Digitized monitoring of pathogen loading in livestock

Abstract

The present invention relates to a computer-implemented method for monitoring pathogen loading in livestock, using a risk matrix for assessing the risk of pathogen loading causing a disease in livestock, a computer program product comprising a code portion suitable for performing the method according to the invention, and a system for monitoring pathogen loading in livestock.

Description

Technical Field

[0001] The present invention relates to a computer-implemented method for monitoring pathogen load in livestock, the method using a risk matrix for assessing the risk of disease caused by pathogen load in livestock, a computer program product comprising code portions adapted to perform the method according to the invention, and a system for monitoring pathogen load in livestock. Background Art

[0002] Infectious diseases of farm animals are one of the major threats to agriculture and can cause considerable damage at the local, regional, and even international level in both industrialized and developing countries. During the last two centuries, considerable efforts have been devoted to understanding the causes and pathogenesis of viral and bacterial diseases of domestic animals. These studies have introduced new methods for the diagnosis, treatment, and control of veterinary diseases. Importantly, the study of veterinary pathogens has also had a major impact in the understanding of basic biological processes of viruses and bacteria. In some cases, the study of veterinary pathogens has revolutionized biology and established entirely new disciplines.

[0003] The production cycle of livestock is usually relatively short, especially for poultry. Therefore, it only provides farmers with a limited time frame opportunity to intervene effectively, especially when the risk of infectious diseases is significant. Therefore, infectious diseases can have a serious impact on the production cycle of livestock. Therefore, firstly, it requires reliable and rapid procedures to detect disease-causing pathogens in livestock production, which provide farmers with sufficient time frame opportunity to intervene effectively.

[0004] However, current best practices for detecting and / or monitoring disease-causing pathogens in livestock production involve several independent processes with little or no connectivity and resulting in times far beyond the intervention window. This practice limits the effective control of the entire process and creates several bottlenecks for process standardization. Other approaches, such as the use of sensors, cameras, and artificial intelligence-assisted sound tracking devices, have also been reported as feasible methods for monitoring livestock production processes. However, these methods are in principle limited to the detection, estimation, and tracking of physical parameters that may not be related to pathogen load and / or disease occurrence on livestock farms. Summary of the invention

[0005] Therefore, there remains a need for a method to monitor pathogen loads in livestock that provides farmers with an opportunity to intervene effectively in an adequate time frame.

[0006] It was found that a method for monitoring the pathogen load in livestock to find out the probability of the pathogen causing disease could solve this problem. In the method, data indicating the identity and load of one or more pathogens in livestock are evaluated by a risk matrix, the risk matrix comprising, for each pathogen, a pathogen detection limit value, a pathogen-caused disease occurrence lower limit value, and a pathogen-caused disease occurrence threshold value, the risk matrix allowing monitoring the pathogen load in livestock. Thus, the risk matrix allows assessing the risk of disease occurrence caused by a specific pathogen.

[0007] Risk matrix for assessing the risk of disease from pathogen load in livestock,

[0008] For each pathogen, the detection limit v of the pathogen is included D , the lower limit of the occurrence of diseases caused by pathogens v L , and the threshold v for the occurrence of disease caused by the pathogen T ,in

[0009] -v L It is n×v D , where n is a real number greater than 1, where the value of n is given by the probability that the pathogen in question causes an infectious disease,

[0010] And among them

[0011] - The pathogen load value does not exceed v D indicates that the risk of an infectious disease caused by the pathogen in question is unlikely,

[0012] - The pathogen load value is greater than v D and not greater than v L indicates that the risk of an infectious disease caused by the pathogen in question is uncommon,

[0013] - The pathogen load value is greater than v L and not greater than v T indicates that the risk of infectious disease caused by the pathogen in question is likely, and

[0014] - The pathogen load value is greater than v T Indicates that the risk of infectious disease caused by the pathogen in question is established.

[0015] Therefore, one object of the present invention is a computer-implemented method for monitoring pathogen load in livestock, comprising the following steps:

[0016] a) receiving a request from an input / output device of a user to connect to an operating unit, wherein the request comprises input of at least a user identification,

[0017] b) The user request of step a) starts the operating unit to perform at least the following steps:

[0018] c) receiving or retrieving data from the input / output device of step a), wherein the data comprises at least

[0019] C1) Data indicating the identity and load of one or more pathogens in livestock pat , where the identity and load of the pathogen are contained in data D pat middle,

[0020] C2) Identification of the farm and / or farmhouse where the livestock of C1) is located farm ,and

[0021] C3) generates the time point T of the data of C1) data ,

[0022] c') the identity and / or load of the pathogen is not yet included in the data D pat In the case of pat the identity and / or load of one or more pathogens described in

[0023] d) evaluating the data from C1) and / or (if applicable) c'), comprising the following steps:

[0024] d1) extract the risk matrix from the database,

[0025] The risk matrix includes, for each pathogen, the detection limit value v of the pathogen D , the lower limit of the occurrence of diseases caused by pathogens v L , and the threshold v for the occurrence of disease caused by the pathogen T ,in

[0026] -v L It is n×v D , where n is a real number greater than 1, where the value of n is given by the probability that the pathogen in question causes an infectious disease,

[0027] And among them

[0028] - The pathogen load value does not exceed v D indicates that the risk of an infectious disease caused by the pathogen in question is unlikely,

[0029] - The pathogen load value is greater than v D and not greater than v L indicates that the risk of an infectious disease caused by the pathogen in question is uncommon,

[0030] - The pathogen load value is greater than v Land not greater than v T indicates that the risk of infectious disease caused by the pathogen in question is likely, and

[0031] - The pathogen load value is greater than v T Indicates that the risk of infectious disease caused by the pathogen in question is established;

[0032] d2) For data D pat For each pathogen in step c') and / or whose identity is determined in step c'), a value v is read from the risk matrix in step d1). D 、v L and v T ,and

[0033] d3) For each pathogen, data D indicating the pathogen load pat or the pathogen load determined in step c') and the value v in step d2) D 、v L and v T The comparison is performed to find the likelihood that any pathogen in C1) or identified in c') will cause disease.

[0034] Detection limit v D Depending on the selectivity of the kit and / or the method used to determine the identity and load of the pathogen in question. In addition, individual conditions on a farm or farms may have different effects on the sensitivity of the kit and / or the method used to determine the identity and load of the pathogen in question. This also has an impact on the detection limit and its value v D Therefore, users of the risk matrix and method according to the present invention can appropriately select n to allow for the value v L and v T Therefore, n is not subject to any restrictions on its specific value. In any case, n is a real number greater than 1, preferably n is a real number greater than 1 to 10. Preferably, the value of n is given by the probability that the pathogen causes an infectious disease. Some pathogens are more likely to cause infectious diseases than other pathogens. Therefore, for those pathogens with a greater probability of causing infectious diseases, the value of n is lower, while for those pathogens with a greater probability of causing infectious diseases, the value of n is higher.

[0035] The lower limit of the occurrence of diseases caused by pathogens v L Indicates the lower limit of pathogen load at which disease caused by the pathogen is typically not observed.

[0036] The threshold for the occurrence of disease caused by a pathogen v T Represents the threshold pathogen load at which disease caused by the pathogen is typically observed.

[0037] These valuesL and v T Depends on the individual pathogen in question. Some pathogens may already cause disease in livestock at relatively low pathogen loads, while other pathogens may only cause disease in livestock at relatively high pathogen loads.

[0038] The request of step a) may further include the identification of the farm and / or farmhouse of the livestock. farm Input.

[0039] In the context of the present invention, the term livestock is used as known to those skilled in the art and refers to domesticated animals raised in an agricultural environment to provide labor and produce commodities such as meat, eggs, milk, fur, leather and wool. Therefore, in the context of the present invention, the term livestock with respect to the risk matrix and the method according to the present invention is not subject to any restrictions regarding a specific type of animal. On the contrary, the term encompasses all domesticated animals, such as cattle, sheep, goats, pigs, horses and poultry, raised in an agricultural environment to provide labor and produce commodities such as meat, eggs, milk, fur, leather and wool. Preferred poultry are chickens and turkeys.

[0040] In one embodiment of the invention, the livestock is one or more of cattle, sheep, goats, pigs, horses and poultry.

[0041] The method according to the invention is neither limited to any particular domesticated animal or species nor to any particular pathogen or pathogen load. Instead, the risk matrix used in the method according to the invention may contain, for each pathogen causing disease in livestock such as cattle, sheep, goats, pigs, horses and poultry, a pathogen detection limit value v D , the lower limit of disease occurrence caused by pathogens v L , and the threshold v for the occurrence of disease caused by the pathogen T Alternatively, the risk matrix may include, for each pathogen that causes disease in one of the specific livestock species, ie, cattle, sheep, goats, pigs, horses, and poultry, a pathogen detection limit value v D , the lower limit of disease occurrence caused by pathogens v L , and the threshold v for the occurrence of disease caused by the pathogen T In step d1), the risk matrix for all domesticated animals or for the specific livestock in question is extracted from a database.

[0042] In the context of the present invention, data D pat The term pathogen in the data D is used to indicate the identity of the pathogen and the load of the pathogen contained in the data D pat middle.

[0043] Alternatively, in the data D patIn the case where the data D does not contain the identification of the pathogen, the method further comprises step c') determining pat The identity and / or load of one or more pathogens in the sample.

[0044] In the context of the present invention, the term pathogen is used as known to those skilled in the art and refers to any organism that can cause disease. The term pathogen may also be referred to as an infectious agent, or simply as a germ. Roughly speaking, there are five main types of disease-causing organisms: viruses, bacteria, fungi, protozoa and helminths. Pathogens may first cause disease in livestock and / or humans who consume any animal-derived food that is contaminated with the disease-causing pathogen.

[0045] Five major types of pathogens and their examples are further disclosed:

[0046] Bacteria are ubiquitous in nature; many species perform functions that are essential or beneficial to animal or human life, while others cause disease. Bacteria are single-celled organisms that lack an organized nucleus and do not contain chlorophyll. They can have a variety of shapes: spherical (cocci), rod-shaped (bacilli), comma-shaped (vibrio), spiral (spirilla), or helical (spirillum), and can be 0.5 to 5.0 μm in size. Some bacteria live in soil, plants, or water; others are parasites of humans, animals, and plants. In general, bacteria can be divided into three groups based on their need for oxygen. Aerobic bacteria thrive in the presence of oxygen and require oxygen for continued growth and survival. Anaerobic bacteria thrive in an anaerobic environment. Facultative anaerobes can survive in either environment, although they prefer the presence of oxygen.

[0047] Important pathogens include:

[0048] The genus Campylobacter (meaning "campy bacteria") is a genus of Gram-negative bacteria. Some Campylobacter species can infect humans, sometimes causing campylobacteriosis, a diarrheal disease in humans. The most known source of Campylobacter is poultry, but due to their different natural sources, Campylobacter species can also be transmitted through water. Other known sources of Campylobacter infection include food, such as unpasteurized milk and contaminated fresh products. Sometimes the source of infection can be in direct contact with infected animals, which usually carry Campylobacter asymptomatically. At least dozens of Campylobacter species are associated with human disease, with Campylobacter jejuni (80 to 90%) and Escherichia coli (C. coli) (5 to 10%) being the most common. Campylobacter jejuni is considered one of the main causes of bacterial foodborne illness in many developed countries. It is the number one cause of bacterial gastroenteritis in Europe, with more than 246,000 cases confirmed each year. Campylobacter fetus (C. fetus) can cause spontaneous abortion in cattle and sheep and is an opportunistic pathogen in humans.

[0049] Salmonella is a genus of rod-shaped (bacillus) Gram-negative bacteria of the family Enterobacteriaceae. It causes salmonellosis, a classic food-borne illness. In humans, the most common symptoms are diarrhea, fever, abdominal cramps, and vomiting. Symptoms usually occur between 12 hours and 36 hours after exposure and last for two to seven days. Sometimes more significant illness can lead to dehydration. The elderly, the young, and others with weakened immune systems are more likely to develop severe illness. Specific types of Salmonella can cause typhoid or paratyphoid fever. Salmonellosis is one of the most common causes of diarrhea worldwide. In 2015, 90,300 deaths were caused by non-typhoidal salmonellosis and 178,000 deaths were caused by typhoidal salmonellosis. In the United States, there are approximately 1.35 million cases and 450 deaths from non-typhoidal salmonellosis each year. In Europe, it is the second most common foodborne illness after campylobacteriosis.

[0050] Clostridium perfringens (formerly known as C. welchii, or Bacillus welchii) is a Gram-positive, rod-shaped, anaerobic, spore-forming pathogenic bacterium of the genus Clostridium. C. perfringens persists in nature and can be found as a normal component of decaying plants, marine sediments, the intestinal tract of humans and other vertebrates, insects, and soil. Clostridium perfringens is one of the most common causes of food poisoning in the United States, along with norovirus, Salmonella, Campylobacter, and Staphylococcus aureus. Infections caused by Staphylococcus aureus show evidence of tissue necrosis, bacteremia, emphysematous cholecystitis, and gas gangrene (also known as clostridial myonecrosis).

[0051] Escherichia coli is a widely used Gram-negative bacillus. Some non-pathogenic strains are part of the normal intestinal flora of humans. However, there are some serologically distinguishable strains that cause intestinal diseases in humans. In addition to the enterohemorrhagic Escherichia coli (EHEC) first described in 1977, there are other pathogenic Escherichia coli (E. coli): enteropathogenic E. coli (EPEC), enterotoxin-forming E. coli (ETEC), and enteroinvasive E. coli (EIEC), enteroaggregative E. coli (EAEC), and diffusely adherent E. coli (DAEC).

[0052] Enterohemorrhagic Escherichia coli (EHEC) are pathogenic strains of the intestinal bacterium Escherichia coli (E. coli). The name prefix "enterohemorrhagic" (from the ancient Greek Enteron, as indicated by the "entero" meaning "intestine" and bleeding, can cause a hemorrhagic diarrheal disease in humans (enterohemorrhagic colitis). It is verotoxin-producing Escherichia coli (VTEC) and synonymous with Shiga toxin-producing Escherichia coli (STEC), that produces the clinical picture in patients. The pathogen and the infectious diseases it causes occur worldwide. The main reservoir of the pathogen is formed by ruminants, especially cattle, but also sheep, goats and deer, in whose intestines it occurs regularly without causing disease in them. In countries around the world, large outbreaks of enterohemorrhagic Escherichia coli (EHEC), mainly caused by serotype O157:H7, have occurred: for example, in 1982 many people in the USA fell ill after eating underheated hamburgers, in 1996 around 9,000 schoolchildren in Japan fell ill after eating radish sprouts, and in 2006 an outbreak in California affected 26 states in the USA. In Germany, a continuous increase in the number of reports was observed in 2003, after the introduction of a nationwide reporting obligation in 1998.

[0053] Enteropathogenic Escherichia coli (EPEC) causes severe diarrhea in young children, which is uncommon in industrialized societies but causes death in children in underdeveloped countries. Using EPEC attachment factors (EAFs), EPEC attach to epithelial cells in the small intestine and then inject toxins into the enterocytes using a type III secretion system. So-called atypical EPEC also exist. They display the serotypes commonly seen in STEC as well as virulence and pathogenicity factors, such as the eae gene. However, they may have lost the Stx prophage and associated stx genes characteristic of STEC.

[0054] Enterotoxigenic Escherichia coli (ETEC) is the more common causative agent of traveler's diarrhea ("Montezuma's revenge"). The cause of this disease is the A / B type heat-labile enterotoxins (LT I and LT II), as well as the heat-stable enterotoxin (ST). This 73 kDa protein has two domains, one of which binds to the G-ganglioside of the target cell (binding domain). The other domain is the active ingredient, which activates adenylate cyclase similar to cholera toxin (about 80% gene homology). ST, which is about 15 to 20 amino acids long, activates guanylate cyclase. Activation of adenylate cyclase and guanylate cyclase ends in secretory diarrhea, in which large amounts of water and electrolytes are lost. The bacteria receive genetic information from lysogenic phages by transduction.

[0055] Enteroinvasive Escherichia coli (EIEC) penetrate the epithelial cells of the colon and proliferate there. Within the cells, the formation of actin tails occurs, which penetrate into adjacent epithelial cells, such as Listeria and Shigella. Inflammation and ulceration occur with the secretion of blood, mucus and white blood cells (granulocytes). In addition, EIEC can release enterotoxins, which lead to electrolyte and water loss. The clinical manifestations are similar to bacterial dysentery with fever and bloody-mucous diarrhea, so the symptoms are weakened and often accompanied by watery diarrhea.

[0056] Enteroaggregative Escherichia coli (EAggEC or EAEC) has the ability to self-aggregate. They attach themselves to the small intestinal epithelium with specific pili. The characteristic is that the mucus production of mucosal cells is increased, which delays excretion. Due to the presence of secretory diarrhea due to enterotoxin (EAST). EAEC causes acute and chronic recurrent diarrheal diseases that can last for weeks. In addition to watery mucous diarrhea, there may also be fever and vomiting or bloody stools. In immunosuppressed people, such as HIV patients, EAEC is the most common causative agent of bacterial enteritis.

[0057] However, other bacteria that generally have only a minor role include Yersinia, Shigella, Brucella, and Leptospirosis.

[0058] Protozoa (single protozoan or protozoa, multiple protozoa or protozoa) is an informal term for a group of unicellular eukaryotic organisms that are free-living or parasitic, feeding on organic matter such as other microorganisms or organic tissues and debris. Historically, protozoa were considered "single-celled animals" because they generally possess animal-like behaviors such as motility and predation, and lack a cell wall, as found in plants and many algae. Protozoan infection, also called protozoal disease or protozoal disease, is the active or passive penetration of protozoa (animal unicellular organisms) into an organism, where they multiply and subsequently react to the organism in the form of disease.

[0059] Important pathogenic protozoa include:

[0060] Entamoeba histolytica (Amoebozoa), which is transmitted through water and food, causes amoebiasis.

[0061] Balantidium coli (Ciliate), which is transmitted through water and food, causes balantidiasis.

[0062] Toxoplasma gondii (Apicomplexa), which is transmitted, inter alia, by undercooked meat, causes toxoplasmosis.

[0063] Cryptosporidium is a genus (phylum Apicomplexa) that is transmitted through fecal contamination of food and water, causing cryptosporidiosis.

[0064] Cyclospora cayetanensis (Apicomplexa), which is transmitted by faecal contamination of food and water, causes cyclosporiasis.

[0065] Viruses are small infectious agents that infect all forms of life, from animals and plants to microorganisms, including bacteria and archaea. Viruses are found in almost every ecosystem on Earth and are the most common type of biological entity. When infected, host cells are usually forced to quickly produce thousands of copies of the original virus. When not in an infected cell or in the process of infecting a cell, the virus exists in the form of an independent particle or virion composed of: (i) genetic material, i.e., a long molecule of DNA or RNA encoding the protein structure of the virus; (ii) a protein coat, i.e., a capsid that surrounds and protects the genetic material; and in some cases (iii) an outer envelope of lipids. The shapes of these virus particles range from simple spiral and icosahedral forms to more complex structures. The virions of most virus species are too small to be observed with an optical microscope and are one hundredth the size of most bacteria.

[0066] There are a large number of important pathogenic viruses for all domesticated animals or for some of them. Some of these viruses, the diseases they cause and the domesticated species they infect are listed in the table below:

[0067]

[0068]

[0069] Table 1: List of exemplary viruses, the diseases they cause and the domesticated species they infect (P. Murcia et al., "Viral Pathogens of Domestic Animals and Their Impact on Biology, Medicine and Agriculture", Encyclopedia of Microbiology 2009, 805-819).

[0070] The method of the present invention is particularly suitable for poultry because their life cycle is relatively short. When poultry is livestock in the method according to the present invention, the pathogen is preferably one or more of the following: Campylobacter, Salmonella, Clostridium perfringens, Escherichia coli, enterohemorrhagic Escherichia coli, enteropathogenic Escherichia coli, enterotoxigenic Escherichia coli, enteroinvasive Escherichia coli, enteroaggregative Escherichia coli, infectious laryngotracheitis virus, infectious bursal disease virus, duck hepatitis virus, highly pathogenic and low pathogenic avian influenza virus, infectious bursal disease virus, Marek's disease virus, Newcastle disease virus, and avian metapneumovirus.

[0071] In step c) of the method according to the invention, data D indicating the identity and load of one or more pathogens in livestock are received or retrieved from the input / output device of step a). patThese data are preferably the concentration of the pathogen per unit mass, and / or the number of DNA copies of the pathogen per unit mass.

[0072] In one embodiment of the method according to the invention, the data of C1) comprises the concentration of the pathogen per unit mass, and / or the number of DNA copies of the pathogen per unit mass.

[0073] The likelihood that any pathogen identified in c1) or c') is causing the disease found in step d3) is preferably sent to the input / output device of step a).

[0074] Alternatively or additionally, the discovery may also be further processed. Advantageously, for the user of step a), the data D received or retrieved in step c) is pat , farm and / or farmhouse logoI farm , and generate data D pat Time point T data , together with the findings from step d), are stored in dataset D user and when the method of claim 1 is repeatedly performed for the same user identification, it is advantageous to transfer the data D received or retrieved in step c) to pat , farm and / or farmhouse logoI farm , and the latest time point T at which the data is generated data , together with the findings from step d3), are stored in the same dataset D as in step c1). user This allows summaries of relevant data to be provided, for example, to represent the data collected. pat developments, and confirmation of trends in pathogen loads in the livestock in question.

[0075] In another embodiment, the method according to the present invention further comprises the following steps:

[0076] d4) For the user of step a), the data D received or retrieved in step c) pat , farm and / or farmhouse logoI farm , and generate data D pat Time point T data , together with the findings from step d3), are stored in the dataset D user In, and

[0077] d5) When the method according to the present invention is repeatedly performed for the same user identification, the data D received or retrieved in step c) is pat , farm and / or farmhouse logoI farm , and the latest time point T at which the data is generated data , together with the findings from step d3), are stored in the same dataset D as step c1).user middle.

[0078] It is further advantageous to transfer the data D to each farm and / or farmhouse of the user of step a). pat , discovery and latest time point T data The reports are grouped into individual reports in a list with the report with the latest point in time at the top of the list. This provides the user with a complete overview of the situation on each of their farms and / or farmhouses at the point in time when the data was generated.

[0079] In another embodiment, the method according to the present invention further comprises the following steps:

[0080] e) The user request of step a) activates the operating unit to perform at least the following steps:

[0081] e1) For each farm and / or farmhouse of the user in step a), from the user's dataset D user Extract the data D stored in step d4) or d5) pat , the discovery and the latest time point T at which the data was generated data ,

[0082] e2) For each farm and / or farmhouse, the data D extracted in step e1) are pat , discovery and latest time point T data Grouped in a list into a single report with the latest time point T data The report is at the top of the list, and

[0083] e3) sending the list of reports obtained in step e2) to the user's input / output device.

[0084] Preferably, the risk matrix according to the invention reports the probability that the pathogen in question causes a risk. It is further preferred that for each pathogen in question, i.e. in the data D pat The risk of infectious disease caused by the pathogen in question is categorized as

[0085] - For the load of the pathogen in the risk matrix, when the data D indicating the load of the pathogen pat Not greater than v D It is unlikely that

[0086] - For the load of the pathogen in the risk matrix, when the data D indicating the load of the pathogen pat Greater than v D and not greater than v L It doesn't happen often.

[0087] - For the load of the pathogen in the risk matrix, when the data D indicating the load of the pathogen pat Greater than v L and not greater than v T , then it is possible, and

[0088] - For the load of the pathogen in the risk matrix, when the data D indicating the load of the pathogen pat Greater than v T , then it is certain.

[0089] In general, every value, in particular any measured value, is subject to measurement errors or inaccuracies in the determination method. Therefore, the method according to the invention must take into account any measurement errors or inaccuracies in the determination method when making an assessment regarding the risk of an infectious disease without losing any validity in the assessment. When the pathogen load is very close to the value v L and v T This is particularly relevant when the risk of an infectious disease caused by the pathogen in question can be classified as either possible or certain, which makes a big difference. L and v T In detail, when the p-value from the comparison of the load of the pathogen in question with the reference value of the lower risk level is <0.05, the risk of infectious disease caused by the pathogen in question is classified as the next higher risk level. For example, when the load has a value v T When compared with a p-value of < 0.05, the load of the pathogen in question was classified as definitive.

[0090] Preferably, the identification and load of the pathogen or pathogens in question are given in a specific color indicating the degree of risk or in a specific pattern according to a code indicating the degree of risk. This has the advantage of better understanding any potential risk of disease occurrence caused by the pathogen. For example,

[0091] - When the risk of an infectious disease caused by the pathogen in question is unlikely, the identity of the pathogen and / or data indicating its abundance pat Colored with green,

[0092] - When the risk of an infectious disease caused by the pathogen in question is rare, the identity of the pathogen and / or data indicating its abundance pat Colored with yellow,

[0093] - When the risk of infectious disease caused by the pathogen in question is possible, the identity of the pathogen and / or data indicating its load pat Colored with orange, and

[0094] - When the risk of infectious disease caused by the pathogen in question is determined, the identity of the pathogen and / or data indicating its load pat Colored with red.

[0095] In the next step, the user can select from the list of reports thus provided. For the report selected in step e4), data D will be generated. pat At each time point T data Data D pat The load of each pathogen in the livestock is plotted, and for data D pat For each pathogen in the L and v T Since the data are plotted for each time point that the data were generated, this process helps to identify trends in pathogen load in livestock in general, i.e. overall pathogen load, and in particular, i.e. specific load with a specific pathogen. In addition, when the value v of the risk matrix L and / or v T When the readings are also plotted into a graph, the total risk of occurrence can be identified.

[0096] In a preferred embodiment, step e) of the method according to the present invention further comprises the following steps:

[0097] e4) receiving a report selection from the input / output device of the user, e5) generating the data D for the report selected in step e4) pat Every time

[0098] Point T data , the data D pat e6) extracting a risk matrix according to the invention from the database, preferably a risk matrix according to the method according to the invention;

[0099] The risk matrix of step d1),

[0100] e7) For data D pat For each pathogen in the present invention, a value v for one or more pathogens is read from the risk matrix according to the invention, preferably the risk matrix according to step d1) of the method according to the invention. L and / or v T ,as well as

[0101] e8) The value v read in step e7) L and / or v T Plot in the diagram obtained in step e5).

[0102] Once the user has read the selected report, the user is usually no longer interested in reading the same old report again. Instead, the main focus of the user is on the most recent reports and data. In order to avoid that the user has to look at any old reports again, the method according to the invention receives a read confirmation of the report selected in step e4) from the user's input / output device and stores the read confirmation of the report together with the corresponding data. When at least step e) or the method according to the invention is repeatedly performed, the data set D is also received. user and sorting all reports without read confirmation in a first list with the report with the latest time point at the top of the first list, and sorting all other reports in a second list with the report with the latest time point at the top of the second list.

[0103] In a further preferred embodiment, step e) of the method according to the invention further comprises the following steps:

[0104] e9) if applicable, receiving a readout of the report selected in step e4) from the user's input / output device

[0105] Read confirmation and report the read confirmation with the corresponding D pat , discovery and latest time point T data Stored together in dataset D user In, and

[0106] e10) When at least step e) is repeatedly performed, step e1) further comprises: user middle

[0107] Extracting the read confirmation, and step e2) further comprises sorting all reports without read confirmation in a first list, wherein the report with the latest time point is at the top of the first list, and sorting all other reports in a second list, wherein

[0108] The report with the latest time point is located at the top of the second list.

[0109] The method according to the invention is based on the time point T of C3). data It is also helpful for the user to provide suggestions for further sampling on the date specified in the sample, in particular, to provide suggestions for the scheduled sampling date.

[0110] In another preferred embodiment of the method according to the present invention, the report of step e2) further comprises a time point T based on step C3). data Date The date on which sampling is scheduled.

[0111] In another preferred embodiment, the method according to the present invention further comprises the following steps:

[0112] f) Planning a new cycle for monitoring pathogen load in livestock, which includes the following steps:

[0113] f1) receiving the selection or input of farms and / or farmhouses for planning a new cycle,

[0114] f2) receiving a selection or input of a start date for a new period, and

[0115] f3) Receive a segment or input of the age of the animal at the start of a new cycle and the range of the new cycle.

[0116] In order to avoid any overlap of new cycles for monitoring the pathogen load in livestock with active cycles for monitoring the pathogen load in livestock, it is beneficial to indicate these active cycles in step f2).

[0117] In a preferred embodiment, step f2) further comprises an indication of a period for monitoring pathogen load in active livestock.

[0118] It is also beneficial for the user to obtain information about the pathogen load of all his farms and / or farmhouses, such as the average pathogen status of all his farms and / or farmhouses and the average conditional risk of all his farms and / or farmhouses.

[0119] Preferably, the method according to the invention therefore further comprises the following steps:

[0120] g) receiving a request from the user's input / output device to aggregate a report, which comprises the following steps: g1) for all farms and / or farmhouses of the user, from the data set D user Data D pat extract

[0121] The load of each pathogen in the livestock on each farm and / or farmhouse was averaged to obtain the average pathogen status for all farms and / or farmhouses.

[0122] In order to obtain the average conditional risk for all farms and / or farmhouses, step g1) is preferably followed by the following steps:

[0123] g2) extracting from the database the risk matrix according to the invention, in particular the risk matrix according to step d1) of the method according to the invention, and

[0124] g3) For all farms and / or farmhouses, read out data D pat The value v of each pathogen in L and / or v T , and form the value v L and / or v T to obtain the average conditional risk v for all farms and / or farmhousesL,mean and / or v T,mean .

[0125] In one embodiment, steps g1) to g3) can also be combined.

[0126] In another embodiment, the method according to the present invention further comprises the following steps:

[0127] g) receiving a request from the user's input / output device to aggregate a report, which comprises the following steps: g1) for all farms and / or farmhouses of the user, from the data set D user Data D pat extract

[0128] The number of pathogens carried by livestock in each farm and / or farmhouse is

[0129] to obtain the average pathogen status for all farms and / or farm buildings,

[0130] g2) extracting from a database a risk matrix according to the invention, preferably a risk matrix according to step d1) of the method according to the invention, and

[0131] g3) For all farms and / or farmhouses, read out data D pat The value v of each pathogen in L and / or v T , and form the value v L and / or v T to obtain the average conditional risk v for all farms and / or farmhouses L,mean and / or v T,mean .

[0132] The method according to the invention can be used on any possible computer device that allows the execution of the method. The purpose of the invention is not subject to any restrictions regarding the computer device, as long as it allows receiving the request of step a), receiving or retrieving the data of step c), and contains or can access the database of the risk matrix according to the method of the invention. For example, the computer device can be a fixed or mobile computer. Examples of fixed computers are desktop computers and network computers. Examples of portable computers are laptop computers, notebook computers, tablet computers and smart phones.

[0133] Therefore, another object of the invention is a computer program product comprising code portions adapted to execute the method according to the invention when said program is loaded into a computer device.

[0134] Preferably, the computer program product comprising code portions comprises or has access to a database with a risk matrix according to the invention.

[0135] Alternatively, the program for the method according to the invention and / or the code portion for executing the method may be stored on a computer-usable medium, such as a USB stick, a memory card, or a CD-ROM. In this case, the program and / or the code portion are stored as a computer-readable program device that causes a computer to execute the method according to the invention.

[0136] Therefore, another object of the invention is a computer program product stored on a computer usable medium, comprising computer readable program means for causing a computer to execute the method according to the invention.

[0137] Preferably, a computer program product stored on a computer usable medium contains or is able to access a database of risk matrices according to the invention.

[0138] The methods according to the present invention may be implemented to provide a system for monitoring pathogen load in livestock.

[0139] Therefore, an object of the present invention is also a system for monitoring the pathogen load in livestock, the system comprising an operating unit, which is adapted to perform the method according to the invention and contains or is able to access a risk matrix according to the method according to the invention, preferably a matrix according to step d1) of the method according to the invention.

Claims

1. A computer-implemented method for monitoring pathogen load in livestock comprising the following steps: a) receiving a request from an input / output device of a user to connect to an operating unit, wherein the request comprises input of at least a user identification, b) The user request of step a) starts the operating unit to perform at least the following steps: c) receiving or retrieving data from the input / output device of step a), wherein the data comprises at least C1) Data indicating the identity and load of one or more pathogens in livestock pat , wherein the identity and load of the pathogen are contained in the data D pat middle, C2) Identification of the farm and / or farmhouse where the livestock of C1) is located farm ,and C3) generates the time point T of the data of C1) data , c') the identity and / or load of the pathogen is not yet included in the data D pat In the case of pat the identity and / or load of one or more pathogens described in d) evaluating the data from c1) and / or, if applicable, c'), comprising the following steps: d1) extract the risk matrix from the database, The risk matrix includes, for each pathogen, the detection limit value v of the pathogen D , the lower limit of the occurrence of the disease caused by the pathogen v L ,as well as The threshold v for the occurrence of disease caused by the pathogen T ,in -v L It is n×v D , where n is a real number greater than 1, where the value of n is given by the probability that the pathogen in question causes an infectious disease, And among them - the pathogen load value does not exceed v D indicates that the risk of an infectious disease caused by the pathogen in question is unlikely, - The pathogen load value is greater than v D and not greater than v L indicates that the risk of an infectious disease caused by the pathogen in question is uncommon, - The pathogen load value is greater than v L and not greater than v T indicates that the risk of infectious disease caused by the pathogen in question is likely, and - The pathogen load value is greater than v T Indicates that the risk of infectious disease caused by the pathogen in question is established; d2) For the data D pat For each pathogen in step c') and / or whose identity is determined in step c'), a value v is read from the risk matrix of step d1). D 、v L and v T ,and d3) For each pathogen, data D indicating the pathogen load pat or the pathogen load determined in step c') and the value v in step d2) D 、v L and v T The comparison is performed to find the likelihood that any pathogen in C1) or identified in c') will cause disease.

2. The method of claim 1, wherein the livestock is one or more of cattle, sheep, goats, pigs, horses and poultry.

3. The method according to claim 1 or 2, wherein the data of C1) comprises the concentration of the pathogen per unit mass and / or the number of DNA copies of the pathogen per unit mass.

4. The method according to any one of claims 1 to 3, further comprising the following steps: d4) For the user of step a), the data D received or retrieved in step c) pat , the identification of the farm and / or farmhouse farm , and generate the data D Pat The time point T data , together with the findings from step d3), are stored in the dataset D user In, and d5) when the method according to claim 2 is repeatedly executed for the same user identification, the data D received or retrieved in step c) is pat , the identification of the farm and / or farmhouse farm , and the latest time point T at which the data was generated data , together with the findings from step d3), are stored in the same dataset D as in step d4). user middle.

5. The method according to claim 4, further comprising the steps of: e) The user request of step a) activates the operating unit to perform at least the following steps: e1) For each farm and / or farmhouse of a user of step a), from said data set D of said user user Extract the data D stored in step d4) or d5) pat , the discovery and the latest time point T at which the data was generated data , e2) For each farm and / or farmhouse, the data D extracted in step e1) are pat , discovery and latest time point T data Grouped in a list into a single report with the latest time point T data The report is at the top of the list, and e3) sending the list of reports obtained in step e2) to the input / output device of the user.

6. The method according to claim 5, wherein said step e) further comprises the following steps: e4) receiving a selection of a report from said input / output device of said user, e5) For the report selected in step e4), generate the data D pat At each time point T data , the data D pat The load of each pathogen in livestock is plotted. e6) extracting the risk matrix according to claim 1 or 2, preferably the risk matrix of step d1) of claim 2, from the database, e7) For the data D pat For each pathogen in the step d1), a value v for the one or more pathogens is read from the risk matrix according to claim 1 or 2, preferably the risk matrix according to step d1) of claim 2. L and / or v T ,as well as e8) The value v read in step e7) L and / or v T Plot in the diagram obtained in step e5).

7. The method according to claim 5 or 6, wherein step e) of the method according to the present invention further comprises the following steps: e9) if applicable, receiving from said input / output device of said user a read confirmation of the report selected in step e4) and comparing said read confirmation of the report with the corresponding D pat , discovery and latest time point T data Stored together in the dataset D user In, and e10) When at least step e) is repeatedly performed, step e1) further comprises: user Extracting read confirmation, and step e2) further includes sorting all reports without read confirmation in a first list, wherein the report with the latest time point is at the top of the first list, and sorting all other reports in a second list, wherein the report with the latest time point is at the top of the second list.

8. The method according to any one of claims 5 to 7, wherein the report of step e2) further comprises a time point T based on step C3). data Date The date on which sampling is scheduled.

9. The method according to any one of claims 1 to 8, further comprising the following steps: f) Planning a new cycle for monitoring pathogen load in livestock, which includes the following steps: f1) receiving the selection or input of farms and / or farmhouses for planning a new cycle, f2) receiving a selection or input of a start date for said new period, and f3) Receiving a segment or input of the age of the livestock at the start of said new cycle and the range of said new cycle.

10. The method of claim 9, wherein step f2) further comprises an indication of a period for monitoring pathogen load in active livestock.

11. The method according to any one of claims 1 to 10, further comprising the steps of: g) receiving a request from said input / output device of a user to aggregate a report, comprising the steps of: g1) For all farms and / or farmhouses of the user, from the dataset D user The data D pat The load of each pathogen in livestock on each farm and / or farmhouse was extracted and the average load of each pathogen was formed to obtain the average pathogen status of all farms and / or farmhouses.

12. The method according to any one of claims 1 to 10, further comprising the steps of: g) receiving a request from said input / output device of a user to aggregate a report, comprising the steps of: g1) For all farms and / or farmhouses of the user, from the dataset D user The data D pat The load of each pathogen in livestock on each farm and / or farmhouse is extracted to form an average of the load of each pathogen in order to obtain the average pathogen status of all farms and / or farmhouses, g2) extracting the risk matrix of claim 1 or 2, preferably the risk matrix of step d1) of claim 2, from a database, and g3) For all farms and / or farmhouses, read out data D pat The value v of each pathogen in L and / or v T , and form the value v L and / or v T to obtain the average conditional risk v for all farms and / or farmhouses L,mean and / or v T,mean .

13. Computer program product comprising code portions adapted to perform the method according to one of claims 1 to 12, when said program is loaded into a computer device.

14. A computer program product stored on a computer usable medium, comprising computer readable program means for causing a computer to execute the method according to any one of claims 1 to 12.

15. System for monitoring pathogen load in livestock, comprising an operating unit adapted to perform the method according to one of claims 1 to 12 and comprising or having access to a database with a risk matrix according to claim 1.

Images