaks in Thailand

Thailand was recognized as having the highest number of lumpy skin disease (LSD) outbreaks in Southeast Asia during 2021. Understanding how LSD outbreaks spread over time and space can provide detailed insight into the distribution and pattern of the disease, allowing for more precise identification of areas with high disease burden. This study aims to explore the spread of LSD among cattle in Thailand during 2021 using spatial and spatio-temporal analyses. Data were analyzed using spatial analysis techniques, including spatial autocorrelation and directional distribution. Additionally, the spatio-temporal models, including space-time permutation (STP) and Poisson with various maximum reported cluster size (MRCS) settings, were applied to the data to determine LSD outbreak clusters. Results showed that a total of 642 LSD outbreaks were reported from March to December 2021. Districts with confirmed cases exhibited spatial autocorrelation, indicating the interconnected spread of LSD across different geographic areas. Furthermore, the disease distribution pattern appeared to extend to the southern and southwestern regions from the northeast. Based on the spatio-temporal models, LSD outbreak clusters were identified in several regions. The STP model tended to identify more clusters with smaller radii compared to the Poisson model. The number of clusters detected varied according to both the model and MRCS setting, underscoring the importance of selecting the most relevant clusters for the effective implementation of disease control strategies. This study was the first of its kind to assess the spatial direction and spatio-temporal distribution of LSD outbreak clusters based on national-level data. Evaluating LSD occurrence through spatial and spatio-temporal analyses can provide valuable insight into its spatio-temporal dynamics, facilitating disease surveillance, control measures, and vector control strategies in Thailand.

Navigating the threat of African swine fever: a comprehensive review

African swine fever (ASF) is caused by Asfivirus and has become one of the most important diseases of swine in recent years. ASF was an endemic disease of the sub-Saharan Africa but later spread to various parts of the world. The infection in ticks and wild swine, alongside global pork trade, drives its spread and persistence. Once introduced to an area, the disease is difficult to eliminate due to sylvatic, domestic, and tick-swine transmission cycles. Because of the existence of various modes of transmission of the ASF virus, biosecurity measures have not been very successful. The line of treatment is not of much use and the outcome of this disease is usually fatal. The prognosis or the recovery of the animal depends on the virulence of the strain involved. Development of vaccines has been attempted but to date has not been very successful. This review focuses on the basic context of ASF, the challenges associated with it, and the options that might be available to prevent its occurrence which includes the different vaccine development strategies tried and tested till now.

Identifying risk clusters for African swine fever in Korea by developing statistical models

Introduction

African swine fever (ASF) is a disease with a high mortality rate and high transmissibility. Identifying high-risk clusters and understanding the transmission characteristics of ASF in advance are essential for preventing its spread in a short period of time. This study investigated the spatial and temporal heterogeneity of ASF in the Republic of Korea by analyzing surveillance data on wild boar carcasses.

Methods

We observed a distinct annual propagation pattern, with the occurrence of ASF-infected carcasses trending southward over time. We developed a rank-based statistical model to evaluate risk by estimating the average weekly number of carcasses per district over time, allowing us to analyze and identify risk clusters of ASF. We conducted an analysis to identify risk clusters for two distinct periods, Late 2022 and Early 2023, utilizing data from ASF-infected carcasses. To address the underestimation of risk and observation error due to incomplete surveillance data, we estimated the number of ASF-infected individuals and accounted for observation error via different surveillance intensities.

Results

As a result, in Late 2022, the risk clusters identified by observed and estimated number of ASF-infected carcasses were almost identical, particularly in the northwestern Gyeongbuk region, north Chungbuk region, and southwestern Gangwon region. In Early 2023, we observed a similar pattern with numerous risk clusters identified in the same regions as in Late 2022.

Discussion

This approach enhances our understanding of ASF spatial dynamics. Additionally, it contributes to the epidemiology and study of animal infectious diseases by highlighting areas requiring urgent and focused intervention. By providing crucial data for the targeted allocation of resources for disease management and preventive measures, our findings lay vital groundwork for improving ASF management strategies, ultimately aiding in the containment and control of this devastating disease.

Genomic Epidemiology of African Swine Fever Virus Identified in Domestic Pig Farms in South Korea during 2019-2021

African swine fever (ASF), a contagious viral disease, poses a significant threat to the global swine industry. In South Korea, ASF outbreaks have occurred since 2019, highlighting the need for a comprehensive understanding of the epidemiology and genetic characterization of the circulating African swine fever viruses (ASFVs). We obtained 21 ASFV isolates from domestic pig farms and analyzed their whole-genome sequences using the Illumina MiniSeq. Phylogenetic analysis was conducted using the maximum likelihood and time-scaled approaches to determine the genetic relationships and evolutionary dynamics of the Korean ASFV isolates. Comparative analysis of the 21 ASFV genomes with the reference strain Georgia 2007/1 revealed that while Korean isolates shared 11 mutations, they also had 22 discrete mutations, including single nucleotide polymorphisms and insertion/deletion polymorphisms (Indels). Phylogenetic analysis indicated that all Korean isolates were within the Asian subgroup of ASFV genotype II but were further divided into at least three distinct subclusters. Spatiotemporal analysis indicated multiple introductions of ASFVs into South Korea, crossing the national border with North Korea. In addition, we observed putative self-recombination between MGF 505-9R and MGF 505-10R genes in the ASFV/Korea/Pig/Inje2/2021 strain. Our findings provide insights into the genetic variations and evolution of ASFVs on South Korean pig farms from 2019 to 2021, uncovering multiple introductions of ASFVs across the national border, and highlighting the need for enhanced disease control strategies.

Spatio-temporal dynamics and distributional trend analysis of African swine fever outbreaks (2020-2021) in North-East India

African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern (NE) parts, since 47% of the Indian pig population is distributed in the NE region. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020-2021. The ASF outbreak data (2020-2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify the potential clusters, spatio-temporal aggregation, temporal distribution, disease spread, density maps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020-2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p < .01) and 2021 (z score = 4.89, p < .01). Spatial autocorrelation and Moran's I value (0.05-0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space-time analysis detected five, five, and two statistically significant (p < .01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north-south (2021), indicate the possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and disease control measures in NE Indian states.

COVID-19 epidemiology and rural healthcare: a survey in a Spanish village

We used primary care data to retrospectively describe the entry, spread, and impact of COVID-19 in a remote rural community and the associated risk factors and challenges faced by the healthcare team. Generalized linear models were fitted to assess the relationship between age, sex, period, risk group status, symptom duration, post-COVID illness, and disease severity. Social network and cluster analyses were also used. The first six cases, including travel events and a social event in town, contributed to early infection spread. About 351 positive cases were recorded and 6% of patients experienced two COVID-19 episodes in the 2.5-year study period. Five space-time case clusters were identified. One case, linked with the social event, was particularly central in its contact network. The duration of disease symptoms was driven by gender, age, and risk factors. The probability of suffering severe disease increased with symptom duration and decreased over time. About 27% and 23% of individuals presented with residual symptoms and post-COVID illness, respectively. The probability of developing a post-COVID illness increased with age and the duration of COVID-associated symptoms. Carefully registered primary care data may help optimize infection prevention and control efforts and upscale local healthcare capacities in vulnerable rural communities.

Seasonal Occurrence of African Swine Fever in Wild Boar and Domestic Pigs in EU Member States

Since 2007, African swine fever (ASF) has spread widely within Europe and beyond. Most affected countries recorded outbreaks in domestic pigs and cases in wild boar. Outbreak data from 2014 to 2021 were used to investigate the seasonal pattern of ASF in domestic pigs and wild boar across affected member states of the European Union, since knowledge of seasonal patterns may provide the potential to adapt prevention, surveillance and control during times of increased risk. In domestic pigs, a yearly peak was observed in many European countries in summer (predominantly in July and August). In wild boar, the patterns showed more variability. In many countries, there was a seasonal peak of ASF occurrence in winter (predominantly in January and December), with an additional summer peak in the Baltic States (predominantly in July) and a further spring peak in Poland (predominantly in March). The observed seasonal effects may be related to the abundance and population dynamics of wild boar and to seasonality in pig farming. Moreover, ASF occurrence may also be influenced by human activities in both domestic pigs and wild boar.

Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021)

Pseudorabies virus (PRV) is a double-stranded linear DNA virus capable of infecting various animals, including humans. We collected blood samples from 14 provinces in China between December 2017 and May 2021 to estimate PRV seroprevalence. The PRV gE antibody was detected using the enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis identified potential risk factors associated with PRV gE serological status at the farm level. Spatial-temporal clusters of high PRV gE seroprevalence were explored using SaTScan 9.6 software. Time-series data of PRV gE seroprevalence were modeled using the autoregressive moving average (ARMA) method. A Monte Carlo sampling simulation based on the established model was performed to analyze epidemic trends of PRV gE seroprevalence using @RISK software (version 7.0). A total of 40,024 samples were collected from 545 pig farms across China. The PRV gE antibody positivity rates were 25.04% (95% confidence interval [CI], 24.61% to 25.46%) at the animal level and 55.96% (95% CI, 51.68% to 60.18%) at the pig farm level. Variables such as farm geographical division, farm topography, African swine fever (ASF) outbreak, and porcine reproductive and respiratory syndrome virus (PRRSV) control in pig farms were identified as risk factors for farm-level PRV infection. Five significant high-PRV gE seroprevalence clusters were detected in China for the first time, with a time range of 1 December 2017 to 31 July 2019. The monthly average change value of PRV gE seroprevalence was -0.826%. The probability of a monthly PRV gE seroprevalence decrease was 0.868, while an increase was 0.132. IMPORTANCE PRV is a critical pathogen threatening the global swine industry. Our research fills knowledge gaps regarding PRV prevalence, infection risk factors, spatial-temporal clustering of high PRV gE seroprevalence, and the epidemic trend of PRV gE seroprevalence in China in recent years. These findings are valuable for the clinical prevention and control of PRV infection and suggest that PRV infection is likely to be successfully controlled in China.

Immunobiological Characteristics of the Attenuated African Swine Fever Virus Strain Katanga-350

The African swine fever virus (ASFV) is the cause of a recent pandemic that is threatening the global pig industry. The virus infects domestic and wild pigs and manifests with a variety of clinical symptoms, depending on the strain. No commercial vaccine is currently available to protect animals from this virus, but some attenuated and recombinant live vaccine candidates might be effective against the disease. This article describes the immunobiological characteristics of one such candidate—the laboratory-attenuated ASFV strain, Katanga-350—which belongs to genotype I. In this study, we assessed clinical signs and post-mortem changes, the levels of viremia and the presence of viral DNA caused by injection of ASF virus strains Katanga-350, Lisbon-57, and Stavropol 08/01. Intramuscular injection of this strain protected 80% of pigs from a virulent strain of the same genotype and seroimmunotype (Lisbon-57). At least 50% of the surviving pigs received protection from subsequent intramuscular infection with a heterologous (genotype II, seroimmunotype VIII) virulent strain (Stavropol 08/01). Virus-specific antibodies were detectable in serum and saliva samples between 8–78 days after the first inoculation of the Katanga-350 strain (the observational period). The results suggested that this strain could serve as a basis for the development of a recombinant vaccine against ASF viruses belonging to seroimmunotype I.

Temporal and Spatial Evolution of the African Swine Fever Epidemic in Vietnam

African swine fever (ASF) is a severe infectious disease affecting domestic and wild suids. Spatiotemporal dynamics analysis of the ASF is crucial to understanding its transmission. The ASF broke out in Vietnam in February 2019. The research on the spatiotemporal evolution characteristics of ASF in Vietnam is lacking. Spatiotemporal statistical methods, including direction analysis, spatial autocorrelation analysis, and spatiotemporal scan statistics were used to reveal the dynamics of the spatial diffusion direction and spatiotemporal aggregation characteristics of ASF in Vietnam. According to the cessation of the epidemic, it was divided into three phases: February to August 2019 (phase 1), April to December 2020 (phase 2), and January 2021 to March 2022 (phase 3). The ASF showed a significant spread trend from north to south in phase 1. The occurrence rate of the ASF aggregated spatially in phase 1 and became random in phases 2 and 3. The high−high ASF clusters (the province was a high cluster and both it and its neighbors had a high ASF occurrence rate) were concentrated in the north in phases 1 and 2. Four spatiotemporal high-risk ASF clusters were identified with a mean radius of 121.88 km. In general, there were significant concentrated outbreak areas and directional spread in the early stage and small-scale, high-frequency, and randomly scattered outbreaks in the later stage. The findings could contribute to a deeper understanding of the spatiotemporal spread of the ASF in Vietnam.

Quantifying risk factors and potential geographic extent of African swine fever across the world

African swine fever (ASF) has spread to many countries in Africa, Europe and Asia in the past decades. However, the potential geographic extent of ASF infection is unknown. Here we combined a modeling framework with the assembled contemporary records of ASF cases and multiple covariates to predict the risk distribution of ASF at a global scale. Local spatial variations in ASF risk derived from domestic pigs is influenced strongly by livestock factors, while the risk of having ASF in wild boars is mainly associated with natural habitat covariates. The risk maps show that ASF is to be ubiquitous in many areas, with a higher risk in areas in the northern hemisphere. Nearly half of the world’s domestic pigs (1.388 billion) are in the high-risk zones. Our results provide a better understanding of the potential distribution beyond the current geographical scope of the disease.

Spatiotemporal characteristics analysis and potential distribution prediction of peste des petits ruminants (PPR) in China from 2007-2018

Peste des petits ruminants (PPR) is a highly infectious disease that mainly infects small ruminants. To date, PPR has been confirmed in more than 70 countries. In China, PPR has occurred in more than 20 provinces and cities. In this study, based on geographic information system (GIS), spatial analysis was used to examine the occurrence of PPR in China from 2007 to 2018. The results showed that PPR first occurred in Tibet and gradually spread to other provinces. The outbreaks of PPR were concentrated in 2014, 2015 and 2018. Combining climate factors with the maximum entropy (MaxEnt), the results also suggested that the potential risk areas of PPR outbreaks in China were mainly Jiangsu, Yunnan and Anhui in Southeast China. Finally, a phylogenetic tree was used to analyse the evolutionary relationship between the peste des petits ruminants virus (PPRV) in China and the global ones, and it was found that the one in China had a close genetic relationship with the one in Mongolia, India and Bangladesh. Understanding and forecasting the distribution of PPR in China will help policymakers develop targeted monitoring plans. Likewise, analysing the global PPRV epidemic trends will play an important role in the elimination and prevention of PPR.

African Swine Fever in Wild Boar in Europe-A Review

The introduction of genotype II African swine fever (ASF) virus, presumably from Africa into Georgia in 2007, and its continuous spread through Europe and Asia as a panzootic disease of suids, continues to have a huge socio-economic impact. ASF is characterized by hemorrhagic fever leading to a high case/fatality ratio in pigs. In Europe, wild boar are especially affected. This review summarizes the currently available knowledge on ASF in wild boar in Europe. The current ASF panzootic is characterized by self-sustaining cycles of infection in the wild boar population. Spill-over and spill-back events occur from wild boar to domestic pigs and vice versa. The social structure of wild boar populations and the spatial behavior of the animals, a variety of ASF virus (ASFV) transmission mechanisms and persistence in the environment complicate the modeling of the disease. Control measures focus on the detection and removal of wild boar carcasses, in which ASFV can remain infectious for months. Further measures include the reduction in wild boar density and the limitation of wild boar movements through fences. Using these measures, the Czech Republic and Belgium succeeded in eliminating ASF in their territories, while the disease spread in others. So far, no vaccine is available to protect wild boar or domestic pigs reliably against ASF.

Risk assessment and integrated surveillance of foot-and-mouth disease outbreaks in Russia based on Monte Carlo simulation

Background

Foot-and-mouth disease (FMD) is a highly contagious disease of livestock worldwide. Russia is a big agricultural country with a wide geographical area where FMD outbreaks have become an obstacle for the development of the animal and animal products trade. In this study, we aimed to assess the export risk of FMD from Russia.

Results

After simulation by Monte Carlo, the results showed that the probability of cattle infected with FMD in the surveillance zone (Surrounding the areas where no epidemic disease has occurred within the prescribed time limit, the construction of buffer areas is called surveillance zone.) of Russia was 1.29 × 10^− 6. The probability that at least one FMD positive case was exported from Russia per year in the surveillance zone was 6 %. The predicted number of positive cattle of the 39,530 - 50,576 exported from Russia per year was 0.06. A key node in the impact model was the probability of occurrence of FMD outbreaks in the Russian surveillance zone. By semi-quantitative model calculation, the risk probability of FMD defense system defects was 1.84 × 10^− 5, indicating that there was a potential risk in the prevention and control measures of FMD in Russia. The spatial time scan model found that the most likely FMD cluster (P < 0.01) was in the Eastern and Siberian Central regions.

Conclusions

There was a risk of FMDV among cattle exported from Russia, and the infection rate of cattle in the monitored area was the key factor. Understanding the export risk of FMD in Russia and relevant epidemic prevention measures will help policymakers to develop targeted surveillance plans.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-02967-x.

Assessing the geographic range of classical swine fever vaccinations by spatiotemporal modelling in Japan

A classical swine fever (CSF) epidemic has been ongoing in Japan since September 2018. The outbreak started in Gifu Prefecture and involved 21 prefectures by the end of October 2020, posing a serious threat to pork industries. The present study was conducted to capture the spatiotemporal dynamics of CSF in Japan and assess the geographic range of the CSF vaccination on pig farms. First infection dates were collected for wild boars and on swine farms by prefecture. A simple statistical model was used to describe the spatiotemporal dynamics of CSF, describing the infection risk in wild boars and the subsequent transmission hazards to swine farms for 47 prefectures. Because the spatial transmission mechanisms and wild boar population dynamics involved substantial uncertainties, 16 models were applied to the empirical data. Estimated hazard parameters were used to predict the risk of infection on swine farms by 15 December 2020 to explicitly evaluate the governmental recommendation for vaccinations on pig farms by prefecture in light of the predicted infection risk in domestic pigs. The best‐fit model for the wild boars indicated that transmission occurred via neighbouring prefectures and involved seasonality. The estimated conditional hazard was 0.008 (95% confidence interval [CI]: 0.001–0.014) per day for infections transmitted from wild boars to swine farms, and the median time from wild boar infection to swine farm infection was 129.4 days (95% CI: 69.5–935.0). Our prediction indicated that prefectures connected by land to those with wild boar infections had a higher risk of infection on swine farms. CSF transmission in Japan likely progressed diffusively via wild boar movement, and tracking wild boar infections may help determine the risk of infection on swine farms. Our risk map highlights the importance of deciding vaccination policies according to predicted risk.

African Swine Fever in wild boar: Assessing interventions in South Korea

African Swine Fever (ASF) was detected in South Korean pig farms in September 2019. Currently, ASF occurs mostly in wild boar (Sus scrofa). We describe the ASF dynamics in wild boar in South Korea from October 2019 to October 2020 and use case studies to evidence the advantages and limitations of the control measures applied. During 2019, ASF remained confined in fenced areas of three counties. Since January 2020 however, the ASF management policy changed from fencing with limited disturbance to culling (with more disturbance), and ASF spread east and south. Until 31 October 2020, a total of 775 wild boar ASF cases have been confirmed, affecting 9 counties. Interventions for ASF control in wild boar included silent (trapping) and non-silent (shooting) population control, local and large-scale fencing, and carcass destruction. Pre-ASF wild boar densities were closed to 10 per km² . Biosafety risks arose from the movements of people and vehicles, swill feeding of wild boar, destroying pig herds, handling wild boar during trapping and hunting, and searching for and disposing of carcasses. Despite training efforts, biosafety regulations were sometimes ignored. We observed differences between counties regarding disease control. While interventions apparently succeeded in controlling ASF in one site where geographical features and fast decision making facilitated an early and efficient fencing, and culling was performed silently, biosafety problems and habitat- and management-related delays hindered ASF control in other situations. Given that carcass, destruction faces specific limitations in South Korea, fencing and trapping (under appropriate biosafety conditions) might represent the most effective intervention option.

A Review of Risk Factors of African Swine Fever Incursion in Pig Farming within the European Union Scenario

African swine fever (ASF) is a notifiable viral disease of pigs and wild boars that could lead to serious economic losses for the entire European pork industry. As no effective treatment or vaccination is available, disease prevention and control rely on strictly enforced biosecurity measures tailored to the specific risk factors of ASF introduction within domestic pig populations. Here, we present a review addressing the risk factors associated with different European pig farming systems in the context of the actual epidemiological scenario. A list of keywords was combined into a Boolean query, “African swine fever” AND (“Risk factors” OR “Transmission” OR “Spread” OR “Pig farming” OR “Pigs” OR “Wild boars”); was run on 4 databases; and resulted in 52 documents of interest being reviewed. Based on our review, each farming system has its own peculiar risk factors: commercial farms, where best practices are already in place, may suffer from unintentional breaches in biosecurity, while backyard and outdoor farms may suffer from poor ASF awareness, sociocultural factors, and contact with wild boars. In the literature selected for our review, human-related activities and behaviours are presented as the main risks, but we also stress the need to implement biosecurity measures also tailored to risks factors that are specific for the different pig farming practices in the European Union (EU).

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus

Background

This study postulates that underlying environmental conditions and a susceptible population's socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data.

Methods

Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters.

Results

Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Spatio-Temporal Analysis of the Spread of ASF in the Russian Federation in 2017-2019

Currently, African swine fever (ASF) is one of the biggest global economic challenges in Europe and Asia. Despite all the efforts done to understand the mechanism of spread, presence and maintenance of ASF in domestic pigs and wild boar, there are still many gaps in the knowledge on its epidemiology.

This study aims to describe spatial and temporal patterns of ASF spread in wild boar and domestic pigs in the country during the last three years. Methods of Spatio-temporal scanning statistics of Kulldorff (SatScan) and Mann-Kendell statistics (space-time cube) were used to identify potential clusters of outbreaks and the presence of hot spots (areas of active flare clusters), respectively. The results showed that ASF in the country has a local epidemic pattern of spread (11 explicit clusters in wild boar and 16 epizootic clusters were detected in the domestic pig population: 11 in the European part and 5 in the Asian part), and only six of them are overlapped suggesting that ASF epidemics in domestic pigs and wild boar are two separate processes. In the Nizhny Novgorod, Vladimir, Ivanovo, Novgorod, Pskov, Leningrad regions, the clusters identified are characterized as sporadic epidemics clusters, while in the Ulyanovsk region, Primorsky Territory, and the Jewish Autonomous Region the clusters are consistent. Considering the low biosecurity level of pig holdings in the far east and its close economic and cultural connections with China as well as other potential risk factors, it can be expected that the epidemic will be present in the region for a long time. The disease has spread in the country since 2007, and now it is reoccurring in some of the previously affected regions. Outbreaks in the domestic pig sector can be localized easily (no pattern detected), while the presence of the virus in wildlife (several consecutive hot spots detected) hampers its complete eradication. Although the disease has different patterns of spread over the country its driving forces remain the same (human-mediated spread and wild boar domestic-pigs mutual spillover). The results indicate that despite all efforts taken since 2007, the policy of eradication of the disease needs to be reviewed, especially measures in wildlife.

Retrospective spatial analysis for African swine fever in endemic areas to assess interactions between susceptible host populations

African Swine Fever (ASF) is one of the most complex and significant diseases from a sanitary-economic perspective currently affecting the world's swine-farming industry. ASF has been endemic in Sardinia (Italy) since 1978, and several control and eradication programmes have met with limited success. In this traditional ASF endemic area, there are three susceptible host populations for this virus sharing the same habitat: wild boar, farmed domestic pigs and non-registered free-ranging pigs (known as "brado" animals). The main goal of this study was to determine and predict fine-scale spatial interactions of this multi-host system in relation to the epidemiology of ASF in the main endemic area of Sardinia, Montes-Orgosolo. To this end, simultaneous monitoring of GPS-GSM collared wild boar and free-ranging pigs sightings were performed to predict interaction indexes through latent selection difference functions with environmental, human and farming factors. Regarding epidemiological assessment, the spatial inter-specific interaction indexes obtained here were used to correlate ASF notifications in wild boar and domestic pig farms. Daily movement patterns, home ranges (between 120.7 and 2,622.8 ha) and resource selection of wild boar were obtained for the first time on the island. Overall, our prediction model showed the highest spatial interactions between wild boar and free-ranging pigs in areas close to pig farms. A spatially explicit model was obtained to map inter-specific interaction over the complete ASF-endemic area of the island. Our approach to monitoring interaction indexes may help explain the occurrence of ASF notifications in wild boar and domestic pigs on a fine-spatial scale. These results support the recent and effective eradication measures taken in Sardinia. In addition, this methodology could be extrapolated to apply in the current epidemiological scenarios of ASF in Eurasia, where exist multi-host systems involving free-ranging pigs and wild boar.

Dynamics of Classical Swine Fever Spread in Wild Boar in 2018-2019, Japan

The prolongation of the classic swine fever (CSF) outbreak in Japan in 2018 was highly associated with the persistence and widespread of the CSF virus (CSFV) in the wild boar population. To investigate the dynamics of the CSF outbreak in wild boar, spatiotemporal analyses were performed. The positive rate of CSFV in wild boar fluctuated dramatically from March to June 2019, but finally stabilized at approximately 10%. The Euclidean distance from the initial CSF notified farm to the farthest infected wild boar of the day constantly increased over time since the initial outbreak except in the cases reported from Gunma and Saitama prefectures. The two-month-period prevalence, estimated using integrated nested Laplace approximation, reached >80% in half of the infected areas in March–April 2019. The area affected continued to expand despite the period prevalence decreasing up to October 2019. A large difference in the shapes of standard deviational ellipses and in the location of their centroids when including or excluding cases in Gunma and Saitama prefectures indicates that infections there were unlikely to have been caused simply by wild boar activities, and anthropogenic factors were likely involved. The emergence of concurrent space–time clusters in these areas after July 2019 indicated that CSF outbreaks were scattered by this point in time. The results of this epidemiological analysis help explain the dynamics of the spread of CSF and will aid in the implementation of control measures, including bait vaccination.

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator-Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%-84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision-makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

BACKGROUND

β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen.

METHODS

In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors.

RESULTS

The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging.

CONCLUSION

This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

Role of Wild Boar in the Spread of Classical Swine Fever in Japan

Since September 2018, nearly 900 notifications of classical swine fever (CSF) have been reported in Gifu Prefecture (Japan) affecting domestic pig and wild boar by the end of August 2019. To determine the epidemiological characteristics of its spread, a spatio-temporal analysis was performed using actual field data on the current epidemic. The spatial study, based on standard deviational ellipses of official CSF notifications, showed that the disease likely spread to the northeast part of the prefecture. A maximum significant spatial association estimated between CSF notifications was 23 km by the multi-distance spatial cluster analysis. A space-time permutation analysis identified two significant clusters with an approximate radius of 12 and 20 km and 124 and 98 days of duration, respectively. When the area of the identified clusters was overlaid on a map of habitat quality, approximately 82% and 75% of CSF notifications, respectively, were found in areas with potential contact between pigs and wild boar. The obtained results provide information on the current CSF epidemic, which is mainly driven by wild boar cases with sporadic outbreaks on domestic pig farms. These findings will help implement control measures in Gifu Prefecture.