Environmental pressure of infection

Abstract

A method for predicting disease risk in epidemiological units of production animals by determining the environmental pressure (EPI) for infection. EPI determination includes establishing an environmental pressure threshold (EPIt) for infection, constructing an environmental baseline (EBL), and forming disease risk alerts.

Description

[Technical Field]

[0001] This invention relates to the field of animal health and the formation of alarms based on the detection of environmental pressures of infection in production animal epidemiology units using diverse molecular and zoonotic infectious disease techniques. [Background technology]

[0002] Air pollution represents a high environmental risk to humans and animals because the air contains inorganic pollutants along with ABP (suspended biological particulate matter), such as bacteria, viruses, and fungi, which originate from various parts of the environment, including soil, plants, and water.

[0003] The probability of animals becoming ill and the severity of their illness depend, in particular, on the infectivity of the pathogen and its aerosolization. In livestock farming environments such as animal facilities, exposure to bacteria, viruses, fungi, and parasites can have significant impacts on animal health, production efficiency, and ultimately, public health.

[0004] Currently, the problem of diseases caused by viruses and bacteria is addressed by several tools: preventive programs using drugs, such as vaccines and antibiotics; sampling for diagnosis by autopsy and blood sampling when a disease is suspected; and control of vaccination programs.

[0005] While specific vaccines may exist in many cases, no vaccine is effective against all known and potential pathogens, making the use of certain drugs in conjunction with specific vaccines essential. On the other hand, the unplanned use of antibiotics leads to antibiotic resistance, which is a potential problem for animal and human health. The method proposed by this invention has two advantages over this: it can respond to the detection of any newly emerging or novel pathogens in nature, and on the other hand, it can detect biological threats as they arise, enabling appropriate treatment and consequently reducing the unnecessary use of antibiotics.

[0006] Autopsies, saliva, cloacal sampling, or blood sampling performed after a disease is suspected have a significant disadvantage: they are carried out after the disease has progressed, i.e., after the onset of symptoms or death, and therefore fail to address anticipated problems. In addition, there are biological factors, such as trimepneumovirus, that kill animals but do not leave a trace in the tissue long enough to be detected later. Therefore, this invention proposes the implementation of a sampling plan that enables the detection of causative biological factors before the manifestation of disease.

[0007] Conventional methods used to detect infections in animals may be biased and / or limited in terms of the representativeness or diversity of the pathogens detected and the time required for these methods to yield results. For direct detection of pathogens, classical microbiological techniques such as bacterial culture are used; while simple and useful, results may be delayed by several days or weeks (in the case of mycoplasma) or unusable due to a far greater diversity of unculturable microorganisms than culturable ones. Serological tests such as ELISA, performed on serum samples from individuals and detecting antibodies and antigens, are either excessively labor-intensive or difficult to detect when other microorganisms are present or when the concentration of the target pathogen is too low in the early stages of infection in a population (Hosseini et al., 2018), resulting in low accuracy in detecting pathogens in samples.

[0008] Controlling vaccination programs using ELISA often involves invasive (sometimes including animal slaughter and blood sampling) and laborious sampling methods, which can be particularly inconsistent in the early stages of infection. The method provided by the present invention is non-invasive, easy to implement, and representative of the sampling environment.

[0009] Furthermore, some detection methods such as ELISA by serology have disadvantages such as having to wait for the immune response caused by the pathogen, and there are other disadvantages. The advantages and novelty of the methods described in the present invention compared to ELISA are summarized in the table shown in FIG. 1.

[0010] Other technologies used for the detection of pathogens in samples such as PCR and next-generation sequencing (NGS) have been shown to provide rapid, comprehensive, and accurate detection of pathogens in environmental and clinical samples. PCR is used for nucleic acid amplification of specific genes, and specific sequences are detected by means of fluorescent probes or hybridization assays or identified by qPCR as described in patent application WO2021250274A1. On the other hand, NGS enables the sequencing of large amounts of DNA and / or RNA fragments much faster and at a lower cost than the Sanger sequencing method, which was long used before NGS and is shown in US Patent 11,485,969B2.

[0011] Current methods for sampling and subsequent detection of pathogens in the environment can be limited in terms of sensitivity, efficiency, and logistics. Special transport solutions are required for sample transport, and the stability of pathogens in these solutions can be affected by water activity and other factors. Various air sampling devices are used for community testing. The Hirst-type device is a good option for community monitoring, but it cannot take into account viruses, is incomplete, and is expensive and non-portable. The polytetrafluoroethylene filter is also good, but lacks an appropriate analytical method for air microbiota samples (WO2021250274A1). In addition, none of these methods, by themselves, provide an alarm or report that enables the producer to predict the adverse effects of pathogens present in the environment.

[0012] To determine the presence of pathogens in the air, there are a series of devices and methods for capturing and analyzing aerosols, as reported, among others, in patent applications WO2021250274Al, WO2022101510, US 11,485,969B2 and UY38805. However, these devices prevent timely and reliable responses from an epidemiological perspective for users to make decisions about animal health and production, and require time-consuming laboratory procedures.

[0013] Regarding prediction techniques or real-time risk identification, Boehringer Ingelheim FetroMedica GmbH has developed the "SoundTalks" technology based on animal weight and sound recordings. On the other hand, methods and computer systems for establishing disease risk indicators, such as those shown in patent applications CN112785198, US Patent Application Publication 2022136730, US Patent Application Publication 2021358632 and CN112986503, can provide fast online information, but their focus is on establishing prediction models using environmental variables such as air pollutants or images, rather than the health of the animals themselves at the population level of a specific epidemiological unit, so specific pathogens cannot be detected or the sensitivity and specificity are low even if detected.

[0014] Therefore, in order to ensure food safety and protect the health of animals and humans, there is a need for an appropriate health management model that maximally fills the correct protection of the corresponding animal population in all processes with maximum efficiency, enables prediction through early detection of infections in animals, and prevention of the spread of infectious diseases.

[0015] The present invention relates to a method that includes measuring the environmental infection pressure from the concentration of pathogens in the air in a different way, similar to that used for detecting airborne infection of SARS-CoV-2 (Krieguel et al., 2022), and establishing disease alerts for any pathogen in a production animal epidemiological unit with appropriate predictive statistical methods.

[0016] This invention enables the establishment of a baseline of infection pressure, thereby predicting the emergence of disease in epidemiological units of production animal populations and thus issuing alerts. Environmental infection pressure is understood as determining the amount of pathogenic microorganisms present in the air and their ability to infect a population at a given time and space.

[0017] While the concept of "infection pressure" exists in prior literature (Perea Gayosso, 2020), it is important to note that it is not applicable to population levels from atmospheric samples as in the present invention. [Overview of the project]

[0018] This invention describes a method for forming an alert from the detection of environmental pressure for infection in an epidemiological unit of production animals. The method includes determining a baseline of environmental pressure for infection from collection of environmental samples, nucleic acid extraction, and molecular analysis, optimized using zoonotic infectious disease and statistical techniques. [Brief explanation of the drawing]

[0019] [Figure 1] This is a comparison table of the method of the present invention and ELISA.

[0020] [Figure 2] This image shows a comparison between environmental samples and cloacal samples.

[0021] [Figure 3] This image shows the results of detecting environmental bird tracheal bronchitis virus against a cloacal sample.

[0022] [Figure 4] This is an image of the results of detecting environmental bird bronchitis virus versus ELISA.

[0023] [Figure 5] Image of sampling module analysis - count of viable microorganisms.

[0024] [Figure 6] This is a table showing the detection limits in an airtight chamber.

[0025] [Figure 7] These are images showing the results of DNA quantification and various storage conditions.

[0026] [Figure 8] This is a table showing the weekly results per hut in the epidemiological unit.

[0027] [Figure 9] This is a table showing the results of poultry production by phase.

[0028] [Figure 10] These are images used to create early warnings about risks: avian bronchitis and mycoplasma.

[0029] [Figure 11] This image shows the creation of an environmental baseline at a hatchery.

[0030] [Figure 12] These are images of 16S metagenomics results in environmental samples from various phases of pig production.

[0031] [Figure 13] This is an image of the indicators and reporting tree. [Modes for carrying out the invention]

[0032] definition

[0033] The term "environmental pressure of infection" (EPI) used here refers to the concentration of a pathogen in the environment at a given time. This is expressed in units of concentration X.

[0034] The "environmental pressure threshold for infection" (EPIt) used here refers to the environmental concentration of a pathogen that, if exceeded, can cause infection / condition in animals. EPIt can also be an empirically established environmental baseline.

[0035] An "environmental baseline" (EBL) is a graph of the environmental pressure of infection for a particular pathogen in a given environment over time.

[0036] "Abnormal excess data" is understood to be any value that is sufficiently far from the typical data of the distribution of the variable under study and therefore implies some kind of warning. In particular, abnormally high levels of mortality, abnormally low body weight, and abnormally high levels of pathogen concentration.

[0037] In this context, the term "CAPTUS" refers to a device for collecting air samples according to UY38805.

[0038] In this specification, the term "epidemiological unit" refers to one or more animal production facilities that contain the same type of animals under the same sanitation, feeding, and safety measures.

[0039] In this specification, the term "user" refers to a person or facility that uses the technology described herein.

[0040] The term "animal infectious diseases" used here refers to a branch of epidemiology that focuses on the study of diseases in animals, particularly animal populations. It encompasses the study of disease distribution, risk factors, diffusion patterns, and impacts in animals. Animal infectious diseases also analyze the interactions between pathogens, host animals, and the environment, aiming for a better understanding of animal diseases and the development of appropriate prevention and response strategies.

[0041] In this specification, the term “sample” means one or more filters or membranes with controlled pore sizes that are obtained as a result of a filtration process and represent an environment, containing dust particles, microorganisms, traces of genetic material, etc. The sample must contain information about the environment that corresponds to the sampling time and period, the name and location of the sampling environment, the associated production phase, batch (or similar), the user, and other identifying factors of the sampling process, which shall be unique to the sample.

[0042] In this specification, the term "production phase" refers to one of the stages in the production process of livestock farming, which can be distinguished by the age of the animals and / or the processes carried out using them.

[0043] In this specification, the term "prediction" refers to the ability to provide forecasts of sanitary / productivity conditions based on data analysis techniques.

[0044] In this specification, the term “sectorization” means an item or sector in the sense of a type of animal production, such as pig fattening, chicken fattening, or egg production or incubator.

[0045] In this specification, the term "confined animals" refers to a group of animals (generally for commercial purposes) kept in a shed or similar enclosed or semi-enclosed enclosure during the production phase.

[0046] In this specification, the term “geographic risk alert” refers to an alert issued due to the detection of a microbiological threat, the presence of an infectious disease outbreak, or the presence of a pathogen that may affect other locations in one location, depending on factors such as the type of threat, transmission route, level of impact at the site of outbreak, and the distance between one site of outbreak and another.

[0047] Preface

[0048] The problem to be solved by this invention is to obtain information predicting the emergence of disease in an animal epidemiological unit in production in a rapid and reliable manner. The proposed solution describes a method that enables reliable and early warning of the emergence of disease associated with a specific pathogen, delivered in a timely manner to users of production animals.

[0049] The proposed method consists of measuring environmental pressure (EPI) for infection to report alerts based on environmental baselines (EBL). These alerts enable decision-makers to access high-quality information to mitigate or completely avoid the impact of disease through the implementation of effective, appropriate, and data-driven prevention and control strategies to reduce disease spread and protect animal and human health.

[0050] The method includes environmental sample collection, sample storage and transport, classical microbiological assays and / or nucleic acid extraction and molecular analysis (sequencing, PCR, LAMP, and others).

[0051] While the concept of "infection pressure" exists in prior literature (Perea Gayosso, 2020), it should be noted that it is not applicable to population levels from atmospheric samples as in the present invention.

[0052] While there are specific devices and methods for determining the presence of pathogens in the environment, such as those described in patent applications WO2021250274Al, WO2022101510, and US11485969B2, these methods do not form an alarm about the emergence of disease.

[0053] Furthermore, while U.S. Patent Application Publication 2020131509Al describes a stepwise method for DNA / RNA extraction and sequencing for determining the biota present in the air, this method does not include the entire process and specifications of the present invention, from sampling to reporting an alarm as a result of measuring the environmental pressure of infection.

[0054] Next, from the perspective of zoonotic disease principles, while patent application WO2012115601Al and U.S. Patent Application Publication 2021293817Al refer to the monitoring and analysis of infectious agents and inter-animal epidemic risk, they do not include the entire process of the present invention, nor do they include the step of measuring the environmental pressure of infection, and therefore cannot obtain the resulting alerts of the present invention. Furthermore, they focus on measurements at the individual and non-population levels (WO2012115601A1), and monitoring is stationary rather than using a mobile device like that used in the present invention (UY38805).

[0055] This invention makes it possible to understand the behavior of all pathogens in the animal production environment, compare seasonal behavior, for example, in a rapid and rigorous manner, and receive alerts that enable preventive measures to be taken from the first day of implementation and corrective actions to reduce the impact of disease on animal health.

[0056] Description of the method

[0057] The method for predicting disease risk in the epidemiological unit of production animals according to the present invention is carried out using the establishment of an environmental baseline.

[0058] Environmental baselines are tools that allow us to understand the behavior of a particular pathogen over time in a specific facility or breeding ground, based on the environmental pressure of infection. They also enable the establishment of critical concentration limits for the pathogen, which represent the underlying risks to production. These critical limits are obtained by evaluating extreme outliers that fall outside the boundary. In this way, an alarm can be issued when the concentration at a given point in time in a breeding ground or facility exceeds the established critical limit.

[0059] In particular, the method follows these steps: determination of the environmental pressure of infection (EPI), establishment of the environmental pressure threshold of infection (EPIt), construction of an environmental baseline (EBL), and formation of disease risk alerts.

[0060] 1. Determination of Environmental Pressure of Infection (EPI): Assuming pathogen A from the epidemiological unit, determine its specific concentration (X) in the environment. A To make a decision on this matter, proceed as follows:

[0061] Performing standardized sampling (for a set time and protocol) using an air sampling device of the type defined in patent application UY38805. Obtaining a filter (sample) containing microorganisms and their associated genetic material along with dust particles.

[0062] Stable for up to 72 hours without the need to add stabilizing solutions, for sample storage at room temperature and transport to the laboratory.

[0063] The sample is subjected to a combination of physical and chemical extraction processes that maximize the amount of genetic material extractable from the filter, which may include bead shaking, enzymatic digestion, centrifugation, and / or other proven / supplementary extraction methods to ensure performance. These phase processes ensure representativeness by increasing the sensitivity of subsequent analysis from small samples, minimizing bias in the sample, and maximizing the opportunity to detect pathogen diversity. Typically, samples of less than 100 μl containing DNA or RNA are obtained for distribution across various assays. DNA / RNA quantification is performed using fluorescence and / or absorbance. Information obtained from the sample is expressed in nanograms / m². 3 That is the case.

[0064] As a representative example, DNA or RNA is extracted using a sample and subjected to qPCR with a fluorescent probe in simple or multiplex form to obtain a Ci value that correlates with the copy number of A in the breeding facility / facility. The information obtained from the sample is copy / m 3 X is represented as A That is the case.

[0065] 2. Determination of the Environmental Pressure Threshold (EPit) for Infection: Assuming pathogen A, determine the baseline concentration (u) at which the pathogen can infect / affect the health of animals. AEstablish (EPIt). EPIt is specific to each pathogen and varies by epidemiological unit. This is established empirically by conducting field trials in the following manner:

[0066] When the presence of A is suspected, there are temporally close precursors, but the epidemiological unit is, for example, the selection of an epidemiological unit that is not in an epidemic area or is suspected of overrepresentation.

[0067] Selection of farms or facilities where past production data (mortality, birth numbers, weights, egg production, etc.), serological and / or symptomatic data are available.

[0068] Conduct a field trial at the selected farm and measure the concentration in the air (X A ) weekly, for example, according to the method described in 1 (EPI).

[0069] Conduct serological tests and / or measure production data (mortality, birth numbers, weights, egg production, etc.) and / or identify symptoms during the field trial period.

[0070] Test the correlation between pairs (e.g., an event of high mortality and an event of high pathogen load X A ) and divide the levels that significantly affect production X A = u A to obtain the EPlt threshold of X A and the embodiment through the conversion of the attributes of production data.

[0071] 3. Environmental baseline (EBL): By going through the epidemiological unit in a farm or some production facility for a certain sector or production sector of interest, measure the environmental infection pressure of pathogen A over time (t), for example, weekly, as the concentration in the air (X A ). The environmental baseline (EBL) is characteristic and unique over time for each disease and each facility and is always fed back with new data.

[0072] The environmental baseline can be used to understand the behavior of a certain pathogen, provide early warnings, and is set as follows:

[0073] At time (t), X A If no data is available, the EBL for the first sampling cycle is:

[0074] According to step 2 above, EBL1 = u A That is the case.

[0075] For the second sampling cycle, the baseline is:

[0076] EBL2 = Q32 + C1 × RIC2, where:

[0077] Q32 is the cumulative value of 75% of the ordered data in the second sampling cycle. RIC2 is the interquartile range of the ordered data in the second sampling cycle, where C1 = (u A -Q31) / RIC1, where u A Q31 is the pathogen concentration determined in step 2. Q31 is the cumulative value of 75% of the ordered data in the first sampling cycle. RIC1 is the interquartile range of the ordered data in the first sampling cycle.

[0078] For the third sampling cycle, the baseline is:

[0079] EBL3 = Q33 + ((C1 + C2) / 2) × RIC3, ​​where Q33 is the cumulative value of 75% of the ordered data in the third sampling cycle, RIC3 is the interquartile range of the ordered data in the third sampling cycle, and C1 = (u A -Q31) / RIC1, where u A Q31 is the pathogen concentration determined in step 2, Q31 is the cumulative value of 75% of the ordered data in the first sampling cycle, RIC1 is the interquartile range of the ordered data in the first sampling cycle, and C2 = (u A -Q32) / RIC2, where u AQ32 is the pathogen concentration determined in step 2, Q32 is the cumulative value of 75% of the ordered data in the second sampling cycle, and RIC2 is the interquartile range of the ordered data in the second sampling cycle.

[0080] From the third cycle onward, the baseline takes the following form:

[0081] EBL i =Q3 i +C×RIC i And here:

[0082] Q3 i This value represents the cumulative value of 75% of the ordered data in the i-th sampling cycle, and is RIC. i is the interquartile range in the i-th sampling cycle, and C i is the reference coefficient in the i-th sampling cycle, where:

[0083] C i =(C1+C2+.....C n-1 ) / n-1.

[0084] To summarize, and to put it another way:

[0085] For sampling cycle 1, the criterion is:

[0086] EBL1=u A

[0087] For each sampling cycle, EBL during sampling cycle t+1. t+1 =Q3 t+1 +C t ×RIC t+1 and C t =(C1+C2+.....C t-1 Use ) / t-1.

[0088] At the end of each sampling cycle, a new coefficient C t+1 Learn this and add it to the average of the next sampling cycle:

[0089] At the end of sampling t+1, C t+1 =( u A -Q3 t+1 ) / RIC t+1 .

[0090] 4. The alarm system is designed based on the establishment of the EBL or situations that need to be known to the user. Some of these alarms are detailed below:

[0091] Early Risk Alert: A notification issued to users of the epidemiological unit when a sample exceeds the concentration of pathogen A established by EBL. For the first implementation, this value is equal to EPlt, which initially triggers the alert X A Rather, it means that reports will be based on known information from A. This has the advantage of enabling alerts to be issued from the first day of implementation, and then adapting these alerts to the specific circumstances of each epidemiological unit.

[0092] Geographic Risk Alerts: These are alerts reported in the vicinity of a disease or pathogen that has a very high relevance to the sector, and are considered early risk alerts. These alerts are constructed using georeferenced data that associates a pathogen load or concentration with different locations. The alerts are implemented while maintaining confidentiality regarding the precise origin of the pathogen.

[0093] Universal Environmental Baseline (EBL) u ) is constructed from all data from farms or facilities of the same phase and productive sector. This consists of a weighted average (by number of measurements) EBL, and EBL u =i=ln(EBLi / n)I(Obs i / Total Obs) where i is an epidemiological unit, n is the total epidemiological unit belonging to the same phase and productive sector, and Obs i This is the number observed in epidemiological unit i (X ATotal Obs are the total number of observations conducted across all farms in that phase and productive sector (measurements of and / or other variable factors). This EBL is universal in nature and is input, updated, and evolved as monitoring is conducted, and adjusted with data that becomes more rigorous as a baseline measure for the sector.

[0094] Examples

[0095] Example 1: Environmental sampling vs. occlusal sampling:

[0096] As evidence of the potential representativeness of environmental samples as indicators of processes occurring in animals, we present an analysis of the correspondence between genetic diversity and species shared between two paired types of sampling in broilers: one from the cloacal environment and the other from the atmospheric environment. DNA was extracted for diversity analysis and subjected to 16s gene sequencing. Both samples show shared species and specific species in each sample. The bacterial species shared between the cloacal samples and the atmospheric samples in the environment in which the broilers are raised reach an R2 of 0.6–0.9. This demonstrates a high degree of representativeness that atmospheric samples inform about cloacal samples and the effectiveness of atmospheric approaches for environmental monitoring of the microbiological reality of animals.

[0097] Example 2: Detection of environmental avian bronchitis virus in the excretory cavity

[0098] In monitoring studies for the presence of avian tracheitis virus (IBV), a pathogen of preferential airborne transmission, qPCR-positive air samples showed less than 50% positivity in their excretory counterparts across different poultry farms and at different time points. This suggests that sampling within this sampling scheme using CAPTUS is superior to excretory sampling, which is more labor-intensive, invasive, and ultimately less representative, for preferentially assessing airborne pathogens.

[0099] Example 3: Detection of avian bronchitis virus by environmental gPCR versus autopsy

[0100] In line with the etiology identified by the veterinarian responsible for the facility's health, when detecting pathogenic IBV strains in the air, samples from the trachea of ​​three infected animals from the same facility were sent to the laboratory within one week for analysis using the same qPCR. None of the tested tracheas were positive, indicating that weekly environmental monitoring methods can detect dynamics in a timely manner, and that attempting detection after the onset of symptoms would have made identifying the causative pathogen more difficult.

[0101] Example 4: Detection of environmental avian bronchitis virus against ELISA

[0102] In poultry farms, a correlation was established between the presence of pathogenic IBV (SAI and SAII strains) and the serological response of birds. Farms were environmentally sampled for 6 weeks, and blood samples were taken at the end of the cycle for ELISA serology. Farms positive for pathogenic IBV by qPCR had high ELISA values ​​and a heterogeneous serological profile, while IVB-negative farms had aggregated ELISA values ​​consistent with vaccination without IVB exposure. Furthermore, in this case, the amplification of positive environmental samples is shown compared to positive controls. This indicates a correlation between infection events that may be detectable several weeks prior and immunological effects, consistent with a history of infection.

[0103] Example 5: Analytical Sampling Module - Counting Viable Microorganisms

[0104] Air samples can be captured with CAPTUS, and the filters can be seeded directly onto plates containing culture medium. The growth of various types of microorganisms, such as aerobic bacteria, fungi, yeasts, enterobacteria, or more specific groups, can be analyzed, allowing for the counting of viable microorganisms. Through image analysis, the number of colonies growing on the filters after an appropriate time (depending on the type of microorganism and its density in the sample) can be manually or automatically counted. An example is 1 m 3 This shows environmental contamination of air by aerobic microorganisms that proliferate in a filter obtained after 10 minutes of sampling, and the direct count of the filter in this case is m 3This is expressed as the number of aerobic microorganisms per unit area, and can be used as a numerical indicator of environmental pollution.

[0105] Example 6: Detection limits in an airtight chamber

[0106] To demonstrate the susceptibility of the method under controlled conditions, a variety of microorganisms problematic in the animal and food production industries were diffused in an airtight chamber. The microbial suspension was volatilized using ultrasound to produce suspended particles, and air samples of the dispersed microorganisms were taken by CAPTUS for a standard time of 10 minutes. 3 Various dilution values ​​from tens of millions to tens of thousands of microorganisms were tested. As a result, m 3 Dilutions of an order of magnitude (or more) of 100 microorganisms per sample successfully enabled specific qPCR amplification for these pathogens / vaccines.

[0107] Example 7: DNA quantification data and various storage conditions

[0108] For DNA extraction from air samples, dry filters performed better than filters immersed in virus transport medium (VTM), being approximately three times more efficient than the extraction process alone, and exhibiting significant stability for 72 hours at room temperature. This indicates that, in addition to the ease of dry storage, the methods of extraction, transport, and dry storage are themselves superior.

[0109] qPCR performed with dry filters yielded better CT results compared to MTV filters, and it is noteworthy that it increased the detection sensitivity of RNA viruses such as vaccine IBV by 100 to 200 times.

[0110] Example 8: Standards for manufacturing a swimming pool

[0111] Environmental monitoring was conducted, and monitoring of IBV in air samples allowed for observation of transmission from one shed to another within the same epidemiological unit within one week.

[0112] Environmental monitoring for IBV and other pathogens was conducted weekly using the methods described herein. This was carried out in three separate but identically organized coops constituting the same epidemiological unit. Each coop contained more than 4,000 hens. Samples were transported to the laboratory and searched for non-vaccinated IBV strains by qPCR. Where infection with a field-grown IBV strain was detected, two or more infected coops were detected, depending on the type of pathogen, at maximum intervals of one week. This indicates that infection may begin where it started, or it may be seen first in two or three coops, and then the following week in other coops different from those where it was first seen. This explains the ability to monitor the environment after transmission and infection dynamics within the epidemiological control unit of the farm.

[0113] Example 9: Traceability between environments / phases

[0114] In this example, the correlation between the appearance of Salmonella in the air and its presence in the incubators and eggs in subsequent phases was observed, showing that it began in the air of the rearing area, continued in the incubators, and ended in the eggs. Four groups of egg-laying hens were environmentally monitored (air + cloaca) for the presence of Salmonella (and subsequent Enteritidis / Typhimurium differentiation), the incubators were environmentally monitored (air), and the egg surface was also targeted. This example shows how it progressed from the first week when there was no Salmonella in the environment and cloaca, to the next sampling day when Salmonella was first detected in the air of the rearing area, with Salmonella simultaneously occurring in the air from the hatchery. Finally, its presence was observed at environmental levels in further rearing areas and incubators, as well as on the egg surface.

[0115] Example 10: Predictive Warning of Risks

[0116] The detection of viruses and bacteria coinciding with the timing of infection can increase productive losses due to co-infection / sequential infections that amplify the impact. In the following example, the continuous presence of Mycoplasma sinovale (myco S) in this breeding flock was followed by the detection of increased levels of IBV-SAI strain for the first time at that farm. This simultaneous occurrence of these two pathogens, known to have synergistic pathogenicity, coincides with a high detectable mortality peak at 35-36 weeks, uncorrelated with fever peaks or other causes. At 35 weeks, the alarming presence of IBV-SAI in the presence of Mycoplasma sinovale constitutes a control tool to avoid the consequences of a combined infection peak (as can be seen by mycoplasma re-emergence after environmental detection of IBV-SAI, and other pathogens such as Salmonella also beginning to increase in the following weeks). In a further example, producers administered antibiotics for secondary infections based on a record of IBV-SAI detection to avoid bacterial infections that have a greater impact in the presence of unvaccinated BVI. This suggests that this method of weekly environmental monitoring of pathogens can help provide warnings at least one week before the peak of deaths, which is a possible reason to implement good treatments such as antibiotics and pain relievers.

[0117] Example 11: Environmental baseline in a hatchery

[0118] Airborne environmental data from counts makes it possible to reconstruct the reality of the facility's contamination levels and create a baseline.

[0119] In this embodiment, the aerobic bacterial count (UFC / m³) was monitored by weekly atmospheric sampling in a typical hatchery environment. 3 The data was analyzed. Using this data, a scatter plot of points by sampling date was created based on 3×RIC upper limit: Q3 - 3×RIC (where Q1 and Q3 are the 25th and 75th percentiles, respectively) and RIC interquartile range, resulting in 16 UFC / m 3The critical limit baseline (black line) was constructed using this method. This allows for data monitoring and the issuance of alerts to producers who report that contamination levels may exceed normal levels. In the following example, an outlier within the distribution (above the line) is reported in just two weeks. It should be noted that the values ​​for the facility's "post-cleanup" data are low with the same monitoring, and the monitoring and alerting system is adjustable to the cleanup schedule.

[0120] Example 12: Long-term traceability for single-facility breeding facilities

[0121] In the following example, atmospheric environmental sampling of a pig farm was carried out in six environments representing various production phases, including pregnancy, farrowing, rearing, and fattening. Each phase was physically separated from the others by tens to hundreds of meters, but corresponded to a single connected management unit. Airborne bacterial diversity at various taxonomic levels was tested by large-scale sequencing to examine the extent of sharing between phases. Different phases showed environmental diversity ranging from tens to hundreds of species, which were classified into major classes that accounted for over 80% of the total relative abundance of all samples and were shared across all production phases. At the species level, 10 to 20 representative species of the most abundant species were shared across phases, but the relative abundance and ranking order changed. The level of bacterial sharing in this environmental analysis indicates cross-sectional microbiological reality at various locations at the regional level of the farm.

[0122] These metagenomic results, when compared with databases created for various phases of pig farming, enable the reporting of airbiota indices for each phase.

[0123] Example 13: Metrics and Reporting Tree

[0124] The following diagrams detail various indicators used to create zoonotic disease reports of warning and indicator types that enable users to take preventive or adaptive actions.

[0125] References

[0126] Hosseini, S., Vazquez-Villegas, P., Rito-Palomares, M., Martinez-Chapa, SO (2018). Advantages, Disadvantages and Modifications of the Conventional ELISA. In: Enzyme-linked immunosorbent assay (ELISA). Springer Briefs in Applied Sciences and Technology ( ). Springer, Singapore. https: / / doi.org / 10.1007 / 978-981-10-6766-25 .

[0127] Pardo Cobas, MV (November 2006). Compendium of Epidemiology of the National Agrarian University. https: / / repositorio.una.edu.ni / 2439 / 1 / nl73p226.pdf

[0128] Perea Gayosso, J. (2020, July 7). Considerations on infection pressure in pig farms. Akosos: https: / / www.engormix.com / porcicultura / articulos / consideraciones-sobre-presion-infeccion-t45665.htm

[0129] Krieguel M. et al (Int. J. Environ. Res. Publico Salud 2022, 19(1), 220). アクセス: https: / / www.mdpi.com / 1660-4601 / 19 / l / 220

Claims

1. A method for predicting disease risk in an epidemiological unit of production animals: a) Determination of the environmental pressure (EPI) for infection; b) Establishment of an environmental pressure threshold (EPIt) for infection; c) Establishment of an environmental baseline (EBL); and d) Disease risk alerts are generated Methods that include...

2. The determination of the environmental pressure of infection is based on the value X calculated for the epidemiological unit of production animals. Ａ The method of claim 1, comprising the determination of, where X is a concentration value and A is a pathogen value.

3. Value X Ａ The method of claim 2, wherein is the concentration of the pathogen measured as the number of copies of the pathogen per cubic meter.

4. Value X Ａ The method of claim 2, wherein the value is determined by applying qPCR to DNA or RNA extracted from an air sample.

5. The determination threshold for environmental pressure of infection (EPIt) is the value calculated for the animal epidemiological unit u Ａ The method of claim 1, comprising, where U is a baseline concentration value at which the pathogen can affect the health of an animal, and A is a pathogen value.

6. The method of claim 5, wherein the UA value is the threshold concentration of the pathogen measured as the number of pathogen copies per cubic meter.

7. The UA value is selected from critical production values ​​such as mortality, body weight, birth rate, and feed efficiency relative to body weight, and pathogen X in a representative epidemiological unit. Ａ The method of claim 5, obtained from the correlation of the events.

8. value u Ａ The environmental pressure threshold (EPIt) for the concentration of pathogen A is equal to the threshold that affects animal production, where X Ａ EPIT = u Ａ The method according to claim 5.

9. Determination of an environmental baseline (EBL) calculates a value X over time (t) for a production animal epidemiological unit for a disease in an animal epidemiological unit Ａ including the determination of, where X is a concentration value and A is a pathogen value, the method of claim 1. In particular, if there is no data for X Ａ at a given time (t), the environmental baseline is u Ａ as such.

10. The determination of the environmental baseline (EBL) is a value X calculated over time (t) for the epidemiological unit of production animals for disease in the animal epidemiological unit. Ａ This includes the decision that X at a certain point in time (t). Ａ If there is no data, then EBL is u Ａ The method according to claim 1.

11. For the second sampling cycle, EBL 2 = Q3 ２ +C １ｘ RIC ２ And here, Q3 ２ This value represents the cumulative value of 75% of the ordered data in the second sampling cycle, and RIC ２ is the interquartile range of the ordered data in the second sampling cycle, and C １ = (u Ａ - Q3 １ ) / RIC １ And here, u Ａ This is the determined pathogen concentration, Q3 １ This value represents the cumulative value of 75% of the ordered data in the first sampling cycle, and RIC １ The method of claim 10, wherein is the interquartile range of the ordered data in the first sampling cycle.

12. Regarding the third sampling cycle, EBL ３ = Q3 ３ +((C １ +C ２ ) / 2)×RIC ３ And here, Q3 ３ This value represents the cumulative value of 75% of the ordered data in the third sampling cycle, and RIC ３ is the interquartile range of the ordered data in the third sampling cycle, and C １ = (u Ａ - Q3 １ ) / RIC １ And here, u Ａ This is the concentration of the pathogen, Q3 １ This value represents the cumulative value of 75% of the ordered data in the first sampling cycle, and RIC １ is the interquartile range of the ordered data in the first sampling cycle, and C ２ = (u Ａ - Q3 ２ ) / RIC ２ And here, u Ａ This is the concentration of the pathogen, Q3 １ This value represents the cumulative value of 75% of the ordered data in the second sampling cycle, and RIC １ The method of claim 11, wherein is the interquartile range of the ordered data in the second sampling cycle.

13. For each sampling cycle from the third sampling cycle onward, EBL ｉ = Q3 ｉ +C ｉ ×RIC ｉ And here, Q3 ｉ This value represents the cumulative value of 75% of the ordered data in the i-th sampling cycle, iQR ｉ This is the interquartile range in the i-th sampling cycle, and C ｉ This is the reference coefficient in the i-th sampling cycle, and C ｉ = (C １ +C ２ +.....C ｎ－１ The method of claim 12, wherein the ratio is ) / n-1.

14. The alarm is produced by X Ａ The method of claim 1, comprising notification when a sample deviates from a predetermined environmental baseline.

15. The method of claim 14, wherein the alarm production generates alarms for users of one or more animal epidemiological units, individually, aggregated, geographically located, stratified, temporally, clustered, universally, comparatively, and by reference.

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