de.bund.bfr.knime.fsklab.metadata.model.tests/files/metadata.json
{
"modelType": "genericModel",
"generalInformation": {
"name": "Attributing Salmonellosis to food sources and water in Latin America and the Caribbean using data from outbreak investigations (Salmonella)",
"source": "PUBLISHED SCIENTIFIC STUDIES",
"identifier": "PiresOutbLA2011-Salmonella",
"author": [
{
"familyName": "Nauta",
"givenName": "Maarten",
"email": "maana@food.dtu.dk"
}
],
"creator": [
{
"givenName": "Stylianos",
"email": "sgeo@food.dtu.dk",
"country": "Denmark",
"organization": "DTU"
}
],
"creationDate": [
2019,
6,
25
],
"rights": "Other",
"availability": "true",
"format": "",
"reference": [
{
"isReferenceDescription": true,
"publicationType": "JOUR",
"date": "2002",
"doi": "https://doi.org/10.1016/j.ijfoodmicro.2011.04.018",
"authorList": "Pires, S.M., Vieira, A.R., Perez, E., Wong, D.L.F., & Hald, T.",
"title": "Attributing human foodborne illness to food sources and water in Latin America and the Caribbean using data from outbreak investigations",
"volume": "0",
"issue": "0",
"status": "Published",
"website": "https://www.sciencedirect.com/science/article/pii/S0168160511002418",
"abstract": "Foodborne pathogens are responsible for an increasing burden of disease worldwide. Knowledge on the contribution of different food sources and water for disease is essential to prioritize food safety interventions and implement appropriate control measures. Source attribution using outbreak data utilizes readily available data from outbreak surveillance to estimate the contribution of different sources to human disease. We developed a probabilistic model based on outbreak data that attributes human foodborne disease by various bacterial pathogens to sources in Latin America and the Caribbean (LA&C). Foods implicated in outbreaks were classified by their ingredients as simple foods (i.e. belonging to one single food category), or complex foods (i.e. belonging to multiple food categories). For each agent, the data from simple-food outbreaks were summarized, and the proportion of outbreaks caused by each category was used to define the probability that an outbreak was caused by a source. For the calculation of the number of outbreaks attributed to each source, simple-food outbreaks were attributed to the single food category in question, and complex-food outbreaks were partitioned to each category proportionally to the estimated probability. We analysed all bacterial pathogens together, focused on important bacterial pathogens separately, and, when data were sufficient, performed analyses by country, decade and location. Between 1993 and 2010, 6313 bacterial outbreaks were reported by 20 countries. In general, the most important sources of bacterial disease were meat, dairy roducts, water and vegetables in the 1990s, and eggs, vegetables, and grains and beans in the 2000s. We observed fluctuations of the most important sources of disease for each pathogen between decades and countries, whichmay be a consequence of changes in the control of zoonotic disease over the years, of changes in food consumption habits, or of changes in public health focus and availability of data of different pathogens. This study identified data gaps in the region and highlighted the importance of effective surveillance systems to identify sources of disease. Still, the application of this method for source attribution in the LA&C region was successful, and we concluded that this approach can be used to attribute disease to food sources and water in other regions, including developing regions with limited data on the public health impact of foodborne diseases."
}
],
"language": "English",
"software": "R",
"languageWrittenIn": "R",
"modelCategory": {
"modelClass": "Process model",
"modelSubClass": [],
"modelClassComment": "Source attribution class - Outbreaks subclass",
"basicProcess": [
null
]
},
"status": "Uncurated",
"objective": "The objectives of the model is to estimate (i) the proportion of Salmonellosis attributed to different sources by decade, and (ii) the probability that an outbreak was caused be each source.",
"description": "Source attribution using outbreak data utilizes readily available data from outbreak surveillance to estimate the contribution of different sources to human disease. A probabilistic model was developed based on outbreak data that attributes Salmonellosis to sources in Latin America and the Caribbean (LA&C). Foods implicated in outbreaks were classified by their ingredients as simple foods (i.e. belonging to one single food category), or complex foods (i.e. belonging to multiple food categories). For each agent, the data from simple-food outbreaks were summarized, and the proportion of outbreaks caused by each category was used to define the probability that an outbreak was caused by a source. For the calculation of the number of outbreaks attributed to each source, simple-food outbreaks were attributed to the single food category in question, and complex-food outbreaks were partitioned to each category proportionally to the estimated probability. We analysed Salmonella and performed analyses by decade."
},
"scope": {
"product": [
{
"name": "Eggs Chicken",
"description": "Eggs",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Dairy products Others",
"description": "Diairy",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Dairy products Goat",
"description": "Goat milk",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Meat, preparations of meat, offals, blood, animal fats; fresh, chilled or frozen, salted, in brine, dried or smoked or processed as flours or meals; other processed products such as sausages and food preparations based on these",
"description": "Meat",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Poultry — chicken, geese, duck, turkey and Guinea fowl — ostrich, pigeon Others",
"description": "Poultry",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Poultry — chicken, geese, duck, turkey and Guinea fowl — ostrich, pigeon Others",
"description": "Chicken",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Poultry — chicken, geese, duck, turkey and Guinea fowl — ostrich, pigeon Others",
"description": "Duck",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Poultry — chicken, geese, duck, turkey and Guinea fowl — ostrich, pigeon Others",
"description": "Turkey",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Bovine",
"description": "Beef",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Swine",
"description": "Pork",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Meat, preparations of meat, offals, blood, animal fats; fresh, chilled or frozen, salted, in brine, dried or smoked or processed as flours or meals; other processed products such as sausages and food preparations based on these",
"description": "Lamb",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
},
{
"name": "Meat, preparations of meat, offals, blood, animal fats; fresh, chilled or frozen, salted, in brine, dried or smoked or processed as flours or meals; other processed products such as sausages and food preparations based on these",
"description": "Mutton",
"unit": "[]",
"method": [
null
],
"packaging": [
null
],
"treatment": [
null
]
}
],
"hazard": [
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
},
{
"type": "Microorganisms",
"name": "Salmonella",
"adverseEffect": "Salmonellosis"
}
],
"generalComment": "",
"temporalInformation": "1993-2010"
},
"dataBackground": {
"study": {
"title": "Attributing human foodborne illness to food sources and water in Latin America and the Caribbean using data from outbreak investigations"
}
},
"modelMath": {
"parameter": [
{
"id": "niter",
"classification": "INPUT",
"name": "Number of Gibbs iterations per chain",
"unit": "[]",
"unitCategory": "Dimensionless Quantity",
"dataType": "INTEGER",
"value": "20"
},
{
"id": "nchains",
"classification": "INPUT",
"name": "Number of Gibbs chains",
"unit": "[]",
"unitCategory": "Dimensionless Quantity",
"dataType": "INTEGER",
"value": "5"
},
{
"id": "burnin",
"classification": "INPUT",
"name": "Number of burn-in iterations",
"unit": "[]",
"unitCategory": "Dimensionless Quantity",
"dataType": "INTEGER",
"value": "0"
},
{
"id": "data",
"classification": "INPUT",
"name": "Outbreaks data",
"unit": "[]",
"unitCategory": "Dimensionless Quantity",
"dataType": "FILE",
"value": "read.csv(\"Salmonella-all.csv\", header=TRUE, as.is=TRUE, dec=\",\", sep=\";\")"
},
{
"id": "nsources",
"classification": "INPUT",
"name": "Number of product sources",
"unit": "[]",
"unitCategory": "Dimensionless Quantity",
"dataType": "INTEGER",
"value": "19"
},
{
"id": "PriorSinit",
"classification": "INPUT",
"name": "Initial values of the PriorS",
"unit": "[]",
"unitCategory": "Dimensionless Quantity",
"dataType": "VECTOROFNUMBERS",
"value": "c(0.1, 0.01, 0.2, 0.05, 0.3)"
},
{
"id": "nperiods",
"classification": "OUTPUT",
"name": "Number of time periods (decades/years/etc)",
"unit": "[]",
"unitCategory": "Dimensionless Quantity",
"dataType": "INTEGER"
},
{
"id": "P",
"classification": "OUTPUT",
"name": "Probability that an outbreak was caused by a specific source (mean, sd, 2.5th percentile, median and 97.5th percentile of all simulated values)",
"unit": "[Probability]",
"unitCategory": "Dimensionless Quantity",
"dataType": "VECTOROFNUMBERS"
},
{
"id": "Attribperiods",
"classification": "OUTPUT",
"name": "Proportion of disease attributed to each source per time period (mean, sd, 2.5th percentile, median and 97.5th percentile of all simulated values)",
"unit": "%",
"unitCategory": "Arbitrary Fraction",
"dataType": "MATRIXOFNUMBERS"
},
{
"id": "Unknownperiods",
"classification": "OUTPUT",
"name": "Proportions of outbreaks with unknown source (mean)",
"unit": "%",
"unitCategory": "Arbitrary Fraction",
"dataType": "VECTOROFNUMBERS"
}
],
"qualityMeasures": [
{
"sse": 0.0,
"mse": 0.0,
"rmse": 0.0,
"aic": 0.0,
"bic": 0.0
}
]
}
}