Spatial distribution of foot-and-mouth disease in Pakistan estimated using imperfect data

A scoping review of foot-and-mouth disease risk, based on spatial and spatio-temporal analysis of outbreaks in endemic settings

Foot-and-mouth disease (FMD) is one of the most important transboundary animal diseases affecting livestock and wildlife species worldwide. Sustained viral circulation, as evidenced by serological surveys and the recurrence of outbreaks, suggests endemic transmission cycles in some parts of Africa, Asia and the Middle East. This is the result of a complex process in which multiple serotypes, multi-host interactions and numerous socio-epidemiological factors converge to facilitate disease introduction, survival and spread. Spatial and spatio-temporal analyses have been increasingly used to explore the burden of the disease by identifying high-risk areas, analysing temporal trends and exploring the factors that contribute to the outbreaks. We systematically retrieved spatial and spatial-temporal studies on FMD outbreaks to summarize variations on their methodological approaches and identify the epidemiological factors associated with the outbreaks in endemic contexts. Fifty-one studies were included in the final review. A high proportion of papers described and visualized the outbreaks (72.5%) and 49.0% used one or more approaches to study their spatial, temporal and spatio-temporal aggregation. The epidemiological aspects commonly linked to FMD risk are broadly categorizable into themes such as (a) animal demographics and interactions, (b) spatial accessibility, (c) trade, (d) socio-economic and (e) environmental factors. The consistency of these themes across studies underlines the different pathways in which the virus is sustained in endemic areas, with the potential to exploit them to design tailored evidence based-control programmes for the local needs. There was limited data linking the socio-economics of communities and modelled FMD outbreaks, leaving a gap in the current knowledge. A thorough analysis of FMD outbreaks requires a systemic view as multiple epidemiological factors contribute to viral circulation and may improve the accuracy of disease mapping. Future studies should explore the links between socio-economic and epidemiological factors as a foundation for translating the identified opportunities into interventions to improve the outcomes of FMD surveillance and control initiatives in endemic contexts.

Spatial distribution of foot-and-mouth disease (FMD) outbreaks in South Africa (2005-2016)

Foot-and-mouth disease (FMD) is a transboundary animal disease that has negative socioeconomic consequences including impacts on food security. In South Africa, FMD outbreaks in communal farming communities cause major livestock and human livelihood concerns; they raise apprehensions about the effectiveness of FMD control measures within the FMD protection areas. This study aimed to identify high-risk areas for FMD outbreaks at the human/domestic animal/wildlife interface of South Africa. Cuzick-Edwards tests and Kulldorff scan statistics were used to detect spatial autocorrelation and spatial-temporal clusters of FMD outbreaks for the years 2005-2016.Four high-risk clusters were identified and the spatial distribution of outbreaks in cattle were closer to game reserve fences and consistent with wildlife contacts as a main contributor of FMD occurrence. Strategic allocation of resources, focused control measures, and cooperation between the affected provinces are recommended to reduce future outbreaks. Further research is necessary to design cost-effective control strategies for FMD.

Vector Surveillance, Host Species Richness, and Demographic Factors as West Nile Disease Risk Indicators

West Nile virus (WNV) is the most common arthropod-borne virus (arbovirus) in the United States (US) and is the leading cause of viral encephalitis in the country. The virus has affected tens of thousands of US persons total since its 1999 North America introduction, with thousands of new infections reported annually. Approximately 1% of humans infected with WNV acquire neuroinvasive West Nile Disease (WND) with severe encephalitis and risk of death. Research describing WNV ecology is needed to improve public health surveillance, monitoring, and risk assessment. We applied Bayesian joint-spatiotemporal modeling to assess the association of vector surveillance data, host species richness, and a variety of other environmental and socioeconomic disease risk factors with neuroinvasive WND throughout the conterminous US. Our research revealed that an aging human population was the strongest disease indicator, but climatic and vector-host biotic interactions were also significant in determining risk of neuroinvasive WND. Our analysis also identified a geographic region of disproportionately high neuroinvasive WND disease risk that parallels the Continental Divide, and extends southward from the US-Canada border in the states of Montana, North Dakota, and Wisconsin to the US-Mexico border in western Texas. Our results aid in unraveling complex WNV ecology and can be applied to prioritize disease surveillance locations and risk assessment.

Seroprevalence and risk factors for foot-and-mouth disease in cattle in Baghlan Province, Afghanistan

A serological study of 376 cattle from 198 herds and a concurrent survey of farmers were undertaken in 53 villages in Khinjan, Doshi and Puli Khumri districts of Baghlan province, Afghanistan to determine the seroprevalence of Foot and Mouth Disease (FMD) and to identify risk factors for seropositive herds. A total of 419 cases of FMD were reported by the farmers in the year preceding the survey. The animal-level population seroprevalence was estimated at 42.0% (95% CI, 37.0-47.2). The seroprevalence increased with age in the sampled cattle (<2 years - 30.4%, 2-6 years - 40.3% and >6 years - 52.2%). Herds were more likely to be seropositive if the farmers: had purchased cattle in the year prior to the survey (OR = 2.6; 95% CI, 1.37-4.97); purchased ruminants from unknown (potentially risky) sources (OR = 2.13; 95% CI, 1.13-4.03); and sold milk to the market (OR = 1.99; 95% CI, 1.09-3.63). Herds that had been vaccinated had a lower odds of being seropositive (OR = 0.33; 95% CI, 0.68-0.66). This was the first epidemiological study of FMD in Baghlan province and the findings provide valuable direction for disease control on FMD in this and other provinces in Afghanistan.

Review of epidemiological risk models for foot-and-mouth disease: Implications for prevention strategies with a focus on Africa

Foot-and-mouth disease (FMD) is a highly infectious transboundary disease that affects domestic and wild cloven-hoofed animal species. The aim of this review was to identify and critically assess some modelling techniques for FMD that are well supported by scientific evidence from the literature with a focus on their use in African countries where the disease remains enzootic. In particular, this study attempted to provide a synopsis of the relative strengths and weaknesses of these models and their relevance to FMD prevention policies. A literature search was conducted to identify quantitative and qualitative risk assessments for FMD, including studies that describe FMD risk factor modelling and spatiotemporal analysis. A description of retrieved papers and a critical assessment of the modelling methods, main findings and their limitations were performed. Different types of models have been used depending on the purpose of the study and the nature of available data. The most frequently identified factors associated with the risk of FMD occurrence were the movement (especially uncontrolled animal movement) and the mixing of animals around water and grazing points. Based on the qualitative and quantitative risk assessment studies, the critical pathway analysis showed that the overall risk of FMDV entering a given country is low. However, in some cases, this risk can be elevated, especially when illegal importation of meat and the movement of terrestrial livestock are involved. Depending on the approach used, these studies highlight shortcomings associated with the application of models and the lack of reliable data from endemic settings. Therefore, the development and application of specific models for use in FMD endemic countries including Africa is encouraged.

A Probability Co-Kriging Model to Account for Reporting Bias and Recognize Areas at High Risk for Zebra Mussels and Eurasian Watermilfoil Invasions in Minnesota

Zebra mussels (ZMs) (Dreissena polymorpha) and Eurasian watermilfoil (EWM) (Myriophyllum spicatum) are aggressive aquatic invasive species posing a conservation burden on Minnesota. Recognizing areas at high risk for invasion is a prerequisite for the implementation of risk-based prevention and mitigation management strategies. The early detection of invasion has been challenging, due in part to the imperfect observation process of invasions including the absence of a surveillance program, reliance on public reporting, and limited resource availability, which results in reporting bias. To predict the areas at high risk for invasions, while accounting for underreporting, we combined network analysis and probability co-kriging to estimate the risk of ZM and EWM invasions. We used network analysis to generate a waterbody-specific variable representing boater traffic, a known high risk activity for human-mediated transportation of invasive species. In addition, co-kriging was used to estimate the probability of species introduction, using waterbody-specific variables. A co-kriging model containing distance to the nearest ZM infested location, boater traffic, and road access was used to recognize the areas at high risk for ZM invasions (AUC = 0.78). The EWM co-kriging model included distance to the nearest EWM infested location, boater traffic, and connectivity to infested waterbodies (AUC = 0.76). Results suggested that, by 2015, nearly 20% of the waterbodies in Minnesota were at high risk of ZM (12.45%) or EWM (12.43%) invasions, whereas only 125/18,411 (0.67%) and 304/18,411 (1.65%) are currently infested, respectively. Prediction methods presented here can support decisions related to solving the problems of imperfect detection, which subsequently improve the early detection of biological invasions.

Data-Driven Models of Foot-and-Mouth Disease Dynamics: A Review

Foot-and-mouth disease virus (FMDV) threatens animal health and leads to considerable economic losses worldwide. Progress towards minimizing both veterinary and financial impact of the disease will be made with targeted disease control policies. To move towards targeted control, specific targets and detailed control strategies must be defined. One approach for identifying targets is to use mathematical and simulation models quantified with accurate and fine-scale data to design and evaluate alternative control policies. Nevertheless, published models of FMDV vary in modelling techniques and resolution of data incorporated. In order to determine which models and data sources contain enough detail to represent realistic control policy alternatives, we performed a systematic literature review of all FMDV dynamical models that use host data, disease data or both data types. For the purpose of evaluating modelling methodology, we classified models by control strategy represented, resolution of models and data, and location modelled. We found that modelling methodology has been well developed to the point where multiple methods are available to represent detailed and contact-specific transmission and targeted control. However, detailed host and disease data needed to quantify these models are only available from a few outbreaks. To address existing challenges in data collection, novel data sources should be considered and integrated into models of FMDV transmission and control. We suggest modelling multiple endemic areas to advance local control and global control and better understand FMDV transmission dynamics. With incorporation of additional data, models can assist with both the design of targeted control and identification of transmission drivers across geographic boundaries.

Statistical models for spatially explicit biological data

Existing algorithms for predicting species' distributions sit on a continuum between purely statistical and purely biological approaches. Most of the existing algorithms are aspatial because they do not consider the spatial context, the occurrence of the species or conditions conducive to the species' existence, in neighbouring areas. The geostatistical techniques of kriging and cokriging are presented in an attempt to encourage biologists more frequently to consider them. Unlike deterministic spatial techniques they provide estimates of prediction errors. The assumptions and applications of common geostatistical techniques are presented with worked examples drawn from a dataset of the bluetongue outbreak in northwest Europe in 2006. Emphasis is placed on the importance and interpretation of weights in geostatistical calculations. Covarying environmental data may be used to improve predictions of species' distributions, but only if their sampling frequency is greater than that of the species' or disease data. Cokriging techniques are unable to determine the biological significance or importance of such environmental data, because they are not designed to do so.

Spatial trend of Foot and Mouth Disease virus (FMDV) serotypes in cattle and buffaloes, Pakistan

The present study describes the frequency of Foot and Mouth Disease (FMD) virus serotypes (O, A and Asia-1) in major regions (all provinces) of Pakistan using Indirect Sandwich ELISA. Also, spatial distribution of various FMD serotypes and their comparison is discussed. A total of 590 samples (Epithelial tissue) have been analyzed during a period of five years (2005-2009). Out of 590 samples, 180 were found positive, giving an overall confirmation of FMDV about 33.2 %. Of the prevalent serotypes, FMDV 'O' serotype caused most outbreaks (20.7 %), followed by serotype A (6.6 %) and serotype Asia-1 (4.6 %) while there was no positive case of type 'C'. The study clearly showed that the disease was more frequent in the agro-climatic zones than in hilly areas. Based on the data of 590 samples (>50 outbreaks), the overall prevalence of FMDV in cattle and buffaloes in Pakistan was 33.2 %, while in cattle alone, it was 37.1 %, higher than in buffalo (28.7 %). There were eight cases of mixed serotypes infection, indicating the presence of endemic state of disease. Another significant feature was the change over time. In phase-I (2005-2007), there was an overall prevalence of 29.4 %, while the occurrence of the serotype O, A and Asia-1 was 20.4 %, 2.9 % and 4.7 %, respectively. During phase-II (2008-2009), the overall prevalence was 59.21 %, while those of serotype O, A and Asia-1 were 22.4 %, 31.6 % and 4.0 %, respectively. This clearly indicated a shift from serotype O to A, which may help to explain the occurrence of more severe outbreaks, despite vaccination.

Spatial-temporal model of haemorrhagic disease in white-tailed deer in south-east USA, 1983 to 2000

The present study constructed a spatial-temporal statistical model to identify the risk and protective factors for haemorrhagic disease (HD) in white-tailed deer in the five states of Alabama, Georgia, South Carolina, North Carolina and Tennessee. The response variable was binary, indicating the presence or absence of HD in an individual county, measured annually from 1983 to 2000. Predictor variables included climatic factors of temperature, rainfall, wind speed and dew point, remotely sensed data of normalised difference vegetation index (NDVI) and land surface temperature derived from archived remotely sensed advanced very-high-resolution radiometer (AVHRR) satellite data, elevation, a spatial autocorrelation (SA) term and a temporal autocorrelation term. This study first applied principal component factor analysis to reduce the volume of climatic data and remotely sensed data. Then, a generalised linear mixed model framework (GLMM) was used to develop a spatial-temporal statistical model. The results showed that the area under receiver operating characteristic curve (ROC) was 0.728, indicating a good overall fit of the model. The total prediction accuracy over the 18 year period with optimal cut-off probability was 67 per cent. The prediction accuracy for individual years ranged from 48 to 75 per cent.

The spatial epidemiologic (r)evolution: a look back in time and forward to the future

Spatial epidemiology enables you to better understand diseases or ill-health processes; investigate relationships between the environment and the presence of disease; conduct disease cluster analyses; predict disease spread; evaluate control alternatives; and basically do things an epidemiologist otherwise would have been unable to do and avoid many errors that otherwise may have been committed. Recently, the discipline of spatial epidemiology has advanced substantially, owing to a combination of reasons. The introduction of the electronic computer has clearly led this advancement. Computers have facilitated the storage, management, display and analysis of data, which are critical to geographic information systems (GIS). Also, because of computers and their increased capabilities and capacities, data collection has greatly expanded and reached a new level owing in large part to the advent of geographic positioning systems (GPS). GPS enables the collection of spatial locations, which in turn present yet another attribute (location) amenable to consideration in epidemiologic studies. At the same time, spatial software has taken advantage of the evolution of computers and data, further enabling epidemiologists to perform spatial analyses that they may not have even conceived of 30 years before. Capitalizing on these now, non-binding technologic constraints, epidemiologists are more able to combine their analytic expertise with computational advances, to develop approaches, which enable them to make spatial epidemiologic methods an integral part of their toolkits. Instead of a novelty, spatial epidemiology is now more of a necessity for outbreak investigations, surveillance, hypothesis testing, and generating follow-up activities necessary to perform a complete and proper epidemiologic analysis.

Nonparametric spatial analysis to detect high-risk regions for schistosomiasis in Guichi, China

Schistosomiasis control in China is facing a new challenge due to the rebound of epidemics in many areas and the unsustainable effects of the chemotherapy-based control strategy. Identifying high-risk regions for schistosomiasis is an important first step for an effective and sustainable strategy. Direct surveillance of snail habitats to detect high-risk regions is costly and no longer a desirable approach, while indirect monitoring of acute schistosomiasis may be a satisfactory alternative. To identify high-risk regions for schistosomiasis, we jointly used multiplicative and additive models with the kernel smoothing technique as the main approach to estimate the relative risk (RR) and excess risk (ER) surfaces by analyzing surveillance data for acute schistosomiasis. The feasibility of detecting high-risk regions for schistosomiasis through nonparametric spatial analysis was explored and confirmed in this study, and two significant high-risk regions were identified. The results provide useful hints for improving the national surveillance network for acute schistosomiasis and possible approaches to utilizing surveillance data more efficiently. In addition, the commonly used epidemiological indices, RR and ER, are examined and emphasized from the spatial point of view, which will be helpful for exploring many other epidemiological indices.

Temporospatial clustering of foot-and-mouth disease outbreaks in Israel and Palestine, 2006-2007

Foot-and-mouth disease (FMD) is endemic to the Middle East and there is a perception that political instability and limited resources have led to the uncontrolled circulation of FMD virus throughout the region. Certain aspects of FMD epidemiology in the Middle East remain unknown. The goal of this study was to identify the geographical location, temporal extent and direction of spread of clusters of 70 FMD outbreaks reported in Israel and Palestine from February 4, 2006, through July 15, 2007. The space-time permutation model of the scan statistic test detected nine significant (P < 0.1) clusters. Significant (P < 0.05) direction of spread was identified in four of the nine clusters. The Gaza Strip, where no outbreaks were reported, or a nearby location, seemed to be the origin of a cluster of outbreaks located in Hadarom (April 2007); a cluster of outbreaks centered in West Bank (February 2006) may be linked with spread from Northern Israel; a cluster in Hazafon (January 2007) seemed to have originated from nearby the Jordan borders; and a cluster located in Northern Hazafon was likely related to areas next to the Lebanon and Syrian borders. The association between the clusters in West Bank and earlier Israeli samples and between the cluster in Hazafon and Jordan was also supported (P < 0.05) by phylogenetic analysis of samples collected from the outbreaks. These results suggest that the FMD outbreaks reported in Israel and Palestine in 2006 and 2007 were likely a consequence of different epidemics associated with the circulation and spread of FMD virus strains from different regions of the Middle East.