Spatial multi-criteria decision analysis to predict suitability for African swine fever endemicity in Africa

Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease

Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.

A comparison of risk factor investigation and experts' opinion elicitation analysis for identifying foot-and-mouth disease (FMD) high-risk areas within the FMD protection zone of South Africa (2007-2016)

Foot-and-mouth disease is a controlled disease in accordance with the South African Animal Diseases Act (Act 35 of 1984). The country was classified by the World Organisation for Animal Health (WOAH) as having a FMD free zone without vaccination in 1996. However, this status was suspended in 2019 due to a FMD outbreak outside the controlled zones. FMD control in South Africa includes animal movement restrictions placed on cloven-hoofed species and products, prophylactic vaccination of cattle, clinical surveillance of susceptible species, and disease control fencing to separate livestock from wildlife reservoirs. The objectives of this study were to evaluate differences in identifying high-risk areas for FMD using risk factor and expert opinion elicitation analysis. Differences in risk between FMD introduction and FMD spread within the FMD protection zone with vaccination (PZV) of South Africa (2007-2016) were also investigated. The study was conducted in the communal farming area of the FMD PZV, which is adjacent to wildlife reserves and characterised by individual faming units. Eleven risk factors that were considered important for FMD occurrence and spread were used to build a weighted linear combination (WLC) score based on risk factor data and expert opinion elicitation. A multivariable conditional logistic regression model was also used to calculate predicted probabilities of a FMD outbreak for all dip-tanks within the study area. Smoothed Bayesian kriged maps were generated for 11 individual risk factors, overall WLC scores for FMD occurrence and spread and for predicted probabilities of a FMD outbreak based on the conditional logistic regression model. Descriptively, vaccine matching was believed to have a great influence on both FMD occurrence and spread. Expert opinion suggested that FMD occurrence was influenced predominantly by proximity to game reserves and cattle density. Cattle populations and vaccination practices were considered most important for FMD spread. Highly effective cattle inspections were observed within areas that previously reported FMD outbreaks, indicating the importance of cattle inspection (surveillance) as a necessary element of FMD outbreak detection. The multivariable conditional logistic regression analysis, which was consistent with expert opinion elicitation; identified three factors including cattle population density (OR 3.87, 95% CI 1.47-10.21) and proximities to game reserve fences (OR 0.82, 95% CI 0.73-0.92) and rivers (OR 1.04, 95% CI 1.01-1.07) as significant factors for reported FMD outbreaks. Regaining and maintaining an FMD-free status without vaccination requires frequent monitoring of high-risk areas and designing targeted surveillance.

Approaches for disease prioritization and decision-making in animal health, 2000-2021: a structured scoping review

This scoping review identifies and describes the methods used to prioritize diseases for resource allocation across disease control, surveillance, and research and the methods used generally in decision-making on animal health policy. Three electronic databases (Medline/PubMed, Embase, and CAB Abstracts) were searched for articles from 2000 to 2021. Searches identified 6, 395 articles after de-duplication, with an additional 64 articles added manually. A total of 6, 460 articles were imported to online document review management software (sysrev.com) for screening. Based on inclusion and exclusion criteria, 532 articles passed the first screening, and after a second round of screening, 336 articles were recommended for full review. A total of 40 articles were removed after data extraction. Another 11 articles were added, having been obtained from cross-citations of already identified articles, providing a total of 307 articles to be considered in the scoping review. The results show that the main methods used for disease prioritization were based on economic analysis, multi-criteria evaluation, risk assessment, simple ranking, spatial risk mapping, and simulation modeling. Disease prioritization was performed to aid in decision-making related to various categories: (1) disease control, prevention, or eradication strategies, (2) general organizational strategy, (3) identification of high-risk areas or populations, (4) assessment of risk of disease introduction or occurrence, (5) disease surveillance, and (6) research priority setting. Of the articles included in data extraction, 50.5% had a national focus, 12.3% were local, 11.9% were regional, 6.5% were sub-national, and 3.9% were global. In 15.2% of the articles, the geographic focus was not specified. The scoping review revealed the lack of comprehensive, integrated, and mutually compatible approaches to disease prioritization and decision support tools for animal health. We recommend that future studies should focus on creating comprehensive and harmonized frameworks describing methods for disease prioritization and decision-making tools in animal health.

An Updated Review of Ornithodoros Ticks as Reservoirs of African Swine Fever in Sub-Saharan Africa and Madagascar

This updated review provides an overview of the available information on Ornithodoros ticks as reservoirs and biological vectors of the ASF virus in Africa and Indian Ocean islands in order to update the current knowledge in this field, inclusive of an overview of available methods to investigate the presence of ticks in the natural environment and in domestic pig premises. In addition, it highlights the major areas of research that require attention in order to guide future investigations and fill knowledge gaps. The available information suggests that current knowledge is clearly insufficient to develop risk-based control and prevention strategies, which should be based on a sound understanding of genotype distribution and the potential for spillover from the source population. Studies on tick biology in the natural and domestic cycle, including genetics and systematics, represent another important knowledge gap. Considering the rapidly changing dynamics affecting the African continent (demographic growth, agricultural expansion, habitat transformation), anthropogenic factors influencing tick population distribution and ASF virus (ASFV) evolution in Africa are anticipated and have been recorded in southern Africa. This dynamic context, together with the current global trends of ASFV dissemination, highlights the need to prioritize further investigation on the acarological aspects linked with ASF ecology and evolution.

Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model

Background

African swine fever (ASF) is a serious contagious viral disease of pigs that affects the pig industry. This study aimed to evaluate the possible African swine fever (ASF) distribution using network analysis and a diffusion model through live pig, carcass, and pig product movement data.

Material and Methods

Empirical movement data from Thailand for the year 2019 were used, and expert opinions were sought to evaluate network properties and the diffusion model. The networks were presented as live pig movement and carcass movement data at the provincial and district levels. For network analysis, a descriptive network analysis was performed using outdegree, indegree, betweenness, fragmentation, and power law distribution, and cutpoints were used to describe movement patterns. For the diffusion model, we simulated each network using spatially different infected locations, patterns, and initial infection sites. Based on expert opinions, the initial infection site, the probability of ASF occurrence, and the probability of the initial infected adopter were selected for the appropriated network. In this study, we also simulated networks under varying network parameters to predict the infection speed.

Results and Conclusions

The total number of movements recorded was 2,594,364. These were divided into 403,408 (403,408/2,594,364; 15.55%) for live pigs and 2,190,956 (2,190,956/2,594,364; 84.45%) for carcasses. We found that carcass movement at the provincial level showed the highest outdegree (mean = 342.554, standard deviation (SD) = 900.528) and indegree values (mean = 342.554, SD = 665.509). In addition, the outdegree and indegree presented similar mean values and the degree distributions of both district networks followed a power-law function. The network of live pigs at provincial level showed the highest value for betweenness (mean = 0.011, SD = 0.017), and the network of live pigs at provincial level showed the highest value for fragmentation (mean = 0.027, SD = 0.005). Our simulation data indicated that the disease occurred randomly due to live pig and carcass movements along the central and western regions of Thailand, causing the rapid spread of ASF. Without control measures, it could spread to all provinces within 5- and 3-time units and in all districts within 21- and 30-time units for the network of live pigs and carcasses, respectively. This study assists the authorities to plan control and preventive measures and limit economic losses caused by ASF.

Determining the spatial distribution of environmental and socio-economic suitability for human leptospirosis in the face of limited epidemiological data

Background

Leptospirosis is among the leading zoonotic causes of morbidity and mortality worldwide. Knowledge about spatial patterns of diseases and their underlying processes have the potential to guide intervention efforts. However, leptospirosis is often an underreported and misdiagnosed disease and consequently, spatial patterns of the disease remain unclear. In the absence of accurate epidemiological data in the urban agglomeration of Santa Fe, we used a knowledge-based index and cluster analysis to identify spatial patterns of environmental and socioeconomic suitability for the disease and potential underlying processes that shape them.

Methods

We geocoded human leptospirosis cases derived from the Argentinian surveillance system during the period 2010 to 2019. Environmental and socioeconomic databases were obtained from satellite images and publicly available platforms on the web. Two sets of human leptospirosis determinants were considered according to the level of their support by the literature and expert knowledge. We used the Zonation algorithm to build a knowledge-based index and a clustering approach to identify distinct potential sets of determinants. Spatial similarity and correlations between index, clusters, and incidence rates were evaluated.

Results

We were able to geocode 56.36% of the human leptospirosis cases reported in the national epidemiological database. The knowledge-based index showed the suitability for human leptospirosis in the UA Santa Fe increased from downtown areas of the largest cities towards peri-urban and suburban areas. Cluster analysis revealed downtown areas were characterized by higher levels of socioeconomic conditions. Peri-urban and suburban areas encompassed two clusters which differed in terms of environmental determinants. The highest incidence rates overlapped areas with the highest suitability scores, the strength of association was low though (CSc r = 0.21, P < 0.001 and ESc r = 0.19, P < 0.001).

Conclusions

We present a method to analyze the environmental and socioeconomic suitability for human leptospirosis based on literature and expert knowledge. The methodology can be thought as an evolutive and perfectible scheme as more studies are performed in the area and novel information regarding determinants of the disease become available. Our approach can be a valuable tool for decision-makers since it can serve as a baseline to plan intervention measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-01010-x.

Application of Spatial Risk Assessment Integrated With a Mobile App in Fighting Against the Introduction of African Swine Fever in Pig Farms in Thailand: Development Study

BACKGROUND

African swine fever (ASF), a highly contagious disease affecting both domestic and wild pigs, has been having a serious impact on the swine industry worldwide. This important transboundary animal disease can be spread by animals and ticks via direct transmission and by contaminated feed and fomites via indirect transmission because of the high environmental resistance of the ASF virus. Thus, the prevention of the introduction of ASF to areas free of ASF is essential. After an outbreak was reported in China, intensive import policies and biosecurity measures were implemented to prevent the introduction of ASF to pig farms in Thailand.

OBJECTIVE

Enhancing prevention and control, this study aims to identify the potential areas for ASF introduction and transmission in Thailand, develop a tool for farm assessment of ASF risk introduction focusing on smallholders, and develop a spatial analysis tool that is easily used by local officers for disease prevention and control planning.

METHODS

We applied a multi-criteria decision analysis approach with spatial and farm assessment and integrated the outputs with the necessary spatial layers to develop a spatial analysis on a web-based platform.

RESULTS

The map that referred to potential areas for ASF introduction and transmission was derived from 6 spatial risk factors; namely, the distance to the port, which had the highest relative importance, followed by the distance to the border, the number of pig farms using swill feeding, the density of small pig farms (<50 heads), the number of pigs moving in the area, and the distance to the slaughterhouse. The possible transmission areas were divided into 5 levels (very low, low, medium, high, and very high) at the subdistrict level, with 27 subdistricts in 10 provinces having very high suitability and 560 subdistricts in 34 provinces having high suitability. At the farm level, 17 biosecurity practices considered as useful and practical for smallholders were selected and developed on a mobile app platform. The outputs from the previous steps integrated with necessary geographic information system layers were added to a spatial analysis web-based platform.

CONCLUSIONS

The tools developed in this study have been complemented with other strategies to fight against the introduction of ASF to pig farms in the country. The areas showing high and very high risk for disease introduction and transmission were applied for spatial information planning, for example, intensive surveillance, strict animal movement, and public awareness. In addition, farms with low biosecurity were improved in these areas, and the risk assessment developed on a mobile app in this study helped enhance this matter. The spatial analysis on a web-based platform helped facilitate disease prevention planning for the authorities.

Prioritisation of Provinces for African Swine Fever Intervention in South Africa through Decision Matrix Analysis

South Africa has experienced an increase in the number of African swine fever (ASF) outbreaks in domestic pigs in the last ten years. Intervention will be needed in the form of control and prevention strategies to minimise the impact of this disease in the country. The aim of this study is to prioritise which provinces resources should be allocated to for ASF intervention strategies, based on the risk factors identified as pertinent in South Africa. A multi-criteria decision analysis approach was followed using an analytic hierarchy process (AHP) method to determine the perceived risk of ASF outbreaks in domestic pigs per province. Nine risk factors applicable to the South African context were identified from literature. Data on the presence of these risk factors per province were collected from records and by means of a questionnaire. The risk factors were weighted by means of an AHP. The decision matrix determined that ASF intervention and prevention resources should be focused on Mpumalanga, Free State and Gauteng provinces in South Africa. Specific intervention strategies should be focused on the confinement of pigs, swill-feeding of pigs and buying/selling of pigs at auctions through a participatory approach with stakeholders.

Spatial-Temporal Movements of Free Ranging Pigs at the Wildlife-Livestock Interface of Murchison Falls National Park, Uganda: Potential of Disease Control at a Local Scale

In many Ugandan rural communities, pigs are generally kept under traditional smallholder systems without basic biosecurity measures in place. In some instances, these systems are at the livestock-wildlife interface, as it is the case in Nwoya district, which is bordered by Murchison Falls National Park (MFNP). This pig system has potential for the maintenance and transmission of pathogens like African swine fever (ASF) between different herds, and also with wild pigs (warthogs and bushpigs). In this paper, we describe the spatial and temporal pattern of the movements of free ranging domestic pigs in a rural setting in Northern Uganda where ASF is endemic. We also determine their use of habitat to highlight the potential interaction hotspots between domestic pigs and between domestic and wild pig populations. We fitted 10 free-ranging domestic pigs owned by different homesteads with GPS harnesses during rainy and dry seasons. The pig home range, daily distance, activity pattern and habitat use were calculated. Our results show that the maximum area covered (MCP 100%) by the pigs varied between 35,965 and 475,077 m2. The core area varied from 1,317 to 50,769 m2. The pigs' home ranges were significantly bigger during the dry season than during the rainy season (Wilcoxon test, W = 22, p = 0.04). The mean full day (24 h) distance was longer in the dry season than in the rainy season (Student test, t = 2.7, p = 0.03). The pigs were mostly located within their own homestead, but they also used other homesteads, grass and crop fields. This study highlights that free-ranging domestic pigs may cover a wide area, especially during the dry season. Interestingly, the home range of pigs from different herds may overlap with areas used by wild pigs which share crops and other resources in this area. This study provides insights into a better understanding of the potential for spread of diseases such as ASF at small-scale and can be used to raise awareness of such risks and to better target implementation of preventive measures.

Risk for African Swine Fever Introduction Into Kazakhstan

African swine fever (ASF) is a disease of swine that is endemic to some African countries and that has rapidly spread since 2007 through many regions of Asia and Europe, becoming endemic in some areas of those continents. Since there is neither vaccine nor treatment for ASF, prevention is an important action to avoid the economic losses that this disease can impose on a country. Although the Republic of Kazakhstan has remained free from the disease, some of its neighbors have become ASF-infected, raising concerns about the potential introduction of the disease into the country. Here, we have identified clusters of districts in Kazakhstan at highest risk for ASF introduction. Questionnaires were administered, and districts were visited to collect and document, for the first time, at the district level, the distribution of swine operations and population in Kazakhstan. A snowball sampling approach was used to identify ASF experts worldwide, and a conjoint analysis model was used to elicit their opinion in relation to the extent at which relevant epidemiological factors influence the risk for ASF introduction into disease-free regions. The resulting model was validated using data from the Russian Federation and Mongolia. Finally, the validated model was used to rank and categorize Kazakhstani districts in terms of the risk for serving as the point of entry for ASF into the country, and clusters of districts at highest risk of introduction were identified using the normal model of the spatial scan statistic. Results here will help to allocate resources for surveillance and prevention activities aimed at early detecting a hypothetical ASF introduction into Kazakhstan, ultimately helping to protect the sanitary status of the country.

Identification of risk areas for foot and mouth disease in Thailand using a geographic information system-based multi-criteria decision analysis

In our study, we used geographic information system (GIS)-based multi-criteria decision analysis (MCDA) to predict suitable areas for foot and mouth disease (FMD) occurrence in Thailand. Eleven experts evaluated 10 spatial risk factors associated with the occurrence and spread of FMD in Thailand during 2014-2015. The analytic hierarchy process was used to conduct problem structuring and prioritising of pairwise comparisons with criterion weighting. Important spatial risk factors were converted to geographical layers using standardised fuzzy membership. Thus, weight linear combination was used to combine and create suitability and uncertainty maps as well as to perform sensitivity analysis. We identified areas in northern, north-eastern, western, and central Thailand as hotspots of FMD occurrence. In the predictive map, the suitable areas presented a moderate degree of agreement with those after FMD outbreaks in the year 2016 (AUC = 0.71, 95 %CI: 0.68-0.75). In conclusion, GIS-based MCDA mapping well supported veterinary services in identifying hotspot areas of FMD occurrence in Thailand. This tool was very useful for disease surveillance.

Risk Mapping of Influenza D Virus Occurrence in Ruminants and Swine in Togo Using a Spatial Multicriteria Decision Analysis Approach

Influenza D virus (IDV) has been identified in several continents, with serological evidence for the virus in Africa. In order to improve the sensitivity and cost–benefit of IDV surveillance in Togo, risk maps were drawn using a spatial multicriteria decision analysis (MCDA) and experts’ opinion to evaluate the relevance of sampling areas used so far. Areas at highest risk of IDV occurrence were the main cattle markets. The maps were evaluated with previous field surveillance data collected in Togo between 2017 and 2019: 1216 sera from cattle, small ruminants, and swine were screened for antibodies to IDV by hemagglutination inhibition (HI) assays. While further samples collections are needed to validate the maps, the risk maps resulting from the spatial MCDA approach generated here highlight several priority areas for IDV circulation assessment.

Spatial Multicriteria Evaluation for Mapping the Risk of Occurrence of Peste des Petits Ruminants in Eastern Africa and the Union of the Comoros

Peste des petits ruminants virus (PPRV), responsible for peste des petits ruminants (PPR), is widely circulating in Africa and Asia. The disease is a huge burden for the economy and development of the affected countries. In Eastern Africa, the disease is considered endemic. Because of the geographic proximity and existing trade between eastern African countries and the Comoros archipelago, the latter is at risk of introduction and spread, and the first PPR outbreaks occurred in the Union of the Comoros in 2012. The objective of this study was to map the areas suitable for PPR occurrence and spread in the Union of the Comoros and four eastern African countries, namely Ethiopia, Uganda, Kenya, and Tanzania. A Geographic Information System (GIS)-based Multicriteria Evaluation (MCE) was developed. Risk factors for PPR occurrence and spread, and their relative importance, were identified using literature review and expert-based knowledge. Corresponding geographic data were collected, standardized, and combined based on a weighted linear combination to obtain PPR suitability maps. The accuracy of the maps was assessed using outbreak data from the EMPRES database and a ROC curve analysis. Our model showed an excellent ability to distinguish between absence and presence of outbreaks in Eastern Africa (AUC = 0.907; 95% CI [0.820-0.994]), and a very good performance in the Union of the Comoros (AUC = 0.889, 95% CI: [0.694-1]). These results highlight the efficiency of the GIS-MCE method, which can be applied at different geographic scales: continental, national and local. The resulting maps provide decision support tools for implementation of disease surveillance and control measures, thus contributing to the PPR eradication goal of OIE and FAO by 2030.

Pig empire under infectious threat: risk of African swine fever introduction into the People's Republic of China

Pig production and pork consumption are very important to the People's Republic of China for both economic and cultural reasons. The incursion and spread of a disease such as African swine fever (ASF), which emerged in Eastern Europe in 2007, could have devastating socioeconomic consequences for both the Chinese and the global pig industry. The Chinese government consequently attributes a very high priority to ASF and is actively seeking to improve its preparedness. This paper discusses different drivers and pathways of potential emergence of ASF in China in light of the country's specificities, including international movements of people, pigs and pig products, swill feeding practices and wild boar populations. It suggests that effective ASF risk management in China will require a comprehensive and integrated approach linking science and policy and will need to involve all relevant stakeholders to develop realistic policies.

Regional level risk factors associated with the occurrence of African swine fever in West and East Africa

Background

African swine fever (ASF) causes severe socio-economic impacts due to high mortality and trade restrictions. Many risk factors of ASF have been identified at farm level. However, understanding the risk factors, especially wild suid hosts, determining ASF transmission at regional level remains limited.

Methods

Based on ASF outbreak data in domestic pigs during 2006–2014, we here tested, separately for West and East Africa, which risk factors were linked to ASF presence at a regional level, using generalized linear mixed models.

Results

Our results show that ASF infections in the preceding year was an important predictor for ASF presence in both West and East Africa. Both pig density and human density were positively associated with ASF presence in West Africa. In East Africa, ASF outbreaks in domestic pigs were also correlated with higher percentages of areas occupied by giant forest hogs and by high-tick-risk areas.

Conclusions

Our results suggest that regional ASF risk in East Africa and in West Africa were associated with different sets of risk factors. Regional ASF risk in West Africa mainly followed the domestic cycle, whereas the sylvatic cycle may influence regional ASF risk in East Africa. With these findings, we contribute to the better understanding of the risk factors of ASF occurrence at regional scales that may aid the implementation of effective control measures.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-016-1953-z) contains supplementary material, which is available to authorized users.

Quantitative assessment of a spatial multicriteria model for highly pathogenic avian influenza H5N1 in Thailand, and application in Cambodia

The Highly Pathogenic Avian Influenza H5N1 (HPAI) virus is now considered endemic in several Asian countries. In Cambodia, the virus has been circulating in the poultry population since 2004, with a dramatic effect on farmers' livelihoods and public health. In Thailand, surveillance and control are still important to prevent any new H5N1 incursion. Risk mapping can contribute effectively to disease surveillance and control systems, but is a very challenging task in the absence of reliable disease data. In this work, we used spatial multicriteria decision analysis (MCDA) to produce risk maps for HPAI H5N1 in poultry. We aimed to i) evaluate the performance of the MCDA approach to predict areas suitable for H5N1 based on a dataset from Thailand, comparing the predictive capacities of two sources of a priori knowledge (literature and experts), and ii) apply the best method to produce a risk map for H5N1 in poultry in Cambodia. Our results showed that the expert-based model had a very high predictive capacity in Thailand (AUC = 0.97). Applied in Cambodia, MCDA mapping made it possible to identify hotspots suitable for HPAI H5N1 in the Tonlé Sap watershed, around the cities of Battambang and Kampong Cham, and along the Vietnamese border.

Prokaryotic expression, purification and antigenicity analysis of African swine fever virus pK205R protein

African swine fever is an acute, febrile and highly virulent porcine disease causing serious economic losses worldwide. The pK205R protein of the African swine fever virus (ASFV) is largely expressed in the early stages of infection, which has given the K205R gene extensive attention. In this study, the ASFV K205R was cloned and expressed in Escherichia coli BL21 (DE3). Expression of histidine-tagged pK205R with a molecular mass of 44 kDa was determined by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Western blot analysis. Optimisation of culture conditions allowed induction of the recombinant protein with 0.4 mM Isopropyl β-D-thiogalactoside (IPTG) at 37°C for 2 h. The protein existed in cellular supernatant and was purified using a Ni-NTA resin column. The purified protein was used to immunize rabbits four times to enable the production of polyclonal antibodies, and the antiserum titre was detected by ELISA. The results showed that the purified pK205R can react with ASFV positive serum specifically by Western blotting. The pK205R had high antigenicity, which indicated that pK205R could be used as an antigen for detection of ASFV-specific antibodies in ELISA testing, and the recombinant protein could contribute to further research of the action and structure of pK205R.

Spatio-temporal patterns and movement analysis of pigs from smallholder farms and implications for African swine fever spread, Limpopo province, South Africa

Infectious and zoonotic disease outbreaks have been linked to increasing volumes of legal and illegal trade. Spatio-temporal and trade network analyses have been used to evaluate the risks associated with these challenges elsewhere, but few details are available for the pig sector in South Africa. Regarding pig diseases, Limpopo province is important as the greater part of the province falls within the African swine fever control area. Emerging small-scale pig farmers in Limpopo perceived pig production as an important means of improving their livelihood and an alternative investment. They engage in trading and marketing their products with a potential risk to animal health, because the preferred markets often facilitate potential longdistance spread and disease dispersal over broad geographic areas. In this study, we explored the interconnectedness of smallholder pig farmers in Limpopo, determined the weaknesses and critical control points, and projected interventions that policy makers can implement to reduce the risks to pig health. The geo-coordinates of surveyed farms were used to draw maps, links and networks. Predictive risks to pigs were determined through the analyses of trade networks, and the relationship to previous outbreaks of African swine fever was postulated. Auction points were identified as high-risk areas for the spread of animal diseases. Veterinary authorities should prioritise focused surveillance and diagnostic efforts in Limpopo. Early disease detection and prompt eradication should be targeted and messages promoting enhanced biosecurity to smallholder farmers are advocated. The system may also benefit from the restructuring of marketing and auction networks. Since geographic factors and networks can rapidly facilitate pig disease dispersal over large areas, a multi-disciplinary approach to understanding the complexities that exist around the animal disease epidemiology becomes mandatory.

Spatial and temporal epidemiological analysis in the Big Data era

Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.

Identifying Areas Suitable for the Occurrence of Rift Valley Fever in North Africa: Implications for Surveillance

Rift Valley fever (RVF) is a vector-borne zoonotic disease that has caused widespread outbreaks throughout Africa and the Arabian Peninsula, with serious consequences for livestock-based economies and public health. Although there have never been any reports of RVF in Morocco, Algeria, Tunisia and Libya, it is a priority disease in the Maghreb, due to the threat of introduction of the virus through transboundary livestock movements or infected mosquito vectors. However, the implementation of surveillance activities and early warning contingency plans requires better knowledge of the epidemiological situation. We conducted a multicriteria decision analysis, integrating host distribution with a combination of important ecological factors that drive mosquito abundance, to identify hotspots and suitable time periods for RVF enzootic circulation (i.e. stable transmission at a low to moderate level for an extended period of time) and an RVF epizootic event (i.e. a sudden occurrence of a large number of infected animals over a large geographic area) in the Maghreb. We also modelled vector species distribution using available information on vector presence and habitat preference. We found that the northern regions of the Maghreb were moderately suitable for RVF enzootics, but highly suitable for RVF epizootics. The vector species distribution model identified these regions as the most favourable mosquito habitats. Due to the low density of animal hosts and arid conditions, the desert region showed low RVF suitability, except in oases. However, the presence of competent vectors in putative unsuitable areas underlines the need for further assessments of mosquito habitat preference. This study produced monthly RVF suitability maps useful for animal health managers and veterinary services involved in designing risk-based surveillance programmes. The suitability maps can be further enhanced using existing country-specific sources of information and by incorporating knowledge - as it becomes available - on the epidemiology of the disease and distribution of vectors in the Maghreb.