Extremely low probability of exposure (8.69 × 10−6 to 3.81 × 10−5) for the three sectors of the pig industry, with exposure through direct swill feeding being 10–100 times more likely to occur than through contact with infected feral pigs. Spread of FMD from the index farm is most likely to occur through movement of contaminated fomites, pigs, and ruminants. The virus is more likely to spread from small-scale piggeries selling at saleyards and abattoirs than from other piggeries. The most influential factors on the spread of FMD from the index farm is the ability of the farmer to detect FMD, the probability of FMD spread through contaminated fomites and the presence of ruminants on the farm. Although small-scale producers selling informally move animals less frequently and do not use external staff, movement of pigs to non-commercial pathways could jeopardize animal traceability in the event of a disease outbreak. This study suggests that producers’ awareness on and engagement with legislative and industry requirements in relation to biosecurity and emergency animal disease management needs to be improved. Results from this study could be used by decision-makers to prioritize resource allocation for improving animal biosecurity in the pig industry.Small-scale pig producers are believed to pose higher biosecurity risks for the introduction and spread of exotic diseases than commercial pig producers. However, the magnitude of these risks is poorly understood. This study is a comparative assessment of the risk of introduction and spread of foot-and-mouth disease (FMD) through different sectors of the pig industry: (1) large-scale pig producers; (2) small-scale producers (<100 sows) selling at saleyards and abattoirs; and (3) small-scale producers selling through informal means. An exposure and consequence assessments were conducted using the World Organization for Animal Health methodology for risk analysis, assuming FMD virus was introduced into Australia through illegal importation of infected meat. A quantitative assessment, using scenario trees and Monte Carlo stochastic simulation, was used to calculate the probabilities of exposure and spread. Input data for these assessments were obtained from a series of data gathering exercises among pig producers, industry statistics, and literature. Findings of this study suggest there is an  Small landholders are commonly thought to pose biosecurity risks to mainstream livestock production, although the magnitude and significance of these risks have not been previously evaluated. Practices of small-scale pig producers are believed to be associated with a higher risk of introduction and spread of exotic diseases than those of larger producers –5. PreviCharacteristics of Australia, such as geographical isolation and quarantine procedures, provide the country with a privileged disease free status for major livestock diseases, like foot-and-mouth disease (FMD). However, the potential for the introduction of exotic diseases still exists. Illegal introduction of meat products by incoming passengers from infected countries is the highest risk source of entry of FMD into Australia –15. BetwThe Productivity Commission  and BuetSpread of disease from the index farm will depend on on-farm biosecurity practices and animal movement patterns of pig enterprises. Understanding these practices among the different sectors of the pig industry is crucial to assess the risk of exotic disease introduction and spread posed by each of these sectors. This study conducts a comparative exposure and partial consequence assessment among different sectors of the pig industry in Australia. The aim of this study is to investigate how the FMD virus, which is assumed to be introduced into the country through illegal importation of contaminated meat, could exposure pigs at the index piggery and subsequently spread from this piggery. The sectors of the pig industry considered are: (1) large-scale or mainstream pig producers; (2) small-scale producers (<100 sows) selling through saleyards and abattoirs; and (3) small-scale producers selling through informal means. These assessments quantify the nature and magnitude of the biosecurity risks posed by each sector of the pig industry. This information could support decision-makers for the prioritization of resources allocation for improving biosecurity in the pig industry.This comparative risk assessment follows the World Organization for Animal Health (OIE) methodology for risk analysis  and usesAn entry assessment as outlined by the OIE risk analysis methodology was not performed in this study as this assessment assumed that FMD had already been introduced into Australia. The assumption was that the virus was introduced through illegal importation of FMD-infected salted or cured meat and an estimated amount of introduced infected meat per year of 5 kg was used. The exposure assessment describes the potential pathways for pigs from the three different types of piggeries getting exposed to the FMD virus and estimates the probability of these pathways to occur. The partial consequence assessment describes the potential pathways of spread of FMD virus from the index farm and estimates the probability of this spread occurring. The assessment of the impacts of the resulting FMD outbreaks after virus introduction is not reported in this manuscript. The scenario trees were implemented in Microsoft Excel  and probabilities were determined using Monte Carlo stochastic simulation modeling with the @RISK software . The outcome probabilities for each pathway of exposure and spread were calculated as a product of all conditional probabilities describing the nodes of each specific pathway. The overall probability of exposure and spread for each type of piggery were obtained by adding the probabilities for each of the exposure and spread pathways, respectively, given these pathways are independent . Each siDifferent definitions on small-scale pig producers can be found. The Australian pig body representative, Australian Pork Limited (APL), defines small landholder as those pig producers with less or equal than 50 sows and/or trading less or equal to 1000 pigs per year . BiosecuFor the purpose of this assessment, small-scale pig producers are those with less than 100 sows with those piggeries with more than 100 sows being defined as commercial enterprises. Moreover, this assessment considers the differences on livestock trade patterns among small-scale pig producers as some livestock movements could pose higher risk for disease transmission. As a consequence, small-scale producers were subsequently classified into two groups; those selling through saleyards and abattoirs, and those selling mainly through informal means. Informal sales included internet, word-of-mouth, family and friends, and local businesses.Data used to populate the exposure and partial consequence assessments were obtained from different data gathering exercises, published literature, and industry statistics. Below is a description of the data gathering exercises to collect information to populate the models used in these assessments.n = 815). The second part of the study involved face-to-face interviews with producers (n = 106) who indicated their willingness to participate during the postal questionnaire, along with volunteers opportunistically recruited from the study saleyards. The interview collected detailed information on demographics, husbandry practices, nutrition, herd health, biosecurity practices, movement practices, animals identification systems, and communication networks (n = 34) were recruited on a voluntary basis from face-to-face interviews and from advertisements placed at the saleyards and in stock agent newsletters. This study included mainly small-scale producers selling through saleyards although there were a small proportion of large-scale producers and small-scale producers selling through informal means.A three-part study involving pig producers at six saleyards situated in eastern Australia was conducted in a 12-month period starting at the end of 2006 n = 15. The snetworks , 42, 43.networks . Focus gn = 29) and face-to-face among participants (n = 24) of pig industry field days.To improve our understanding of practices of small-scale pig producers selling by informal means , a total of 13 small-scale (≤100 sows) pig producers using this marketing strategy were interviewed in New South Wales . This quExposure 1: The FMD-infected meat ends up in a household without pigs and some of this meat is discarded as waste. This waste is then accessible to feral pigs and these pigs become infected with FMD. As the final step of the pathway, the infected feral pigs travel to the index piggery getting in contact with domestic pigs from this piggery.Exposure 2: The FMD-infected meat ends up in the index piggery and some of this meat is discarded as waste. This waste is then fed directly to the domestic pigs in the piggery.Exposure 3: The FMD-infected meat ends up in the index piggery and some of this meat is discarded as waste. This waste is then accessible to feral pigs around the piggery and these pigs become infected with FMD. The feral pigs get in contact with domestic pigs from the same piggery.Exposure 4: The FMD-infected meat ends up in a non-index piggery, and some of this meat is discarded as waste. This waste is then accessible to feral pigs and these pigs become infected with FMD. The infected feral pigs travel to the index piggery getting in contact with domestic pigs from this piggery.This assessment evaluates the probability of exposure of a pig from a piggery to FMD-infected meat that has been illegally introduced into the country. The assessment considers that the FMD-infected meat could end up in any household in the country, with or without pigs. Four different pathways have been identified as potential pathways of exposure of a pig at an index piggery for each of the three piggery types , depending on: (1) the type of household where the meat is destined to; (2) the proportion of waste discarded from this meat; (3) the involvement of feral pigs in the pathway; and (4) the probability of pig producers feeding swill to their pigs. These pathways of exposure are:A scenario tree was developed to represent these four pathways of exposure and the same structure of the scenario tree was used for modeling the risk of FMD exposure among the three groups of pig producers considered in this study. Figure Scenario 1: This scenario represents no spread beyond the index farm, which could occur in different situations: (1) in a piggery with pigs only, when infection is detected and reported; (2) in a piggery with pigs and ruminants, when infection is detected in both species and reported; (3) in a piggery with pigs only, when infection is not detected, but there are no movement of pigs off the farm during the infective period either movements of contaminated fomites. In this last situation, the infection would die out before spread occurs. If ruminants are kept on the farm, these are very likely to become infected before FMD is detected in pigs and moved off the farm (see Scenario 5).Scenario 2: Infection is not detected (in pigs and ruminants) at the first exposed piggery and FMD virus is spread through movement of pigs off farm. This spread could be more or less significant and at local, regional, or national level, depending on the destination of the animals. Within Scenario 2, further scenarios were identified depending on the destination of the animals moving from the index farm when the infection is initially not detected by the farmer. These scenarios slightly differed between small and large-scale piggeries, and are described in Table Scenario 3: Infection in the first exposed piggery is spread through movement of contaminated fomites. Spread through fomites can happen independently of farmer detection. If the farmer does not detect, spread of the virus to other properties through contaminated fomites is more likely than when detection occurs. However, if detection is delayed, spread through fomites can still happen. For this assessment, fomites were defined as mechanical vectors and included vehicles, equipment, and clothing.Scenario 4: Infection in the first exposed piggery is spread through movement of people carrying infective virus particles in the respiratory tract. As the previous scenario, spread through contaminated people is more likely to happen when the farmer does not detect the infection. When detection occurs but it is in late stages of the infection, spread through people could also be possible. Spread through people carrying the virus was considered separate to the spread through fomites as a person could be contaminated despite biosecurity measures being applied to avoid spread through fomites .Scenario 5: Infection is not detected by the farmer (in pigs and ruminants) in the first exposed piggery and FMD virus is spread through movement of ruminants off farm. Even when infection is detected in pigs, movement of infected ruminants off the farm could occur before infection is detected in pigs. This spread could be more or less significant at a local, regional, or national level, depending on the destination of the animals. Information on the movement of ruminants off the farm, such as potential destinations and frequency of movements, was not collected during this assessment. However, information on the presence and number of ruminants kept on the farm was used to evaluate the likelihood of the spread through ruminant movement.Scenario 6: Infection in the first exposed piggery is spread through airborne transmission. Airborne transmission of FMD has been extensively investigated in the past  is exposed to the FMD virus, different potential outbreak scenarios could occur depending on different factors. This partial consequence assessment evaluates the potential outbreak scenarios and their corresponding probabilities occurring. The main factors considered in the consequence assessment are the ability of the farmer to detect the disease, the presence of ruminants on the farm and the movement of animals, fomites, and people from the index farm. In the event that the infection in the index farm is not detected, the virus could spread beyond this property. Six main outbreak scenarios have been identified:Two different consequence scenario trees were developed to represent the previously described potential outbreak scenarios for small and large-scale piggeries. The only difference in the structure of these scenario trees was the potential destinations where pigs from the index farm could go once moved off the farm. Figure Prob_Swill) and the amount of meat illegally introduced into Australia. For investigating which practices had the most influence on the probability of spread of the virus, the following spread scenarios were included in the sensitivity analysis: Scenario 1 (no spread beyond the index farm), Scenario 2 (spread through movement of pigs), Scenario 3 (spread through contaminated fomites), and Scenario 5 (spread through movement of ruminants). The input parameters investigated were the probability of the farmer detecting FMD in pigs (stDetProb_1) and ruminants (stDet_Rum)Prob_1, the presence of ruminants on the farm (Prop_Rum), the probability of FMD transmission through contaminated fomites moving off the index farm (Prob_Fomites), and the probability of movement of pigs (Prob_MovPigs) and ruminants (Prob_MovRum) off the farm.The influence of some input parameters on the model outputs was investigated using the @Risk Advanced Sensitivity Analysis . For the probability of exposure for each piggery type, the input parameters evaluated were the probability of pig producers swill feeding  and the values used for the amount of meat illegally introduced into Australia were 10, 50, 100, and 200 kg. Each of the values for each input parameter was evaluated separately in a simulation of 5,000 iterations, while values for all other input variables were fixed to the base value.The exposure assessment evaluated potential pathways of exposure of a pig from a piggery to FMD-infected meat illegally introduced into Australia and estimated the probability of these pathways to occur. Four potential exposure pathways were identified in this assessment, which were mainly dependent on the type of household in Australia where the contaminated meat was destined to and the characteristics of these households. Description of the exposure pathways, the likelihood of each of these pathways to occur and the overall likelihood of exposure for the three assessments  are presented in Table Exposure 2). The probability of this exposure pathway to occur is extremely low , with one exposure estimated to occur for every 10,000–100,000 times FMD-infected meat is illegally introduced into the country, depending on the type of piggery. The lowest value was estimated for small-scale piggeries selling informally as shown in Table negligible. Exposure 1, which represents the exposure through feral pigs which have been infected from contaminated waste from a household without pigs, has a higher probability to occur than the other two pathways, given most households in Australia do not have pigs (99.9%) and the illegally introduced meat is more likely to be destined to these households than households with pigs. The overall probability of exposure was estimated 8.69 × 10−6, 7.26 × 10−5, and 3.81 × 10−5 (extremely low), for small-scale piggeries selling informally, those selling through saleyards and abattoirs and large-scale piggeries, respectively. The probabilities of exposure among small-scale piggeries selling through saleyards and abattoirs and large-scale piggeries are slightly higher than that for small-scale piggeries selling informally, due to the higher number of producers within the former categories, and the higher potential contact between feral and domestic pigs in these piggeries.Results indicate that the most likely pathway of exposure to FMD-infected meat illegally introduced into Australia among the three types of piggeries is through the direct feeding of the infected meat to the pigs (very low probability (0.003–0.005) was obtained for all piggery types, given most of this meat will be consumed and only a small proportion will be waste that could be fed to the pigs.This assessment considered that households without and with pigs had even probability of illegally introducing meat into the country. When evaluating the probability of exposure of the pigs to the infected meat, once this meat is introduced to the piggery of concern, a Following the exposure of a pig from a piggery, six main scenarios have been identified depending on the ability of the farmer to detect FMD and the movement of animals, fomites, and people from the index farm. These scenarios are the same for the three groups of pig producers.Scenario 1, Scenario 2, Scenario 3, and Scenario 5. Scenario 1, representing no spread of FMD beyond the index farm, has a 0.193 (Low) probability to occur, as the ability of the farmer to detect FMD in pigs is estimated low. The probability of Scenarios 2 and 5 to occur is estimated 0.137 and 0.100, respectively (Low). In these scenarios, the FMD-infected animals at the index farm are not detected and the virus spread from this farm through movement of pigs or ruminants. The extent of the spread and the impact of the consequences will depend on the destination of the animals moving off the farm. The likelihood of Scenario 3 to occur is estimated similar, with a probability of 0.175 (Low). This scenario represents the spread of the FMD virus through the movement of contaminated fomites independently of the farmer detection. The virus could spread to another farm, saleyards, or abattoirs. The low frequency of movement of pigs and ruminants from this type of piggeries is the main reason why there is a low probability of spread through movement of animals (Scenarios 2 and 5) and fomites (Scenario 3).Table Scenario b), with a likelihood of occurring of 0.102 (Low). This scenario is more likely to occur than the rest of scenarios involving movement of pigs off the index farm as the main destination of pigs from a small-scale piggery selling through informal means is another small-scale piggery. The less likely scenarios are those involving spread to saleyards and abattoirs.Within Scenario 2, Table Scenario 2, Scenario 3, and Scenario 5, all with a Moderate likelihood of occurring  was estimated to be 0.175 (Low) as the virus only survives for up to 28 h in the human respiratory tract, these farms do not usually employ external staff to work in the piggery, and over half of these farms have controlled entry of visitors and wash their hands after handling the pigs. However, this estimate is higher than the estimate for small-scale piggeries selling through informal means, as the latter group of producers have better on-farm practices, which could avoid the spread of the virus through contaminated people. Scenario 1 , representing detection of FMD at the index farm and no spread beyond this farm, has a similar estimate than for the previous group of producers. This scenario has the lowest probability of occurring as the probability of the farmer detecting and reporting at these piggeries was estimated lower than for the other groups of producers.Among the five main potential outbreak scenarios from a small-scale piggery selling at saleyards and abattoirs, the most likely scenarios are ng Table . The likScenario j  and k , representing movement of pigs to saleyards, where infection is or is not detected, respectively.Regarding those scenarios involving movement of pigs off the index farm to different destinations, the most likely scenarios to occur in this group of pig producers are Scenario 1 and Scenario 3. Scenario 1, with a probability of 0.317 (Moderate), represents detection of FMD at the index farm and no spread beyond this farm. The likelihood of this scenario in large-scale piggeries is higher than that in both groups of small-scale piggeries, as the ability to detect disease among large-scale producers is estimated higher than that for small-scale producers and there is a lower proportion of large-scale producer with ruminants on the farm, limiting the potential spread off the farm through this species. The likelihood of Scenario 3 to occur was estimated 0.499 (Moderate). This estimate is the same than the estimate for this scenario for small-scale piggeries selling at saleyards and abattoirs. Spread of FMD through movement of pigs (Scenario 2) when the infection has not been detected has a probability of occurring of 0.263 (Low). This probability is higher than that for the same scenario in small-scale piggeries selling through informal means, due to the more frequent movements of pigs, but lower than that estimated for small-scale piggeries selling through saleyards and abattoirs, as the producer in a large-scale piggeries is more likely to detect FMD than that in the small-scale piggery. Similarly, the likelihood of spread through movement of ruminants  is lower than that in small-scale piggeries selling at saleyards and abattoirs but higher than that in small-scale piggeries selling informally.The six main outbreak scenarios are the same than those described for small-scale pig producers; however, scenarios involving movement of pigs off the index farm differed depending on the destination of these animals. Table Scenario 2), the most likely scenarios were those where pigs were sent to the abattoir , as this was the most common destination of pigs from large-scale piggeries.Among the scenarios of spread due to movement of pigs off the index farm  among small-scale pig producers, the input parameter most influential on the output of the model is the presence of ruminants on the farm. A significant proportion of these producers stated having ruminants on the farm. If the proportion of producers with ruminants in the piggery is decreased to only 10% , the likelihood of Scenario 1, increases 35 and 18% in the small-scale producers selling informally and in those selling at saleyards and abattoirs, respectively. For large-scale producers, the probability of the farmer detecting FMD in pigs is the input value with the most influence on the output probability of Scenario 1, with 18% increase in the probability of FMD not spreading form the index farm when detection in pigs is 0.9 . A similar influence of the probability of FMD detection is seen among small-scale producers selling informally. The probability of spread through movement of infective pigs off the farm (Scenario 2) decreases when increasing the probability of the farmer detecting in pigs and ruminants for the three groups of pig producers. The influence of these input variables is more significant among large-scale and small-scale pig producers selling at saleyards and abattoirs (decrease of up to 25%). As expected, movement of pigs off farm influences the probability of Scenario 2 for all piggery types. The probability of spread through movement of contaminated fomites has a significant influence on the occurrence Scenario 3, given this model assume that the virus would spread through fomites independently of the farmer detecting the disease. When Prob_Fomites is reduced to 0.1 , there is a fivefold decrease in the probability of spread through this pathway. These results stress the importance of maintaining appropriate biosecurity practices to minimize the spread of FMD through fomites, such as pig transport and visitor vehicles, clothes and equipment. For Scenario 5 (spread through movement of ruminants), the input value with the most influence on the model output is the probability of movement of ruminants during the infective period. However, the influence is different among the three groups of pig producers as seen in Figure Scenario 5 occurring, especially among small-scale piggeries selling through saleyards and abattoirs, which are those with an estimated lower probability of detection . When detection in these piggeries is improved to 0.89, the probability of Scenario 5 occurring decreases by 13.5-folds.Results of the sensitivity analysis on Several studies have suggested that small-scale pig producers are more likely to introduce and spread emergency animal diseases (EAD) compared to large-scale pig producers , 47, 48,Extremely low for the three groups of piggeries. One of the factors driving this low probability is the fact that this assessment considered households without and with pigs having an even probability of illegally introducing meat into the country and most households in Australia do not keep pigs. As such, the probability of the illegally introduced meat being destined to a household with pigs is extremely low. There is no available data providing scientific evidence about households with pigs being more likely to illegally introduce FMD-infected meat into Australia than other households. However, data on illegal movements of meat products are needed to confirm this assumption. The most likely pathway of exposure according to this assessment is through the direct feeding of the infected meat to the pigs. In addition, the sensitivity analysis indicates that exposure is highly influenced the probability of producers to swill feed pigs. Swill feeding was estimated to be more likely among small-scale producers; however, estimates of swill feeding were incorporated in the model with significant uncertainty as they were based on information on feeding practices of producers collected during questionnaires and interviews . Spread of FMD through contaminated vehicles, equipment or clothing with poor or absent appropriate disinfection has been reported as an important pathway of FMD spread from infected properties . Similarly than for Scenario 3, spread through movement of pigs was less likely to occur among small-scale piggeries selling informally due to the lower number of pigs kept on the farm and frequency of pig movements off the farm. However, although the probability of this scenario of spread was lower than for other piggeries, movement of pigs to non-commercial pathways could jeopardize animal traceability in the event of a disease outbreak. Some of these pig properties are not registered within government and/or industry databases, challenging the ability to trace back animal movements in the event of an EAD outbreak and increasing the potential magnitude of the outbreak. A recent study among 198 small-scale pig producers in Australia reported over 85% of participant producers moving pigs off their property in the last 12 months, ~10% of producers not recording animal movements and 3% not having a legally required property identification code for their property  was more likely to occur in large-scale piggeries as these are considered more likely to detect disease and a lower proportion of these producers keep ruminants  is the most likely pathway of exposure, and the probability of this to occur is slightly higher among small-scale producers. If FMD is to be introduced into a piggery in Australia, spread is more likely to occur if this piggery is a small-scale piggery selling at saleyards and abattoirs with movement of contaminated fomites and movement of pigs and ruminants off the farm the most likely pathways of spread. Presence of ruminants on the farm and the probability of the farmer detecting FMD are the most influential factors for the spread of the virus. Although large-scale piggeries have higher probability of FMD spread than small-scale piggeries selling informally, they are easy to locate, are members of the pig industry body APL and do not use non-commercial venues to market their pigs. These would limit the potential extent of an outbreak. Small-scale piggeries selling informally do not pose a higher likelihood of spread of disease than the other sectors of the industry; however, if spread from these piggeries occurs, non-traceable movement of pigs would increase the magnitude of an outbreak.This study suggests that there is a need for improving engagement with biosecurity and animal health management of pig producers and agricultural shows, saleyards and abattoirs stakeholders understanding producers’ current attitudes and behaviors toward biosecurity and animal health management, especially among small-scale producers, and collaboration among government and industry stakeholders are crucial for the development of effective extension strategies that could lead to practice change in relation to biosecurity and EAD management.MH-J led the study, building and implementing the risk assessment models, and writing the manuscript. NS led the data gathering exercises of this study, participated in the estimation of parameter estimates, and providing input in the writing of the manuscript. PH and J-AT participated in the design of the data gathering exercises, provided input in the scenario tree model building and the estimation of the input parameters, and provided input on the writing of the manuscript. PM provided guidance and ongoing input on the building of the risk assessment models, estimation of input parameters and implementation of the models, as well as the writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer FS and handling editor declared their shared affiliation, and the handling editor states that the process nevertheless met the standards of a fair and objective review.


Correlation Filter (CF) based trackers have demonstrated superior performance to many complex scenes in smart and autonomous systems, but similar object interference is still a challenge. When the target is occluded by a similar object, they not only have similar appearance feature but also are in same surrounding context. Existing CF tracking models only consider the target’s appearance information and its surrounding context, and have insufficient discrimination to address the problem. We propose an approach that integrates interference-target spatial structure (ITSS) constraints into existing CF model to alleviate similar object interference. Our approach manages a dynamic graph of ITSS online, and jointly learns the target appearance model, similar object appearance model and the spatial structure between them to improve the discrimination between the target and a similar object. Experimental results on large benchmark datasets OTB-2013 and OTB-2015 show that the proposed approach achieves state-of-the-art performance. Research interest in visual object tracking comes from the fact that it is widely used in smart and autonomous systems, e.g., anomaly detection, smart video compression, and driver intelligent assistance systems. The main challenges of visual tracking are how the tracker can online adapt to the large appearance variations including occlusion, abrupt motion, deformation, illumination variations, in plane rotation, out of plane rotation, similar object interference, etc.To get a more general adaptive tracker, researchers have proposed many tracking approaches using various visual representations. These approaches, which are mainly focused on the research of the object appearance model, can be divided into two categories: generative models and discriminative models. Generative models ,2,3,4,5 Recently, Correlation Filter (CF) based trackers have gained much attention in visual tracking, and exhibited outstanding performance in both speed and accuracy. These trackers can be performed effectively by employing all circular shifts of the positive sample in the Fourier domain, and overcome the shortcomings of traditional discriminative model using densely-sampled samples. Bolme et al.  introducHowever, similar object interference is still a challenge in visual tracking, and has no corresponding algorithm to solve this problem in CF tracking framework. Most existing tracking approaches, which are used to deal with occlusion problem, have difficulty discriminating the target from a similar object. In ,8,14,19,Based on above discussions, we propose an approach that jointly learns the appearance model of the target, the appearance of similar objects and the spatial structure between them to alleviate the interference from similar objects. In the proposed approach, we train the target and similar objects by their own appearance model. When the target is occluded by a similar object, the occlusion object is matched by its own appearance model, which avoids the appearance model of the target being updated by the similar object.(1)We propose to add ITSS constraint into existing CF tracking model for alleviating similar object interference. The proposed approach jointly learns the target appearance model, similar object appearance model and the spatial structure between the target and similar objects.(2)We propose to introduce interference degree weight into the model, which makes our approach switch between the baseline model and the constraint model (integrating the baseline model and ITSS constraint) dynamically.(3)During the model update, instead of only updating the target appearance model, we collaboratively update the target model and the interference object model. Moreover, we propose combining the target model and the interference model for re-detecting the lost target when the target is almost totally occluded by a similar object.Our main contributions are listed as follows.The rest of this paper is organized as follows. We found that the spatial structure constructed by similar object and target is very useful information that can help to distinguish target from similar objects effectively. When the target and a similar object overlap, we can effectively identify the occlusion relationship between the target and a similar object by their spatial structure. Once the spatial structure is obtained, the model update can be controlled reasonably to alleviate occlusion of similar target. Thus, we propose utilizing online learning interference-target spatial structure constraint for alleviating similar object occlusion.The interference from a similar object is gradually formed when similar objects get closer to the target. Therefore, we assume that there exists a warning area where similar object is considered as the potential interference. The warning area is divided into eight overlapping search regions. Each search region has a partial overlap with the target, but the size of the overlapping area is controlled less than half the size of the target. Similar targets are detected simultaneously by parallel computing in the eight search regions. The spatial layout has several advantages. First, the maximum response of the detector will be not on the target when similar object has attended in the search area. Second, each search area is formulated conveniently by the location and size of the target, and the eight search areas can be proportionally synchronized when the target size changes.Usually, the similar object and target will deform during tracking, and the degree of the deformation is random. To overcome the problem, we consider two types of features, one is sensitive to deformation and the other is insensitive. Inspired by Staple tracker , we inteSpecifically, during coarse detection, we take the location of maximal correlation response as the center of a similar object. The correlation response is obtained by the correlation between each search region and the detector trained by HOG features extracted from the estimated target. The confidence map constructed from the correlation response reflects the structural similarity between the estimated target and the testing sample from the search area. The second stage is called fine detection that gains reliable similar objects and eliminates unreliable ones from potential candidate similar objects. In this stage, the color statistical feature is extracted by a color statistical model trained by current estimated target and surrounding context. For more details of the color statistical model training, we refer to . The difZhang  proposedG = . Node set V is constructed by the target appearance and all similar object appearance. The edges set E include all connection between the target and each similar object. The edges of E can be viewed as springs that constraint between the target and each similar object. The length of the edge indicates the relative distance between the target and each similar object. Meanwhile, the thickness of the edge means the strength of a similar object interferes with the target. Now, our aim is to define a score function for describing the structure of graph G and design an optimization method to obtain the structural parameters when the score function reaches a maximum value.Interference-target Spatial Structure (ITSS) can be viewed as a topological graph figuratively. We define a graph The score function of the configuration j-th similar object in frame j-th similar object in frame j-th similar object and the target. The parameter The variable These weight parameters introduced into Equation (1) have two important functions. (1) They control the importance of the corresponding similar object and reflect the interference degree of the similar object to the target. The greater the weight is, the more serious the disturbance is. (2) These weights also maintain a fixed function structure of Equation (1). We do not need to change the form of Equation (1) when the number of similar targets in the warning area changes.We train the parameter Here, The structure labels The function Here, Herein, the task-loss After the transformation above, the problem is how to optimize Equation (3) for learning parameter lgorithm  to optimThe detection task is to find a configuration with the best score over all possible configurations which maximizes Equation (1) when the model parameter All parameters in Equation (10) have the same physical meaning as in Equation (1). The score of the best configuration from this recursive form corresponds to the max score of Equation (1), e.g., Our model update method is controlled by the occlusion estimation, and the model is updated when there is no occlusion. In our approach, the occlusion estimation is divided into two steps: occlusion detection and occlusion identification.We dynamically maintain a topological graph, which consists of similar objects and the target, by the method proposed in By the Equation (11), we can compute the positional relation of abscissa and ordinate between If bination  of the aWe compare Next, we can update the appearance model of the target and the similar object according to the result of the occlusion detection. We optimize these models by kernel trick and circulant matrix, so our model update formula is defined as Equation (13).t-th frame. The bar—represents complex conjugation.Here, When the target is almost completely occluded by a similar object, the distance between the target and the occlusion object When We activate the re-detection mechanism when the target is lost. In our tracking framework, the spatial structure constraint can alleviate the part occlusion. However, when When Equation (14) is established, we need to activate re-detection strategy to detect the lost target.If Our experimental observations are reported from two aspects: special performance evaluation and comprehensive performance evaluation. In special performance evaluation, we evaluate the proposed tracking algorithm and other six state-of-the-art tracking algorithms on nine challenging sequences including occlusion from a similar object to demonstrate the effectiveness of the proposed approach for alleviating similar object interference. In comprehensive performance evaluation, the goal is to demonstrate the comprehensive performance of the proposed algorithms in various challenges including serious occlusion, noise disturbance, non-rigid shape deformation, out-of-plane object rotation, pose variation, similar object interference, etc.To achieve a latest comparison, we report the results of several recent trackers by adding these trackers to the testing framework of visual tracking benchmark. In experiments, each tracker uses the source code provided by the author. The parameter settings from authors are kept for all the test sequences. All testing sequences are from the OTB-2015.The Staple tracker combines template and histogram scores to alleviate deformation, and significantly outperforms many state-of-the-art trackers in comprehensive performance. However, color histogram weakens structural information while alleviates deformation, which leads to the poor performance of Staple tracker when there exists similar object in the scenes. Thus, we choose the Staple tracking algorithms as the baseline tracker. The new tracker integrates the basic tracker and the proposed ITSS.To fully reflect the merits of our approach, we evaluate the specific performance of our approach from two aspects: qualitative evaluation and quantitative evaluation.(1)Basketball, Girl and Walking2: in the Basketball, Girl and Walking2 sequences, target and occlusion object have similar appearance feature including structure and color. Staple uses color statistical feature to alleviate deformation, so the tracking result is easy to drift to occlusion object when the occlusion object has similar color feature with the target. Although the DAT algorithm also uses color statistical feature to resist deformation, but it does not consider the structural information of the target. It shows a poor performance on this video. Our tracker integrates ITSS to the Staple, which alleviates similar object interference, can resist deformation. In contrast, the Staple tracker does not obtain the spatial structure information of between the similar object and the target, and do not distinguish accurately the target and similar targets. Rest trackers rely too much on structural information and lead to drift when the target undergoes severe deformation.(2)Football and Girl: in the Football sequence and the Girl sequence, the target is almost fully occluded by nearly the similar object. Thus, these trackers relying on structural feature mistakenly estimate the target location to the interference. In this case, our ITSS model is powerless because the structural features that the model relies on disappear. However, our tracker introduces a re-detection strategy which can effectively distinguish the target and nearly similar objects when the target appears again, then reconstruct the interference-target spatial structure. The experiment demonstrates that it is necessary to introduce the re-detection into our tracking framework to alleviate almost full occlusion.(3)Shaking1: the target and interference have similar structure and color in the Shaking1 sequence. Compared to our algorithm, KCF and DSST exhibit varying degrees of drift because they have no ability to resist deformation. The Staple tracker the model, which can resist deformation by color statistical features, have no enough prior information for distinguishing target from similar objects. Our tracker combines effectively advantages of ITSS and Staple. When similarity interference occurs, using ITSS distinguishes the target and the interference. When the interference is removed, our algorithm switches to Staple. Therefore, our tracker can resist similarity interference as well as deformation.(4)Coupon: the Coupon sequence is different from other sequences, where the target occludes the similar object. In addition, the target and the interference are obviously different in structural feature, but similar in color statistical feature in the sequence. Thus, except DAT, all other algorithms track the target robustly. Our tracking model not only gains the color feature of target appearance but also obtains its structure feature; therefore, our tracker gains satisfactory performance on this type of sequence.(5)Liquor: there exist multiple structures similar targets in the Liquor sequence. In addition, the target is occluded repeatedly by different similar objects. Only our approach and Staple keep correct tracking throughout the tracking process. Staple utilizes the color difference between the target and other objects to distinguish them, while our approach uses ITSS constraint to discriminate the target from similar objects.(6)Bolt2 and Deer: These two sequences are different from other sequences. The target is not occluded by a similar object, but the similar object is occluded by it. When the similar object gradually approaches the target, some tracker track the similar target incorrectly, e.g., DSST, while our tracker can use the learned ITSS constraint to correctly differentiate target and interference.Overall, our approach provides promising results compared to the six other trackers on these sequences including similar object interference.B) is a function used to calculate the area of the bounding box B. We compare the Basketball, Skating1, Football, Coupon, Girl, Liquor, Bolt2, Deer and Walking2. In particular, on the sequence Basketball and Skating1, our tracker slightly drifts in the 200th and 230th frames, respectively, due to in plane rotation of the target and deformation of the nearest similar object appear simultaneously.In addition, we provide a quantitative comparison of our tracker and the six trackers on the nine challenging sequences mentioned above. Our quantitative evaluations with fame-by-frame comparison are two metrics: center location errors  on benchmark datasets : OTB-201We present the results of OTB-2013 and OTB-2015 in Comparing In this paper, we propose a generic approach for alleviating similar object interference. The approach integrates the interference-target spatial structure (ITSS) constraint to existing CF tracking algorithm for improving the robustness of the algorithm when the target is severely occluded by a similar object. When a similar object exists around the target, the proposed ITSS constraint is online learning by using a minimum spanning tree model to optimize the structured SVM, which provides an effective strategy to identify the target and similar object when there is occlusion between them. Moreover, when the target is almost completely occluded by a similar object, we combine the target model and the interference model for re-detecting the missing target. The experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.


The tension brought about by sickbeds is a common and intractable issue in public hospitals in China due to the large population. Assigning the order of hospitalization of patients is difficult because of complex patient information such as disease type, emergency degree, and severity. It is critical to rank the patients taking full account of various factors. However, most of the evaluation criteria for hospitalization are qualitative, and the classical ranking method cannot derive the detailed relations between patients based on these criteria. Motivated by this, a comprehensive multiple criteria decision making method named the intuitionistic multiplicative ORESTE  was proposed to handle the problem. The subjective and objective weights of criteria were considered in the proposed method. To do so, first, considering the vagueness of human perceptions towards the alternatives, an intuitionistic multiplicative preference relation model is applied to represent the experts’ preferences over the pairwise alternatives with respect to the predetermined criteria. Then, a correlation coefficient-based weight determining method is developed to derive the objective weights of criteria. This method can overcome the biased results caused by highly-related criteria. Afterwards, we improved the general ranking method, ORESTE, by introducing a new score function which considers both the subjective and objective weights of criteria. An intuitionistic multiplicative ORESTE method was then developed and further highlighted by a case study concerning the patients’ prioritization. Nowadays, with the rapid growth of the world population, finite medical resources make it hard to meet people’s requirements for healthcare. Most countries face the issue of how to rationally and effectively allocate medical resources so that patients can be treated timely. The same situation exists in China. The scales of public hospitals in China are far beyond that of the primary-level medical and health care institutions. Patients’ prioritization of hospitalization can be taken as a Multiple Criteria Decision Making (MCDM) problem, which consists of 1) evaluating the patients’ performances; 2) determining the weights of evaluation criteria; 3) aggregating the evaluation values with respect to the criteria weights and then determining the patients’ orders. Due to the complexity of objective things, usually it is hard to evaluate the utility value of an alternative (criterion) directly. However, giving the preference relation of pairwise alternatives (criteria) is easy to implement. Fuzzy set theory [ evaluati determin aggregattion, Xu  depictedrelation  and the relation . Howevera et al.  employed−9 scale  instead The IMPR model is flexible in expressing the uncertain preference opinions of individuals given that it can reflect the membership, non-membership, and hesitation degree clearly. Because of the effectiveness, IMPR has attracted growing concerns. Researchers studied the operations and comparison method , measureHowever, there are some drawbacks in the exiting MCDM methods with IMPRs. (1) The connection between criteria is ignored in the existing IM-MCDM methods and the highly-related criteria may mislead the results; (2) The uncertain preference opinions of experts on the importance of criteria are translated into fuzzy criteria weights by some weighting techniques, such as the possibility degree-based weighting method ,20 and tThe ORESTE  is a genTo solve the problem of patients’ prioritization for hospitalization, staff in hospital need to consider multiple complex evaluation criteria, and then give their ranking results of patients through a complex analysis process. As usual, traditional methods are not only difficult for evaluating the criteria, such as the value of pathology, clinical features, related risks, and the emergency degree, but also hard for deriving scientific ranking results of patients. Thus, this paper aims to propose a comprehensive method to handle the MCDM problems with intuitionistic multiplicative information, and then apply it to solve the problem concerning patients’ prioritization of hospitalization in HX hospital.We present a correlation coefficient-based weight determining method in the context of IMPR. This method can avoid highly-related criteria misleading the final results by assigning small weights to them.We introduce a new global score function to aggregate the weights of criteria and the alternatives’ performance under each criterion. Both the subjective and objective weights of criteria are considered in this function.We derive threshold values which can be used to determine the PIR relations between alternatives under the IMPR environment.We carry out a case study concerning patients’ prioritization for hospitalization in a public hospital in China by the proposed IM-ORETSE method.There are some limitations with the traditional ORESTE method, i.e., it is limited in considering the subjective and objective weights of criteria simultaneously ,26. BesiThe rest of this paper is summarized as follows: In this section, some basic knowledge of IMPR and the ORESTE method are introduced.Xia et al.  proposedDefinition 1.Let :which assigns to each element  Let X beFor convenience, the pair Let Definition 2.For an IMN and the accuracy function is :If If  For an IQian and Niu  defined Definition 3.Let (1) (2)  Let α1=Step 1. Calculate the global preference score of alternative ee Refs. ,29 for mStep 2. Aggregate the global weak ranking of each alternative by Equation (5)The weak rankings of alternatives are determined by the ascending order of Step 3. Determine the PIR relations between alternatives after the conflict analysis. If the weak rankings of two alternatives are equal or similar, they are not always the indifference relation that they can replace to each other in decision making. Two alternatives may have the same weak rankings but their performances are quite different under some criteria. In this case, if we select one of them, the criteria under which the selected alternative performs well than another will be highlighted. Therefore, it is necessary to distinguish the incomparability relation from the indifference relation between two alternatives. The ORESTE method applies the preference intensities to make the conflict analysis.The average preference intensity of The net preference intensity of If If If If The conflict analyses to construct the PIR structures are as follows:Step 4. Determine the strong ranking by the weak ranking and the PIR structure.As a general outranking method, the ORESTE has some advantages: (1) It is simple to understand and easy to make application in terms of technical parameters which are deduced objectively . (2) TheThis section develops an IM-ORESTE method to solve the MCDM problems with the evaluations expressed as IMNs. We first present an objective weight determining method based on the correlation coefficient, which can avoid the misleading results caused by highly-related criteria. Then, the classical ORESTE method is improved by introducing a new global score function. Finally, the IM-ORESTE method is proposed based on the distance measure between IMNs.A MCDM problem consists of the alternatives There are two types of criteria weights to describe the importance of criteria, including the subjective weights and the objective weights. The subjective weights are determined by experts’ preferences. The relative importance evaluated by experts of pairwise criteria are expressed as IMNs, This part presents an objective weight determining method based on the correlation coefficient of criteria with the IMNs information.Most practical decision-making problems are defined on multiple criteria. The “conflict” is the fundamental concept in MCDM problems in that different criteria should provide independent information on the performance of alternatives . HoweverStep 1. Aggregate the preference values Step 2. Calculate the Pearson correlation coefficient between criteria Step 3. Compute the subjective weights by:There are other kinds of objective weighting methods, such as the standard deviation-based weighting method  and the Example 1.Suppose that we rank three teachersWe can find that criteria The scores We rank the alternatives based on the scores of IMNs. The comprehensive score of each alternative can be calculated by:Rank the teachers based on the correlation coefficient-based weighting methodFrom Equations (3)–(5), we obtain the weights of criteria are Rank the teachers based on the standard deviation-based weighting methodThe dispersion degree-based weighting methods aim to determine the criteria weights based on the variation of evaluations under each criterion. A small weight is assigned when the evaluations under this criterion are close, while a big weight is assigned when the gaps of the evaluations are large. We use a representative method of this type, the standard deviation-based weighting method, to solve Example 1. Standard deviation In the following, we use two methods to calculate the objective weights of criteria and then derive the rankings of the three teachers.In this Example, by Equation (14), we obtain Comparative analyses: From the preference matrices, we can find that This part improves the ORESTE method by introducing the objective criteria weights and extends it to solve the MCDM problems with the evaluation values expressed as IMNs. In IM-ORESTE, both the preferences on criteria evaluated by experts subjectively and the preferences on pairwise alternatives under each criterion are expressed as IMNs. Like the classical ORESTE method, the process of the IM-ORESTE method is divided into two stages.Stage 1. Determine the weak rankingsWe introduce a global preference score function to consider both the subjective and objective weights of criteria. It is hard to assign a crisp weight to a criterion by experts due to the fuzziness of people’s cognition and the complexity of the objects. However, it is easy to compare the importance between two criteria. The IMN is effective in describing the experts’ uncertain and fuzzy preferences over criteria. Suppose that the preference of  in Ref. , we aggrThe utility value of each alternative is determined by aggregating the global scores under all criteria:The weak rankings are determined by the utility values in ascending order.Stage 2. Determine the PIR relationIt is a strict way to determine the relations among alternatives based on the utility values. If The PIR relations between two alternatives should meet the following conditions:(1) When the absolute value of the net preference intensity (2) If (3) If We define Suppose that there are one criterion and two alternatives. For a small dominance, We determine the PIR relations among alternatives based on the comprehensive preference intensity (1) Based on the Pareto optimality theory, if for (2) If (3) For the incomparability relation Based on the above analyses, we present the test rule to establish the PIR relations in the IM-ORESTE method:The procedure of the IM-ORESTE method is given as follows.Algorithm 2(The IM-ORESTE method)Step 1. Experts compare pairwise alternatives based on their performances under each criterion. The evaluations are expressed as IMNs Step 2. Integrate the preference values Step 3. Determine Step 4. Calculate the global score Step 5. Compute the utility values Step 6. Derive the preference intensity Step 7. Determine the value of Step 8. Derive the strong rankings of alternatives considering both the weak rankings and the PIR relations between pairwise alternatives.In this section, a case concerning the patients’ prioritization of admission is presented to illustrate the application of the IM-ORESTE method.HX is a large scale hospital in Chengdu, China. According to statistics, every month there are about 6000 patients waiting for beds for hospitalization in HX hospital. The average waiting time of each patient is about 2 to 3 months, and some patients may even have to wait half a year to be hospitalized. There are some serious patients among them who are in urgent need of hospitalization. HX hospital has about 200 thousand hospitalized patients every year. However, over 30% of them do not need to be hospitalized in HX hospital.To solve this problem, in December 2011, HX hospital set up the Admission Service Center (ASC) to optimize the hospitalization process and promote the effective management of medical resources. Almost all beds are assigned by this center to help patients have a convenient and quick admission. It can assist the hospital to manage the beds efficiently so as to let the patients with serious illness be hospitalized quickly. However, many patients go to HX hospital no matter if they have a serious disease or not. The staff in ASC have to evaluate each patient to determine the severity of the illness and the priority. On this basis, the sequence of patients’ admission can be derived by ASC. In ASC, the evaluation indicators of patients’ prioritization are summarized as: Patients’ prioritization has attracted much attention in hospital management in recent years. For example, Ashour and Kremer  developeIn the past, the ASC selected the patients for hospitalization through artificial screening. However, there are many issues in this selection process, such as the factors of human intervention on beds and the complexity of the multifactor decision-making problem. There are no clear evaluation criteria. It is difficult for the staff in ASC to determine the priority of each patient. Motivated by this, we apply the IM-ORESTE method proposed in this paper to solve the patients’ prioritization in ASC.Suppose that four patients are waiting for beds in ASC, and three experts are invited as a group to take part in the decision process. Note that the experts are selected from the staff in ASC and the doctors in HX hospital. The expert group analyzes all evaluation criteria and then gives their overall preference relation matrix in IMPR according to pairwise comparisons over the criteria, shown as:The preferences of expert group on pairwise alternatives with respect to each criterion are expressed as IMPRs, shown as:Step 1. The relevant evaluation information is given in Step 2. Aggregate the preference values Step 3. Calculate the Pearson correlation coefficient Step 4. Calculate the global score Step 5. The utility values of patients are calculated as Step 6. Calculate Step 7. Predefine that Step 8. Based on the weak ranking and the PIR relations of the alternatives, we can obtain the final ranking of alternatives as In this section, a detailed comparative analysis between the IM-ORESTE method and the existing MCDM methods is presented to illustrate the effectiveness and superiority of our proposed method.Considering that the crisp weights of criteria are the basis of most MCDM methods, we translate the subjective weights evaluated by DMs to crisp weights byThen we apply Equation (22) to integrate the crisp subjective weight and the objective weight calculated from Equation (12) of each criterion into a collective one.By Equation (22), we obtain From  0.6 see . In addiAs we know, both the IM-TOPSIS method and the IM-VIKOR method are based on the overall utility values of alternatives. Neither the IM-TOPSIS method nor the IM-VIKOR method can derive the PIR relations among the alternatives. The IM-TOPSIS method fails to accurately describe the indifference relation between the patients Some theoretical defects exist in the IM-PROMETHEE method. There is indifference relation between For the IM-ORESTE method, it can overcome the defects discussed above. In other words, it is more reasonable than the IM-TOPSIS method, the IM-VIKOR method, and the IM-PROMETHEE method in practice. Specifically, the IM-ORESTE method has the following advantages:(1) Both the objective and subjective weights of criteria are considered in the IM-ORESTE method. In addition, the correlation coefficient-based weight determining method can avoid the effects of correlated criteria on the final result.(2) The PIR relations are derived from the IMPR of alternatives but not from the outranking flows. The preference, indifference, and incomparability relations among the alternatives are determined rationally based on the scientific calculation process.Experts are allowed to give their opinions by pairwise comparisons of alternatives, which are easy to determine. The uncertain evaluations of experts can be fully reflected by the IMSs in terms of the membership degree, non-membership degree, and uncertainty degree.Based on the correlation coefficient-based objective weight determining method, the biased results caused by the highly-related criteria can be avoided in solving MCDM problems.Experts’ preferences on the importance of criteria are also considered. The fuzzy subjective weights of the criteria are not required to translate into crisp weights, which can avoid information loss.A reliable result is ensured by the conflict analysis to distinguish the PIR relations between alternatives.Compared with the primary medical institutions, the public hospitals in China face a huge sickbed crisis. An irrational order of hospitalization often leads to worsening of the disease. In recent years, there has been a dramatic increase of patient dissatisfaction on the admission process in China. A reasonable method is needed to rank a patient for hospitalization based on multiple conflict criteria. This is a typical MCDM problem. This paper proposed the IM-ORESTE method to rank the patients according to seven predefined evaluation criteria. The advantages of this method compared with other existing MCDM methods are summarized as follows:On this basis, we applied the proposed IM-ORESTE method to solve the problem concerning patients’ prioritization of hospitalization in China. The results showed that it can be used to deal with the complex MCDM problem in which the weights of criteria were unknown and both the qualitative and quantitative criteria existed simultaneously.However, there are some limitations in the proposed MCDM method. When applying the proposed method to handle this kind of problem concerning hospitalization, it may cost manual effort since heavy work exists in the process of pairwise comparison between patients. Thus, in the future, we will continue to improve this method via some computer aid techniques to make it timesaving and laborsaving. In addition, we will focus on setting up a systematic criterion to rank the patients for hospitalization. It would be valuable to apply the developed IM-ORESTE method to other medical management problems. Extending the improved ORESTE method to other types of evaluation models ,40 shoul


What, When, and Whether model (www-model) which postulates that the content, the timing and the possibility of generating an action can be partially independent both at the cognitive level and at the level of their neural implementation. The original proposal was based on a limited number of studies, which were reviewed with a discursive approach. To assess whether the model stands in front of the more recently published data, we performed a systematic review of the literature with a meta-analytic method based on a hierarchical clustering (HC) algorithm. We identified 15 PET/fMRI studies well-suited for this quest. HC revealed the existence of a rostro-caudal gradient within the medial prefrontal cortex, with the more anterior regions (the anterior cingulum) involved in more abstract decisions of whether to execute an action and the more posterior ones (the middle cingulum or the SMA) recruited in specifying the content and the timing components of actions. However, in contrast with the original www-model, this dissociation involves also brain regions well outside the median wall of the frontal lobe, in a component specific manner: the supramarginal gyrus for the what component, the pallidum and the thalamus for the when component, the putamen and the insula for the whether component. We then calculated co-activation maps on the three component-specific www clusters of the medial wall of the frontal/limbic lobe: to this end, we used the activation likelihood approach that we applied on the imaging studies on action contained in the BrainMap.org database. This analysis confirmed the main findings of the HC analyses. However, the BrainMap.org data analyses also showed that the aforementioned segregations are generated by paradigms in which subjects act in response to conditional stimuli rather than while driven by their own intentions. We conclude that the available data confirm that the neural underpinnings of intentionality can be fractionated in discrete components that are partially independent. We also suggest that intentionality manifests itself in discrete components through the boosting of general purpose action-related regions specialized for different aspects of action selection and inhibition.In their attempt to define discrete subcomponents of intentionality, Brass and Haggard  proposed Motor control has been the object of interest of many disciplines, including psychology, cognitive neuroscience, and, since the earliest days, philosophy, particularly when the object of enquiry are the conscious aspects of motor control and intentionality , the timing (when component) and the possibility of being executed or inhibited (whether component). However, in reviewing the then available neuroimaging literature, they concluded that there was no sufficient evidence that such components are represented in discrete brain circuits. In their unitary brain model of intentionality, they proposed a “system” for intentional actions located in the pre-frontal cortex, anterior cingulate cortex, and supplementary motor area (SMA), with subcortical inputs coming from the striatum and through the thalamus: in their views, these systems are responsible for the all three features of intentional action www-model was based on a set of experiments that we review here briefly together with more recent observations.Ten years later, Brass and Haggard  re-assesWhat component has been mostly investigated by using fMRI procedures similar to the “Free selection paradigm”  to identify component specific clusters. As the reader will see, the specific literature available is barely sufficient to make statistical inferences on the significance of the clusters identified. However, after the initial hierarchical clustering procedure, we interrogated the vast BrainMap.org database and generated co-activation maps based on the main component-specific medial wall clusters of the frontal lobe. This permitted the desired statistical assessment of the anatomical dissociations initially identified by hierarchical clustering.These were all lingering questions on the Our meta-analysis was based on neuroimaging articles investigating the neurofunctional correlates of intentional action using PET or fMRI in adult subjects.http://www.ncbi.nlm.nih.gov/pubmed/). The search keys were: “Intentional action & fMRI” and “Intentional action & PET.” These queries returned 27 neuroimaging articles investigating the neurofunctional correlates of intentional actions. We included only studies that did satisfy the following inclusion criteria: (1) sample population composed of normal adult subjects; (2) imaging technique: PET or fMRI; (3) data reported using stereotactic coordinates; (4) comparison between intentional actions and stimulus-driven actions. As a consequence of these inclusion criteria, 12 studies were excluded from the analysis. Among the 15 studies that satisfied our inclusion criteria, 6 studies investigated the What component, 3 studies the When component, and 4 studies the Whether component. Finally, 2 studies investigated both the What and the When components. These studies were then classified on the basis of the examined component (see Supplementary Table Candidate studies were selected through the PubMed database (riteria:  sample psimple effects and (ii) interaction effects. The simple effects were: “Intention driven trials” and “Stimulus driven trials.” The interaction effectsFor the suitable studies, in the meta-analysis we used data derived from (i) within group 71 peaks associated with the What component, 42 peaks with the When component and 37 peaks with the Whether component of intentional actions.To summarize, the data selection led us to analyse 150 stereotactic activation loci, For each activation peak, we recorded all relevant information about the statistical comparison that generated it, the nature of the experimental task and the investigated component.– t-values or z-scores;– Sample size;– Average age of the subjects;– Stereotactic template (MNI or Tailarach and Tournoux template);– Whole brain or region-of-interest analysis;– Scanning Technique (PET or fMRI);– Statistical thresholds and nature of the correction for multiple comparisons.We therefore determined a list of classification criteria to characterize each peak of activation included in the dataset:www.brainmap.org).In order to combine the data coming from studies based on different stereotactic spaces, the stereotactic coordinates of studies in which activation peaks were reported in terms of the Talairach and Tournoux  atlas weWe first performed a component specific hierarchical clustering analyses (HC) of the activation peaks: the analysis allowed us to extract the principal clusters of regional effects from the database , and whose root represents one large cluster including all the activation peaks submitted to the algorithm. Each level of the tree reports the clusters created by the algorithm at a specific processing step, as it progresses from individual activation peaks at the lowest level to the all-inclusive final cluster at the top of the tree.” The procedure was continued until the average standard deviation around the cluster centroids of the individual peaks, in the x, y, and z directions, remained below 5.0 mm. This measure roughly mimics the spatial resolution of fMRI studies. As hierarchical clustering may be sensitive to the order in which the individual data are processed, and may generate alternative clustering trees when integers are used  on the one hand we wanted to test the hypothesis that co-activation maps generated starting from the three main clusters of the medial wall of the frontal lobe and of the cingulate cortex can produce similarly segregated results as in the specific hierarchical clustering, once the three maps were formally compared (2) further, we wanted to test the hypothesis on whether the brain regions outside the frontal lobe would co-vary with the original seeds in a similar manner (3) by analyzing the composition of the BrainMap.org data-base experiments, that contributed to the identification of medial-wall clusters, we wanted to learn to what extent these could be associated, even if loosely, to intentional action and its subcomponents at the center of our quest.As an initial step, three separate co-activation maps were calculated for each of the three seed clusters identified by the HC procedure. Subsequently, each co-activation map was compared with the other two lumped together.Furthermore, once a component specific cluster was identified in the BrainMap.org database, we explored its composition as far as the generative paradigms was concerned. We paid particular attention to the relative contribution of go/no-go paradigms as these are closer to aspects of motor control entailed by the intentional/conditional dichotomy, particularly for the whether component.what component the data clustered in the right middle cingulum, the right frontal middle gyrus, the right supramarginal gyrus and the left inferior triangular frontal gyrus .Figure what seed alone) most of the regions implicated by the HC analysis were also identified by the co-activation maps analysis  was used.Each individual map was then compared with a combination of the other two maps: for example, the These analyses led to identification of regions that genuinely dissociate on the basis of their co-activation with the seeds used.what, when, and whether components. Together with these brain regions, there were co-segregations of component-specific regions, particularly for the lenticular nuclei for the when component , suggesting the existence of a rostro-caudal gradient within the medial prefrontal cortex, with the more anterior regions involved in more abstract decisions of whether to execute an action and the more posterior ones recruited in specifying the content and, yet more dorsally, the timing components of actions.what component) the data clustered at the level of the right middle cingulum; the middle cingulum has been previously associated to conflict processing and conflict monitoring , as strange as it might seemphrenologically in terms of minute segregated regions exclusively involved in intentionality; on the other hand, the same observation suggests that the what, when, and whether segregation for action most likely exists beyond the concept of intentionality.In returning to the crucial matter of our contention here, the fact that the medial wall seeds, on which the co-activation maps analyses were based, segregate in an identical manner to what is revealed by the hierarchical clustering performed on “intentional” experiments, suggests that the specificity of intentionality and its subcomponents cannot be sought www action regions at some microscopical level, perhaps thanks to the boosting effect of some ascending modulatory attentional pathways for tasks in which subjects have to decide how and whether to act by themselves, rather than in reaction to a conditional stimulus. Another possibility is that the relative weights of the connections between the regions involved in the different aspects of intentionality change during intentional action, something that needs to be explored with effective connectivity techniques and that goes beyond the potentials of meta-analyses based on data generated by univariate analyses.However, it cannot be denied that the presence of an intentional stance during the paradigms meta-analyzed here induced stronger activity in these regions in a component specific manner. This suggests, on the one hand, that intentionality is expressed in these www-model of intentionality, in order to overcome the methodological limitations of previous attempts. In particular, we are much in need of novel fMRI experiments in which the sub-components of intentionality, postulated by the above mentioned neurocognitive models, are assessed with an uniform procedure in the same group of subjects. The advantages of a uniform procedure are obvious: significant differences between the intentional tasks in the different conditions should not be confounded by factors  that may hamper the possibility of firm assignations of specific functional anatomical patterns to subcomponents of intentionality. Another unexplored issue is the characterization of intentional actions in conditions in which content, timing and the very decision on whether to act or not are explicitly and jointly manipulated. Furthermore, it remains to be discovered how general-purpose action brain regions become more active when intentionality is operating. This will clearly require analytical approaches that go beyond univariate analyses of the fMRI data. Clearly, one such ambitious model should have some predictive value for pathologies in which a disorder of intentionality and its specific components is expected. Brass and Haggard (when component in Parkinson's disease). These specific associations also remain to be demonstrated convincingly. However, in spite of all these unsolved issues, we believe that the available literature contains sufficient evidence to think that the www-model of intentionality might be a useful starting framework for future investigations.After this meta-analytic review, it is clear that further studies are needed to assess the functional anatomical foundations of the  Haggard  speculatLZ, SS, and EP reviewed the data for the meta-analyses, performed the analyses, and drafted the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The advancement of knowledge about control of antibiotic resistance depends on the rigorous evaluation of alternative intervention strategies. The STAR*ICU trial examined the effects of active surveillance and expanded barrier precautions on acquisition of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) in intensive care units. We report a reanalyses of the STAR*ICU trial using a Bayesian transmission modeling framework.The data included admission and discharge times and surveillance test times and results. Markov chain Monte Carlo stochastic integration was used to estimate the transmission rate, importation, false negativity, and clearance separately for MRSA and VRE. The primary outcome was the intervention effect, which when less than (or greater than) zero, indicated a decreased (or increased) transmission rate attributable to the intervention.The transmission rate increased in both arms from pre- to postintervention (by 20% and 26% for MRSA and VRE). The estimated intervention effect was 0.00  for MRSA and 0.05  for VRE. Compared with MRSA, VRE had a higher transmission rate , higher importation probability (0.22 vs 0.17), and a lower clearance rate per colonized patient-day (0.016 vs 0.035).Transmission rates in the 2 treatment arms were statistically indistinguishable from the pre- to postintervention phase, consistent with the original analysis of the STAR*ICU trial. Our statistical framework was able to disentangle transmission from importation and account for imperfect testing. Epidemiological differences between VRE and MRSA were revealed. Antibiotic-resistant pathogens are major causes of morbidity and mortality in healthcare settings such as acute care hospitals. A variety of strategies for control of resistant organisms have been proposed, including antibiotic stewardship, active surveillance, environmental decontamination, and treatment of colonization . Yet, thStudies have typically entailed the comparison of rates of outcomes in groups of patients assigned to different interventions after either a quasiexperimental study design or cluster-randomized trial , 10. TheAnother challenge in the analysis of infection outcomes is the need to deal with the dependence of events. Statistical methods that appropriately account for transmission are better able to distinguish the effects of different kinds of system changes on the spread of resistant organisms in human populations. Compared with the acquisition rate, the transmission rate parameter (transmission rate), defined as the rate of cross-infections per infectious individual is a more direct measure of the effectiveness of an intervention to improve source control via active surveillance. Unlike the acquisition rate, the transmission rate is not subjected to confounding because of importation of individuals who are infectious at the time of admission.Statistical methods based on dynamic transmission models have been implemented previously . Thus, eIn this paper, we present the application of a dynamic transmission model to analyze the results of a cluster-randomized trial of active surveillance for control of MRSA and VRE . The STAOur rationale for reanalyzing the STAR*ICU trial was to generate deeper insights about variation in control of transmission across participating ICUs. The methods that we present here yielded an estimate of the transmission rate, embedded within a hierarchical modeling framework, extending recently published work . Our appWe performed a retrospective analysis of data originally collected as part of the STAR*ICU trial. The data were collected during the period from April 2005 to August 2006 and included 20 945 patient-admissions admitted into one of the 18 participating ICUs. Nasal and perianal surveillance swabs were collected at the time of admission to the ICU, weekly thereafter, and on discharge from the ICU. Surveillance swabs were not collected for short-stay patients (ICU stay <3 days) in the original STAR*ICU trial, except for a random sample, which was used to estimate admission prevalence in the original study. However, swabs were collected from all long-stay patients (ICU stay ≥3 days), resulting in approximately 60% of all admissions to the ICU having at least 1 swab for MRSA and VRE. The observed data that we used in the transmission model were ICU identifier, ICU study-arm (control vs intervention), patient identifier, admission and discharge times, and surveillance culture times and results.The transmission model incorporated 2 main components, a within-ICU level component, which was nested in a between-ICU level component. The within-ICU component modeled patient movement into and out of the ICUs, colonization and clearance within the ICUs, and incorporated surveillance test data to inform the transmission dynamics. The between-ICU component was a model that specified the variation of model parameters across the ICUs.A key feature of the between-ICU component of the transmission model was the stipulation of a hierarchical model for the pre- and postintervention transmission rates. Under the hierarchical model, the transmission rates were assumed to vary across the ICUs according to a common probability distribution in which the mean log-transformed postintervention transmission rate depended on the ICU’s assigned treatment. In particular, ICU-specific transmission rates were not modeled as fixed parameters, but they were modeled as log-normal random variables having a common mean and covariance with ICUs in the same study arm. The use of a hierarchical model for the transmission rates allowed us to characterize variation in transmission across ICUs and improved precision of ICU-specific transmission rate estimates via statistical shrinkage towards the overall mean log transmission rate across the ICUs. The hierarchical framework expressed statistical inferences for the treatment effect relative to the variation in transmission rates across the study ICUs. In this way, the Bayesian credible interval for the treatment effect on the transmission rate applied to the broader population of ICUs, and it extended inferences beyond the specific set of ICUs included in the study.The models for MRSA and VRE were implemented independently within the Bayesian modeling framework, and parameter estimation was based on Markov chain Monte Carlo (MCMC) methods. In the next 2 sections, we give an overview of the transmission model and its assumptions. For additional details on the modeling assumptions and formulas, see the Supplementary Material.A schematic illustrating the within-ICU model is provided in Culture sensitivity parameters for MRSA and VRE were assumed to be the same across all 18 ICUs; part of the rationale for this simplifying assumption was that all surveillance cultures were sent to a common laboratory. All 18 ICUs were assumed to have a common mean transmission rate during the preintervention period and a potentially different mean transmission rate during the postintervention period, modeled independent of the intervention. The reason we included both pre- and postintervention mean transmission rates was to control for temporal trends in transmission rates across all ICUs. The estimated intervention effect parameter was modeled as an additive effect on the mean log-transmission rate in the intervention ICUs during the intervention period, thus the exponentiated intervention effect parameter can be interpreted as the relative change in the transmission rate. An estimated intervention effect parameter greater than zero denotes an increase in mean transmission rate, and an intervention effect parameter less than zero indicates a decrease in the transmission rate in the intervention ICUs during the intervention period compared with the control ICUs.Estimation within each iteration of the MCMC consisted of generating a new sample of both the augmented data and the parameters, using either Gibbs sampling or Metropolis-Hastings sampling. Given the observed data and the current parameter values, new augmented data (or patient histories) consistent with the observed data and parameter values were proposed, and accepted, with probability that depends on the relative likelihood of the models with the proposed and current augmented data. If the proposed augmented data was not accepted, the current augmented data remained as the subsequent augmented data sample until the next iteration through the MCMC. Given the new augmented data, parameter values were proposed based on the observed data and the new augmented data. The process of updating the augmented data and parameter values was iterated, and resulted in a collection of parameter values, having a distribution consistent with the likelihood, conditioned on all observed and unobserved data. This collection of parameter values, known as the posterior distribution for the parameters, formed the basis for the point estimates and credible intervals. The implementation of this model was done in C++ and was run using an ASUS Ultrabook laptop. The posterior distributions were based on 50 000 samples with a burn-in of 10 000 samples, and these took approximately 2.7 hours to run. Additional details on the implementation of the MCMC estimation together with the code are included in the Supplementary Material.We reported both posterior means and 95% credible intervals (CIs) for the parameters. The estimated intervention effect parameter with its CI served as the statistical test of the effect of assignment to the intervention arm in comparison to the control arm. In addition, we estimated the preintervention and postintervention mean transmission rates, examined variation in ICU-specific estimates of the transmission rate (pre- and postintervention), importation probability, and clearance rate, and estimated the surveillance culture false-negative probability. For importation and clearance, median and range of estimates across all ICUs were calculated.The overall mean transmission rate  rose from the pre- to the postintervention periods, from 0.0039  to 0.0046  for MRSA, representing a 20% increase. For VRE, mean transmission increased from 0.0069  to 0.0087 , corresponding with a 26% rise . The estThe ICU-specific estimates of the transmission rate demonstrated modest correlations with the acquisition rate estimates obtained in the analysis of the STAR*ICU trial . For MRSOur estimates of importation demonstrated wide variability across the 18 ICUs, similar to the variability in estimates for admission prevalence in the original study. The median estimate of MRSA importation was 0.17 (0.12–0.33), whereas the median estimate of VRE importation was 0.22 (0.12–0.42) . The medOur results support the conclusion of the primary analysis of the STAR*ICU trial , which iOur results demonstrated that the acquisition rate was a relatively poor predictor of the ICU-specific transmission rate. Stated differently, the acquisition rate varied substantially for any given estimate of the transmission rate. A number of factors likely contributed to this variability. The calculation of the acquisition rate is based on the count of the number of instances where an individual converts from a negative baseline test to a positive follow-up test. One of the problems with this determination is that it ignores situations where either the baseline test or follow-up test is falsely negative. Another limitation of the acquisition rate is that it fails to account for dependency between the number of infectious individuals present in a population and the force of infection, which is the rate of new infections (or colonizations) in susceptible members of the population.By definition, the transmission rate offers a more precise assessment of the effect of an intervention to reduce cross-infection than more commonly used statistical measures. The distinctive dependence of infection events on the status of other members of the population renders conventional approaches to estimation of causal effects less valid . The masEstimates of importation were consistently higher than those of admission prevalence , reflectThe STAR*ICU trial was a cluster-randomized trial, so by design it did not need to explicitly account for patient-level or facility-level covariates. Consequently, our model excluded patient-level and ICU-level covariates to more directly contrast the results of our analysis with the results from the original study . HoweverIn addition, this modeling framework can be used to explore, through virtual experiments, how the impact of interventions might vary under hypothetical intervention scenarios. Knowledge gained by such exploration could enhance the design of future intervention studies by providing an improved and more subtle understanding about what factors contribute to a successful intervention.We developed a dynamic transmission model that can be used for studying the impact of infection control interventions in settings where surveillance cultures are collected. The results of our analysis of the STAR*ICU trial data suggest no reduction of transmission for either MRSA or VRE due to the intervention, consistent with the original study. In addition, we found a broad range of estimates for importation and clearance rates, and estimates of the false-negative probability for surveillance cultures were consistent with other studies.Open Forum Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at ofw247_suppl_khadermanuscriptofid_supplemental_materialClick here for additional data file.ofw247_suppl_supplementaryfilesClick here for additional data file.