Space–time interaction as an indicator of local spread during the 2001 FMD outbreak in the UK

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010-2020

Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state−space model with Box−Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and hybrid methods. These methods were applied to monthly n-FMD episodes (n = 1209) from January 2010 to December 2020. Results showed that the n-FMD episodes had a stable trend from 2010 to 2020, but they appeared to increase from 2014 to 2020. The outbreak episodes followed a seasonal pattern, with a predominant peak occurring from September to November annually. The single-technique methods yielded the best-fitting time-series models, including SARIMA(1,0,1)(0,1,1)12, NNAR(3,1,2)12,ETS(A,N,A), and TBATS(1,{0,0},0.8,{<12,5>}. Moreover, SARIMA-NNAR and NNAR-TBATS were the hybrid models that performed the best on the validation datasets. The models that incorporate seasonality and a non-linear trend performed better than others. The forecasts highlighted the rising trend of n-FMD episodes in Thailand, which shares borders with several FMD endemic countries in which cross-border trading of cattle is found common. Thus, control strategies and effective measures to prevent FMD outbreaks should be strengthened not only in Thailand but also in neighboring countries.

Variance partitioning in spatio-temporal disease mapping models

Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov random fields, that we name the variance partitioning model. The variance partitioning model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability. The use of a penalized complexity prior on the mixing parameter aids in coding prior information in an intuitive way. We illustrate the advantages of the variance partitioning model using two case studies.

Subgrouping and analysis of relationships between classical swine fever virus identified during the 2018-2020 epidemic in Japan by a novel approach using shared genomic variants

Classical swine fever (CSF) is a worldwide devastating disease of the pig industry caused by classical swine fever virus (CSFV). In September 2018, an outbreak of CSF occurred in Japan where the disease had been eradicated and was officially designated a CSF-free country since 2015. Following the detection of the first 2018 case on a farm in Gifu Prefecture, the disease spread among both farm pigs and wild boars and still continues. Epigenome analysis using whole-genome information is helpful in identifying the infection route, but the current approaches provide an insufficient resolution. In this study, a novel method of using single-nucleotide variants (SNVs) was employed to identify the associations among 158 isolates (65 from farms and 93 from wild boars). The identified groups of CSFV strains were plotted in different colours on a map, identifying the location where each strain was collected. The lack of an SNV set shared between the index case and the other strains suggested the first infection in Japan during the outbreak occurred in wild boars, not at the index farm. For the Atsumi Peninsula outbreaks, where nine farms were found infected within a 10-km radius area, the farm strains were assembled into three groups, suggesting these outbreaks resulted from at least three different infection events in this area. For the infections in the area around Saitama Prefecture, an area remote from the epicentre, strains from both the farms and wild boars were identified as being in the same group, suggesting they resulted from one viral introduction. Likewise, seven infected farms in Okinawa Prefecture, almost 1,500 km from Gifu Prefecture, were identified as being in a common, but separate group. By demonstrating the variety of transmission routes and possibility of long-distance infection, these results will help improve disease control measures.

Spatiotemporal Dynamics of Highly Pathogenic Avian Influenza Subtype H5N8 in Poultry Farms, South Korea

Highly pathogenic avian influenza (HPAI), a zoonotic disease, is a major threat to humans and poultry health worldwide. In January 2014, HPAI virus subtype H5N8 first infected poultry farms in South Korea, and 393 outbreaks, overall, were reported with enormous economic damage in the poultry industry. We analyzed the spatiotemporal distribution of HPAI H5N8 outbreaks in poultry farms using the global and local spatiotemporal interaction analyses in the first (January to July 2014) and second (September 2014 to June 2015) outbreak waves. The space–time K-function analyses revealed significant interactions within three days and in an over-40 km space–time window between the two study periods. The excess risk attributable value (D₀) was maintained despite the distance in the case of HPAI H5N8 in South Korea. Eleven spatiotemporal clusters were identified, and the results showed that the HPAI introduction was from the southwestern region, and spread to the middle region, in South Korea. This spatiotemporal interaction indicates that the HPAI epidemic in South Korea was mostly characterized by short period transmission, regardless of the distance. This finding supports strict control strategies such as preemptive depopulation, and poultry movement tracking. Further studies are needed to understand HPAI disease transmission patterns.

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.

Statistical Exploration of Local Transmission Routes for African Swine Fever in Pigs in the Russian Federation, 2007-2014

African swine fever (ASF) is a devastating viral disease of swine that is present in both pigs and wild boar in the western part of the Russian Federation and the eastern part of the European Union. It represents a significant threat for the European pig production industry as neither treatment nor vaccine is available. This study analysed the spatial and spatio-temporal distributions of ASF cases that were reported in domestic pigs and wild boar for assessing the likelihood of wild boar-to-domestic pig and farm-to-farm transmission routes in the epidemic that occurred from 2007 to 2014 in the Krasnodar and the Tver regions, two of the most affected areas of the Russian Federation. Results suggest that in both regions, the spatial proximity to an infectious farm was a strong risk factor for infection of a susceptible farm. In the Krasnodar region, the results of the statistical analysis suggest that the epidemics in wild boar and in domestic pigs were independent from each other. In contrast, there seemed to be a dependence between the two epidemics in the Tver region. But because outbreaks in domestic pigs were not statistically significantly clustered around wild boar cases, the joint spatial distribution of wild boar cases and of outbreaks in domestic pigs in the Tver region may be explained by regular spillovers from the domestic pig to the wild boar population. These findings confirm the need to maintain high biosecurity standards on pig farms and justify strict control measures targeted at domestic pig production such as culling of infected herds and local movement restrictions.

Resurgence of Pseudoperonospora cubensis: The Causal Agent of Cucurbit Downy Mildew

The downy mildew pathogen, Pseudoperonospora cubensis, which infects plant species in the family Cucurbitaceae, has undergone major changes during the last decade. Disease severity and epidemics are far more destructive than previously reported, and new genotypes, races, pathotypes, and mating types of the pathogen have been discovered in populations from around the globe as a result of the resurgence of the disease. Consequently, disease control through host plant resistance and fungicide applications has become more complex. This resurgence of P. cubensis offers challenges to scientists in many research areas including pathogen biology, epidemiology and dispersal, population structure and population genetics, host preference, host-pathogen interactions and gene expression, genetic host plant resistance, inheritance of host and fungicide resistance, and chemical disease control. This review serves to summarize the current status of this major pathogen and to guide future management and research efforts within this pathosystem.

Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems

During the last 30years it has become commonplace for epidemiological studies to collect locational attributes of disease data. Although this advancement was driven largely by the introduction of handheld global positioning systems (GPS), and more recently, smartphones and tablets with built-in GPS, the collection of georeferenced disease data has moved beyond the use of handheld GPS devices and there now exist numerous sources of crowdsourced georeferenced disease data such as that available from georeferencing of Google search queries or Twitter messages. In addition, cartography has moved beyond the realm of professionals to crowdsourced mapping projects that play a crucial role in disease control and surveillance of outbreaks such as the 2014 West Africa Ebola epidemic. This paper provides a comprehensive review of a range of innovative sources of spatial animal and human health data including data warehouses, mHealth, Google Earth, volunteered geographic information and mining of internet-based big data sources such as Google and Twitter. We discuss the advantages, limitations and applications of each, and highlight studies where they have been used effectively.

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

Exploratory space-time analyses of Rift Valley Fever in South Africa in 2008-2011

BACKGROUND

Rift Valley fever (RVF) is a zoonotic arbovirosis for which the primary hosts are domestic livestock (cattle, sheep and goats). RVF was first described in South Africa in 1950-1951. Mechanisms for short and long distance transmission have been hypothesised, but there is little supporting evidence. Here we describe RVF occurrence and spatial distribution in South Africa in 2008-11, and investigate the presence of a contagious process in order to generate hypotheses on the different mechanisms of transmission.

METHODOLOGY/PRINCIPAL FINDINGS

A total of 658 cases were extracted from World Animal Health Information Database. Descriptive statistics, epidemic curves and maps were produced. The space-time K-function was used to test for evidence of space-time interaction. Five RVF outbreak waves (one in 2008, two in 2009, one in 2010 and one in 2011) of varying duration, location and size were reported. About 70% of cases (n = 471) occurred in 2010, when the epidemic was almost country-wide. No strong evidence of space-time interaction was found for 2008 or the second wave in 2009. In the first wave of 2009, a significant space-time interaction was detected for up to one month and over 40 km. In 2010 and 2011 a significant intense, short and localised space-time interaction (up to 3 days and 15 km) was detected, followed by one of lower intensity (up to 2 weeks and 35 to 90 km).

CONCLUSIONS/SIGNIFICANCE

The description of the spatiotemporal patterns of RVF in South Africa between 2008 and 2011 supports the hypothesis that during an epidemic, disease spread may be supported by factors other than active vector dispersal. Limitations of under-reporting and space-time K-function properties are discussed. Further spatial analyses and data are required to explain factors and mechanisms driving RVF spread.

Connecting network properties of rapidly disseminating epizoonotics

BACKGROUND

To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure.

METHODS

Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) 'connectivity', a model that integrated bio-physical concepts (the agent's transmission cycle, road topology) into indicators designed to measure networks ('nodes' or infected sites with short- and long-range links), and 2) 'contacts', which focused on infected individuals but did not assess connectivity.

RESULTS

THE CONNECTIVITY MODEL SHOWED FIVE NETWORK PROPERTIES: 1) spatial aggregation of cases (disease clusters), 2) links among similar 'nodes' (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a "20:80" pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads.

CONCLUSIONS

Geo-temporal constructs of Network Theory's nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.

The application of epidemiology in aquatic animal health -opportunities and challenges

Over recent years the growth in aquaculture, accompanied by the emergence of new and transboundary diseases, has stimulated epidemiological studies of aquatic animal diseases. Great potential exists for both observational and theoretical approaches to investigate the processes driving emergence but, to date, compared to terrestrial systems, relatively few studies exist in aquatic animals. Research using risk methods has assessed routes of introduction of aquatic animal pathogens to facilitate safe trade (e.g. import risk analyses) and support biosecurity. Epidemiological studies of risk factors for disease in aquaculture (most notably Atlantic salmon farming) have effectively supported control measures. Methods developed for terrestrial livestock diseases (e.g. risk-based surveillance) could improve the capacity of aquatic animal surveillance systems to detect disease incursions and emergence. The study of disease in wild populations presents many challenges and the judicious use of theoretical models offers some solutions. Models, parameterised from observational studies of host pathogen interactions, have been used to extrapolate estimates of impacts on the individual to the population level. These have proved effective in estimating the likely impact of parasite infections on wild salmonid populations in Switzerland and Canada (where the importance of farmed salmon as a reservoir of infection was investigated). A lack of data is often the key constraint in the application of new approaches to surveillance and modelling. The need for epidemiological approaches to protect aquatic animal health will inevitably increase in the face of the combined challenges of climate change, increasing anthropogenic pressures, limited water sources and the growth in aquaculture.

Spatiotemporal spread of cucurbit downy mildew in the eastern United States

The dynamics of cucurbit downy mildew, caused by Pseudoperonospora cubensis, in the eastern United States in 2008 and 2009 were investigated based on disease records collected in 24 states as part of the Cucurbit downy mildew ipmPIPE monitoring program. The mean season-long rate of temporal disease progress across the 2 years was 1.4 new cases per day. Although cucurbit downy mildew was detected in mid-February and early March in southern Florida, the disease progressed slowly during the spring and early summer and did not enter its exponential phase until mid-June. The median nearest-neighbor distance of spread of new disease cases was ≈110 km in both years, with ≈15% of the distances being >240 km. Considering disease epidemics on all cucurbits, the epidemic expanded at a rate of 9.2 and 10.5 km per day in 2008 and 2009, respectively. These rates of spatial spread are at the lower range of those reported for the annual spread of tobacco blue mold in the southeastern United States, a disease that is also aerially dispersed over long distances. These results suggest that regional spread of cucurbit downy mildew may be limited by opportunities for establishment in the first half of the year, when fewer cucurbit hosts are available for infection. The O-ring statistic was used to determine the spatial pattern of cucurbit downy mildew outbreaks using complete spatial randomness as the null model for hypothesis testing. Disease outbreaks in both years were spatially aggregated and the extent of spatial dependence was up to 1,000 km. Results from the spatial analysis suggests that disease outbreaks in the Great Lakes and mid-Atlantic regions may be due to the spread of P. cubensis sporangia from outbreaks of the disease near the Georgia/South Carolina/North Carolina border rather than from overwintering sites in southern Florida. Space-time point pattern analysis indicated strong (P < 0.001) evidence for a space-time interaction and a space-time risk window of ≈3 to 5 months after first disease outbreak and 300 to 600 km was detected in both years. Results of this study support the hypothesis that infection of cucurbits by P. cubensis appears to be an outcome of a contagion process, and the relative large space-time window suggests that factors occurring on a large spatial scale (≈1,000 km) facilitate the spread of cucurbit downy mildew in the eastern United States.

Spread of porcine circovirus associated disease (PCVAD) in Ontario (Canada) swine herds: Part I. Exploratory spatial analysis

Background

The systemic form of porcine circovirus associated disease (PCVAD), also known as postweaning multisystemic wasting syndrome (PMWS) was initially detected in the early 1990s. Starting in 2004, the Canadian swine industry experienced considerable losses due to PCVAD, concurrent with a shift in genotype of porcine circovirus type 2 (PCV2). Objectives of the current study were to explore spatial characteristics of self-reported PCVAD distribution in Ontario between 2004 and 2008, and to investigate the existence and nature of local spread.

Results

The study included 278 swine herds from a large disease-monitoring project that included porcine reproductive and respiratory syndrome (PRRS) virus-positive herds identified by the diagnostic laboratory, and PRRS virus-negative herds directly from the target population. Herds were included if they had growing pigs present on-site and available geographical coordinates for the sampling site. Furthermore, herds were defined as PCVAD-positive if a producer reported an outbreak of circovirus associated disease, or as PCVAD-negative if no outbreak was noted. Spatial trend was investigated using generalized additive models and time to PCVAD outbreak in a herd using Cox's proportional hazard model; spatial and spatio-temporal clustering was explored using K-functions; and location of most likely spatial and spatio-temporal clusters was investigated using scan statistics. Over the study period, the risk of reporting a PCVAD-positive herd tended to be higher in the eastern part of the province after adjustment for herd PRRS status (P = 0.05). This was partly confirmed for spread (Partial P < 0.01). Local spread also appeared to exist, as suggested by the tentative (P = 0.06) existence of spatio-temporal clustering of PCVAD and detection of a spatio-temporal cluster (P = 0.04).

Conclusions

In Ontario, PCVAD has shown a general trend, spreading from east-to-west. We interpret the existence of spatio-temporal clustering as evidence of spatio-temporal aggregation of PCVAD-positive cases above expectations and, together with the existence of spatio-temporal and spatial clusters, as suggestive of apparent local spread of PCVAD. Clustering was detected at small spatial and temporal scales. Other patterns of spread could not be detected; however, survival rates in discrete Ontario zones, as well as a lack of a clear spatial pattern in the most likely spatio-temporal clusters, suggest other between-herd transmission mechanisms.

Foot-and-mouth disease in Tanzania from 2001 to 2006

Foot-and-mouth disease (FMD) is endemic in Tanzania, with outbreaks occurring almost each year in different parts of the country. There is now a strong political desire to control animal diseases as part of national poverty alleviation strategies. However, FMD control requires improving the current knowledge on the disease dynamics and factors related to FMD occurrence so control measures can be implemented more efficiently. The objectives of this study were to describe the FMD dynamics in Tanzania from 2001 to 2006 and investigate the spatiotemporal patterns of transmission. Extraction maps, the space-time K-function and space-time permutation models based on scan statistics were calculated for each year to evaluate the spatial distribution, the spatiotemporal interaction and the spatiotemporal clustering of FMD-affected villages. From 2001 to 2006, 878 FMD outbreaks were reported in 605 different villages of 5815 populated places included in the database. The spatial distribution of FMD outbreaks was concentrated along the Tanzania-Kenya, Tanzania-Zambia borders, and the Kagera basin bordering Uganda, Rwanda and Tanzania. The spatiotemporal interaction among FMD-affected villages was statistically significant (P≤0.01) and 12 local spatiotemporal clusters were detected; however, the extent and intensity varied across the study period. Dividing the country in zones according to their epidemiological status will allow improving the control of FMD and delimiting potential FMD-free areas.

Simulation of the K-function in the analysis of spatial clustering for non-randomly distributed locations--exemplified by bovine virus diarrhoea virus (BVDV) infection in Denmark

The K-function is often used to detect spatial clustering in spatial point processes, e.g. clustering of infected herds. Clustering is identified by testing the observed K-function for complete spatial randomness modelled, e.g. by a homogeneous Poisson process. The approach provides information about spatial clustering as well as the scale of distances of clustering. However, there are several problems related to applying the K-function, e.g. estimation of the size of the study area and the assumption about modelling spatial random distribution of the events by, e.g. a homogeneous Poisson process. The objective of the present study was to develop a null hypothesis version of the K-function that overcomes the assumption about a specific underlying spatial distribution characterising complete spatial randomness. Furthermore, the objective was to develop an approach that does not include the estimation of the size of the study area. The paper presents a simulation procedure to derive the null hypothesis version of the K-function. The null hypothesis version of the K-function is simulated by random sampling of N(+) locations from the distribution of N observed locations (infected (N(+)) and non-infected (N-N(+))). The differences between the empirical and the estimated null-hypothesis version of the K-function are plotted together with the 95% simulation envelopes versus the distance, h. In this way we test if the spatial distribution of the infected herds differs from the spatial distribution of the herd locations in general. The approach also overcomes edge effects and problems with complex shapes of the study region. An application to bovine virus diarrhoea virus (BVDV) infection in Denmark is described.

Animal disease and human trauma: the psychosocial implications of the 2001 UK foot and mouth disease disaster

The 2001 UK foot and mouth disease (FMD) crisis is commonly understood to have been a nonhuman animal problem, an economic industrial crisis that was resolved after eradication. By using a different lens, a longitudinal ethnographic study of the health and social consequences of the epidemic, the research reported here indicates that 2001 was a human tragedy as well as an animal one. In a diary-based study, it can be seen that life after the FMD crisis was accompanied by distress, feelings of bereavement, fear of a new disaster, loss of trust in authority and systems of control, and the undermining of the value of local knowledge. Diverse groups experienced distress well beyond the farming community. Such distress remained largely invisible to the range of "official" inquiries into the disaster. That an FMD epidemic of the scale of 2001 could happen again in a developed country is a deeply worrying prospect, but it is to be hoped that contingency plans are evolving along with enhanced understanding of the human, animal, and financial cost.