Environmental infection stress

Abstract

A method of predicting the risk of disease in an epidemiological unit of a production animal by determining environmental infection stress (EPI). Determining the EPI includes establishing an environmental infection pressure threshold (EPit), constructing an environmental baseline (EBL), and generating a disease risk warning.

Description

Technical Field

[0001] The present invention relates to the field of animal health and also to the detection of environmental infection pressure in epidemiological units of production animals by applying a variety of molecular and epidemiological techniques, thereby generating an alarm. Background Art

[0002] Because air contains inorganic pollutants originating from different parts of the environment (such as soil, plants and water sources) as well as ABPs (airborne biological particles) such as bacteria, viruses, fungi, etc., air pollution represents a high environmental risk for humans and animals.

[0003] The likelihood of animal illness and its severity depends on factors such as the infectious potential of the pathogen and its degree of aerosolization. In livestock environments, such as livestock facilities, exposure to bacteria, viruses, fungi and parasites can significantly affect animal health, production efficiency and ultimately public health.

[0004] Currently, the problem of diseases caused by viruses and bacteria is usually addressed using: pharmaceutical prevention programs (e.g., the use of vaccines and antibiotics); diagnostic sampling through autopsies and blood sampling when the disease is suspected; and follow-up control of vaccination programs.

[0005] In many cases, the use of drugs is necessary and irreplaceable, as is the case with certain specific vaccines. However, effective vaccines are not currently available for all known and potential pathogens. In addition, the abuse of antimicrobial drugs leads to the development of antimicrobial resistance, which will pose a potential threat to animal and human health. The method proposed in the present invention has two major advantages over the above method: First, the method can be Applies to First, it can detect any incipient or emerging pathogens that occur in nature. Second, this method can detect biological threats as soon as they appear, thereby enabling timely treatment and reducing unnecessary use of antibiotics.

[0006] The main disadvantage of autopsy, saliva collection, cloacal sampling or blood sampling when the disease is suspected is that the above methods are usually carried out at the stage of disease development, that is, after the organism has shown symptoms or died, and therefore cannot solve the problem of preventive detection. In addition, some biological agents can cause death in animals, but their traces in the tissues do not remain long enough for subsequent detection, such as avian metapneumovirus. Therefore, the present invention proposes a sampling plan to detect pathogenic biological agents before disease symptoms appear.

[0007] Traditional methods for detecting infections in animals may have biases and / or limitations, for example in terms of the representativeness or diversity of pathogens detected and the time required to obtain results. Conventional microbiological techniques such as bacterial culture have been widely used for direct detection of pathogens. Although conventional techniques are simple and practical, their results may take days or weeks to obtain (such as in the case of mycoplasma) or cannot be applied because the number of unculturable microorganisms far exceeds that of culturable species. Serology based on serum samples from individuals with antigen-antibody detection (such as ELISA) may be too cumbersome or have low accuracy in detecting pathogens in samples because the concentration of the target pathogen is low if the population is at the beginning of infection (Hosseini et al., 2018), and serological tests may also be difficult to perform due to the presence of other microorganisms.

[0008] Regarding the control of vaccination programs by ELISA, the sampling methods used in such follow-up tests are usually invasive (in some cases even requiring the sacrifice of animals and the collection of blood samples), cumbersome to operate, and may not be highly representative in the early stages of infection. The method proposed in this application is non-invasive, easy to implement, and well representative of the sampling environment.

[0009] In addition, coupled detection methods such as ELISA tests using serology have some disadvantages, such as the need to wait for the immune response triggered by the pathogen to occur, as well as other conditions. The method described in the present invention has significant advantages and novelties compared to ELISA tests. These advantages are summarized as follows compared with existing methods: Figure 1 as shown in the table shown.

[0010] Other technologies have been applied to detect pathogens in samples, such as polymerase chain reaction (PCR) and next-generation sequencing (NGS), both of which have been shown to rapidly, comprehensively and accurately detect pathogens in environmental and clinical samples by using PCR to amplify nucleic acids of specific genes and using fluorescent probes or hybridization detection or qPCR identification to detect specific sequences, as described in patent application WO2021250274. On the other hand, as described in U.S. Patent No. 11,485,969B2, NGS can sequence DNA and / or RNA fragments on a large scale faster and at a lower cost than the Sanger sequencing method that has been used for a long time before NGS.

[0011] Current methods for sampling and subsequent detection of pathogens in the environment may have limitations in sensitivity, efficiency, and transportation logistics. The transportation of samples requires specific transport solutions, and the stability of pathogens in these solutions may be affected by water activity and other factors. Various air sampling devices have been used for communities. The Hirst-type device has become the preferred choice for monitoring communities, but the device cannot take viruses into account, so the device is not comprehensive in terms of functionality in addition to being expensive and inconvenient to carry. Polytetrafluoroethylene filters are also good, but there is still a lack of appropriate analytical methods for the entire air microbial sample (as described in patent WO2021250274A1). In addition, none of the above methods themselves can provide early warnings or reports that can be used by producers to predict the negative impacts of pathogens present in the environment.

[0012] In order to determine the presence of pathogens in the air, there are a series of devices and methods for capturing and analyzing microorganisms in the air, such as those described in patent applications WO2021250274A1, WO2022101510, US Patent Nos. 11,485,969B2 and UY38805, etc. However, these devices rely on laboratory methods with long cycles, which is not conducive to producers to respond promptly and reliably to animal health and production from an animal epidemiological perspective.

[0013] Regarding predictive technology or real-time risk identification, Boehringer Ingelheim Vetmedica GmbH has developed the "SoundTalks" technology for risk identification based on animal weight and voice changes. On the other hand, methods and computer systems for establishing disease risk indicators have been developed, such as those described in patent application CN112785198, US patent application publication number 2022136730, US patent application publication number 2021358632, and CN112986503, which can provide fast, networked information for reference, however, the above methods cannot detect specific pathogens, or have low sensitivity and specificity because the main focus of the above methods is to establish predictive models for environmental variables (such as air pollution or images), rather than animal population health based on epidemiological units.

[0014] Therefore, in order to ensure good care of the corresponding animal populations, it is necessary to establish a health management model that can maximize benefits at all stages. This model can ensure food safety and protect animal and human health by detecting whether animals are infected at an early stage and preventing the spread of infectious diseases.

[0015] The present invention describes a method that includes measuring environmental infection pressure by detecting the concentration of pathogens in the air. This method is similar to the method for detecting the airborne transmission of SARS-CoV-2 (type 2 coronavirus) (Krieguel et al., 2022), but the difference is that it can establish disease early warning for all pathogens in the production animal epidemiology unit through appropriate predictive statistical methods.

[0016] The present invention establishes a baseline of infection pressure, thereby predicting the occurrence of disease and generating early warning in epidemiological units of production animal populations. "Environmental infection pressure" in this application refers to the number of pathogenic microorganisms present in the air and their ability to infect biological populations within a specific time and space range.

[0017] It is worth noting that although the concept of “infection pressure” has been proposed in this field (Perea Gayosso, 2020), unlike this application, it was not applied at the population level based on air samples. Summary of the Invention

[0018] The present invention describes a method for generating early warnings by detecting environmental infection pressure in production animal epidemiological units. The method includes determining a baseline of environmental infection pressure by collecting environmental samples, extracting nucleic acids and performing molecular analysis, and is optimized using epidemiological and statistical techniques. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0020] Figure 2 is a comparison of the environmental sampling results and the cloaca samples.

[0021] Figure 3 A comparison of environmental avian bronchitis virus test results and cloaca samples.

[0022] Figure 4 is a comparison of the environmental avian bronchitis virus test results and the ELISA test.

[0023] Figure 5 Image of live microbial analysis count for the sampling module.

[0024] Figure 6 Table showing the detection limit results in sealed chambers.

[0025] Figure 7 The images show the DNA quantification results and different storage conditions.

[0026] Figure 8 Tabulate weekly surveillance results for each shed in the epidemiological unit.

[0027] Fig. 9 Table showing results for different stages of poultry production.

[0028] Figure 10 shows the early risk warning structure diagram: avian bronchitis and mycoplasma.

[0029] Fig.11 Constructing a map for the environmental baseline in the hatchery.

[0030] Figure 12 is an image of 16S metagenomic results from environmental samples at different stages of pig production.

[0031] Fig.13 An image of the indicator and report tree. DETAILED DESCRIPTION

[0032] definition

[0033] The "environmental infection pressure" (EPI) in this article refers to the concentration of a pathogen in the environment within a specific period of time, with the unit of concentration X.

[0034] The Environmental Infection Pressure Threshold (EPIt) in this article refers to the concentration of a pathogen in the environment that, when exceeded, causes infection or other health problems in animals. EPIt is also a validated environmental baseline.

[0035] An “environmental baseline” (EBL) is a graph of the environmental infection pressure of a pathogen in a specific environment over time.

[0036] "Abnormal data" refers to all values ​​that are far enough away from the typical data of the distribution of the variable under study. Abnormal data represents the occurrence of some kind of alarm, such as high mortality, low body weight, abnormal pathogen concentration, etc.

[0037] The term "CAPTUS" refers to the air sampling device described in Patent No. UY38805.

[0038] The term "epidemiological unit" in this article refers to one or more livestock production facilities that contain the same batches of animals and have the same hygiene management, feeding methods, security measures, etc.

[0039] The term "user" in this document refers to individuals or organizations that use the technology described in this document.

[0040] The term "epidemiology" in this article refers to the branch of epidemiology that focuses on the study of diseases in animals, especially in animal populations. It mainly studies the distribution of animal diseases, risk factors, transmission patterns, and the effects of diseases on animals. In addition, it analyzes the interactions between pathogens, host animals, and the environment, aiming to better understand animal diseases and develop appropriate prevention and control strategies.

[0041] The term "sample" in this context refers to one or more filters or membranes with controlled pore sizes obtained through a filtration process, which contain dust particles, microorganisms, traces of genetic material, etc., and are representative of a certain environment. The sample must contain information about the environment it represents, such as the time and duration of sampling, the name of the sampling environment, the location, the relevant production stage, the sampling batch (or similar information), the production user and other identifiers that can identify the sampling process.

[0042] The term “production stage” in this context refers to a stage in the animal farming production process distinguished by the age of the animals and / or the production processes associated with the animals.

[0043] The term “prediction” in this article refers to the ability to provide forecasts of health / production conditions based on data analysis techniques.

[0044] The term "division" in this context refers to an item or area, specifically the type of production animals, such as fattening pigs, fattening chickens or egg production, hatchery, etc.

[0045] The term "captive animals" as used herein refers to a population of animals that are kept during the production phase (usually for commercial purposes) in sheds or similar closed or semi-enclosed facilities.

[0046] The term “geolocation risk alert” in this article refers to an alert triggered by the detection of the presence of a microbial threat, an infectious disease outbreak, or a pathogen present in one location that could affect another location, depending on factors such as the type of threat, the transmission pathway, the degree of impact on the location, and the distance between the locations.

[0047] introduction

[0048] The invention enables to obtain information in a fast and reliable way in order to predict the occurrence of diseases in animal production epidemiological units in advance. The proposed solution describes a method that can provide timely and reliable early warning of diseases associated with specific pathogens to production animal users.

[0049] The proposed method involves measuring environmental infection pressure (EPI) and reporting early warnings based on environmental baselines (EBL). Such early warnings can provide decision makers with high-quality information to mitigate or completely avoid the impact of diseases, reduce the spread of diseases, and protect animal and human health through the implementation of effective, appropriate, and data-based prevention and management strategies.

[0050] This method includes the collection of environmental samples, storage and transportation of samples, classical microbiological testing and / or nucleic acid extraction and molecular analysis (such as sequencing, PCR, LAMP, etc.).

[0051] It is worth mentioning that although the concept of "infection pressure" has been proposed in this field (Perea Gayosso, 2020), it has not been analyzed at the population level based on air samples as in this application.

[0052] Although there are some specific devices and methods for determining the presence of pathogens in the environment, such as those described in patent applications WO2021250274A1, WO2022101510, and US11485969B2, the aforementioned devices and methods cannot provide an early warning of the occurrence of disease as an output of the process.

[0053] In addition, although U.S. Patent Publication No. 2020131509A1 provides a step-by-step method for DNA / RNA extraction and sequencing to determine the presence of biological populations in the air, the method does not include all the steps and specifications in the present application, namely, from sample collection to early warning reporting based on environmental infection pressure measurement methods.

[0054] Accordingly, from the perspective of animal epidemiology, although the monitoring and analysis of infection sources and epidemic risks are mentioned in patent application WO2012115601A1 and US patent application publication number 2021293817A1, the aforementioned patents do not include all the steps in this application, nor do they include steps for measuring environmental infection pressure, nor do they provide the warnings in this application. In addition, the aforementioned patents focus on individual-level measurements (WO2012115601Al), rather than population-level measurements, and the monitoring method is a fixed device, rather than the mobile device used in the present invention (UY38805).

[0055] The present invention helps users understand the changing behavior of any pathogen in an animal production environment. For example, the changes in pathogens in different seasons can be quickly and accurately compared. Users can receive early warnings from the first day of applying the present invention so that users can take preventive and corrective measures to reduce the impact of the disease on animal health.

[0056] Method Description

[0057] The currently applied approach to predicting disease risk in epidemiological units of production animals is by establishing an environmental baseline.

[0058] The environmental baseline is a tool that allows studying the behavior of a pathogen over time in a specific location or farm through changes in the environmental infection pressure. The environmental baseline also allows the determination of critical concentration limits for the pathogen, above which a risk may be posed to production animals. This critical limit is determined by evaluating extreme outliers, i.e. values ​​outside the boundaries. In this way, an early warning can be issued to the user when the concentration of a pathogen at a certain moment, on a certain farm or location exceeds the set critical limit.

[0059] Specifically, this method follows the following steps: determine the environmental infection pressure (EPI), establish the environmental infection pressure threshold (EPIt), construct the environmental baseline (EBL) and generate disease risk warning.

[0060] 1. Determine the environmental infection pressure (EPI): For a pathogen A in a certain epidemiological unit, follow the steps below to determine its specific concentration (X A ):

[0061] Standardized sampling (at a certain time and under certain conditions) and sampling using air sampling equipment of the type defined in patent application UY38805. Obtain filters (samples) containing dust particles, microorganisms and their genetic material.

[0062] The samples were stored and transported to the laboratory at room temperature without the addition of stabilizing solution and remained stable for 72 hours.

[0063] The sample is extracted through a series of physical and chemical methods to maximize the amount of genetic material extracted from the filter, including bead shaking, enzymatic digestion, centrifugation and / or other validated auxiliary extraction methods to ensure performance. The above steps can effectively improve the sensitivity of subsequent analysis at the small sample level and ensure the representativeness of the sample by minimizing bias and maximizing the possibility of detecting pathogen diversity in the sample. Typically, the sample volume with DNA or RNA is less than 100μl, which is used for a variety of analyses. Quantification of DNA / RNA is performed by fluorescence analysis and / or absorbance spectrophotometry. The information obtained from the sample is expressed in nanograms per cubic meter (ng / m 3 )express.

[0064] In one form, DNA or RNA is extracted from a sample and then quantitative real-time PCR (qPCR) is performed using simple or multiplex fluorescent probes to obtain C i This value is related to the number of pathogen A copies in the farm / site. This information obtained from the sample is denoted as X A, the unit is copies / m3 3 ).

[0065] 2. Determine the environmental infection pressure threshold (EPIt): for a certain pathogen A, determine its concentration (u A ). The EPIt for each pathogen is specific and varies with the epidemiological unit. This value is established in field tests, i.e., empirical tests, by the following method:

[0066] Select an epidemiological unit where a precursor of pathogen A is suspected to exist, but the unit does not belong to an endemic infectious disease area and there is no possibility of over-representation.

[0067] Select farms or sites with available historical production data (e.g. mortality, birth rates, weights, animal condition, etc.), serological data, and / or symptom data.

[0068] Conduct field tests on selected farms and measure the concentration (X) in the air of the site weekly according to the method in step 1 (EPI) A ).

[0069] During the field trials, serological tests and / or production data measurements (such as mortality, birth rate, weight, animal condition, etc.) and / or symptom identification are performed.

[0070] By X A Transformation of attributes and production data to study the correlation between the two (e.g., high mortality events and high pathogen load X A The correlation of events) is used to obtain the EPIt threshold X A =u A , and divide the level values ​​that significantly affect production efficiency.

[0071] 3. Environmental baseline (EBL): For an epidemiological unit of a farm or production site, or a production stage of a production department that people want to know about, the environmental infection pressure (EPI) needs to be measured at different times (t), such as weekly measurement of pathogen A concentration in the air (X A ). The Environmental Baseline (EBL) will be unique over time for each disease and each location, and will be continuously adjusted as new data are added.

[0072] The Environmental Baseline (EBL) allows users to understand changes in the behavior of a pathogen, providing users with early warnings, and is established in the following ways:

[0073] If there is no X at time (t) Adata, the EBL of the first sampling period is:

[0074] EBL1=u A , according to step 2 above.

[0075] During the second sampling period, the baseline is:

[0076] EBL2=Q32+C1×RIC2,where:

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

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

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

[0080] Starting from the third sampling period, the baseline takes the following form:

[0081] EBL=Q3 i +C i ×RIC i ,in:

[0082] Q3 i is the value of the ordered data accumulated to 75% in the i-th sampling period, RIC i is the interquartile range in the i-th sampling period, C i is the reference coefficient in the i-th sampling period, where:

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

[0084] To summarize and express it another way:

[0085] For sampling period 1, the reference value is:

[0086] EBL1=u A

[0087] For each sampling period: EBL t+1 =Q3 t+1 +C t ×RIC t+1 , applicable to sampling period t+1, where C t =(C1+C2+...+C t-1 ) / t-1.

[0088] At the end of each sampling period, study A new coefficient C t+1 , and add it to the average value of the next sampling period:

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

[0090] 4. Build an early warning system based on established EBLs or situations that require notification to users. Some of the alerts are listed in detail below:

[0091] Early risk alert: When the sample concentration of a pathogen A exceeds the set value of EBL, the user of the epidemiology unit is notified. In the initial application, this set value is equal to EPIt, that is, the initial alert is based on the known information of A, not X. A The advantage of this approach is that alerts can be issued from the first day of application and then adjusted according to the actual situation of each epidemiological unit.

[0092] Geographic risk alerts: When a disease or pathogen is detected in a certain location with early risk alerts and is highly relevant to the user's unit, an alert is sent to users in the vicinity. This alert is constructed using geo-referenced data associated with specific pathogen loads or concentrations at different locations, while keeping the exact source address of the pathogen anonymous.

[0093] Common Environmental Baseline (EBL u ): It is constructed from all data from farms or institutions in the same production stage and the same production industry. It consists of the weighted average (by number of measurements) EBL, calculated as: EBL u=i=1n(EBLi / n) / (Obsi / Total Obs), where i represents a specific epidemiological unit, n is the total number of epidemiological units belonging to the same production stage and production industry, and Obs i is the number of observations in epidemiological unit i (X A Total Obs is the total number of observations made on all farms in that stage and production industry. The EBL is universal and is adjusted, updated and optimized with the input of monitoring data, eventually forming a more accurate reference indicator in the industry.

[0094] Example

[0095] Worked Example 1: Environmental and Cloaca Samples

[0096] To prove that environmental samples are representative as indicators of changes in the animal body, two groups of broiler chickens were used as subjects for comparative testing. Here, the test results of the two sampling methods of cloaca samples and air environment samples in terms of species genetic diversity and the corresponding relationship between the two are shown. In both sets of tests, DNA of broiler chickens was extracted and 16S gene sequencing was performed for species genetic diversity analysis. According to the test results, there were common bacterial species in the two groups of samples and unique bacterial species obtained for each sample. In the environment where broiler chickens are raised, the correlation R2 of common bacterial species between cloaca samples and air samples reached 0.6 to 0.9. This shows that air samples are highly representative of cloaca samples and verifies the effectiveness of air sampling methods in animal microbial environmental monitoring.

[0097] Worked Example 2: Detection of Avian Infectious Bronchitis Virus in Environmental and Cloacal Samples

[0098] Avian infectious bronchitis virus (IBV) is a preferentially airborne pathogen that is present in different poultry farms and time periods. In a surveillance experiment for IBV, results showed that less than 50% of tissues in the cloaca samples tested positive for air samples by qPCR. This suggests that CAPTUS sampling is more accurate than cloaca sampling for assessing the presence of preferentially airborne pathogens. The latter can be more cumbersome, invasive, and ultimately less representative.

[0099] Worked Example 3: Detection of Avian Infectious Bronchitis Virus by Environmental PCR and Autopsy

[0100] In the event that a pathogenic IBV strain was detected through environmental air testing, three tracheal samples from infected birds on the same farm were sent to the laboratory one week after the birds had been infected and tested using the same qPCR method, according to the pathogenesis described by the veterinarian responsible for animal health at the site. The results showed that all tracheal samples tested negative for infection. This shows that weekly environmental monitoring can detect the dynamics of the pathogen in a timely manner, while if testing is attempted after symptoms appear, it may be more difficult to identify the pathogen causing the disease.

[0101] Worked Example 4: Detection of Avian Infectious Bronchitis Virus by Environmental Monitoring and ELISA

[0102] The association between the presence of pathogenic IBV (SAI and SAII strains) and the serological response of the flocks was evaluated in the chicken houses. Environmental sampling was performed in the chicken houses for 6 weeks, and blood was sampled for serological testing by ELISA at the end of the sampling period. The serological profile of farms that tested positive for pathogenic IBV by qPCR showed high and uneven distribution of ELISA values, while farms that tested negative for IBV showed a uniform distribution of ELISA values, consistent with vaccination and freedom from IBV. In addition, the example shows the test results of positive environmental samples in this case compared to positive controls. This shows a correlation between infection events that can be detected weeks in advance and the effectiveness of immunity, consistent with the history of infection.

[0103] Worked Example 5: Analytical Sampling Module – Active Microbial Counts

[0104] Air samples are obtained by CAPTUS air sampling devices and the samples on the filter are inoculated directly on culture plates to analyze the growth of different types of microorganisms, such as aerobic bacteria, fungi, yeasts, enterobacteria or other specific communities, thus achieving the counting of active microorganisms. By image analysis, the number of colonies growing in the filter can be counted manually or automatically after an appropriate time (depending on the type of microorganisms in the sample and their density). The example shows the growth of aerobic microorganisms in the filter at 1m after continuous sampling for 10 minutes. 3 Environmental pollution caused by air. In this case, the direct count of microorganisms in the filter can be expressed as the number of aerobic microorganisms per cubic meter and can be used as a numerical indicator of environmental pollution.

[0105] Worked Example 6: Detection Limits in a Closed Chamber

[0106] To demonstrate the sensitivity of this method under controlled conditions, a variety of target microorganisms related to the animal and food production industries were dispersed in a closed chamber. The microbial suspension was vaporized by ultrasound to produce suspended particles, and the dispersed microorganisms were sampled for a standard 10-minute period using CAPTUS. Different dilutions from millions of microorganisms per cubic meter to tens of microorganisms were tested. The results showed that when the dilution reached about 100 microorganisms per cubic meter (or higher), it could be successfully amplified by targeted qPCR for these pathogens or vaccines.

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

[0108] In terms of the efficiency of DNA extraction from air samples, the dry filter was nearly 3 times higher than the filter extracted from viral transport medium (VTM), and had better stability at room temperature for 72 hours. remove Dry storage is simple and convenient , dry extraction also has advantages in transportation and storage.

[0109] Notably, better CT results were obtained when using dry filters for qPCR compared with using MTV filters, which can increase the detection sensitivity of RNA viruses (such as vaccinia IBV) by 100- to 200-fold.

[0110] Worked Example 8: Developing Convergence Standards

[0111] Within the same epidemiological unit, observations regarding IBV virus surveillance in air samples showed the spread of the pathogen from one farmhouse to another in less than a week.

[0112] Environmental monitoring for the presence of IBV and other pathogens was performed weekly using the methods described in this invention. The monitoring covered three separate farms that belonged to the same epidemiological unit. Each farm had 4,000 or more breeder chickens. Samples were sent to the laboratory and tested for non-vaccine IBV strains using the qPCR method. At each detection of an IBV wild strain infection, two or more infected farms were tested for the presence of the strain, with an interval of no more than one week. This means that the infection appeared from the first farm, or was initially found in 2 to 3 farms, or appeared in a farm different from the first farm within the following week. This monitoring reflects the spread of pathogens within the epidemiological unit and the ability of this method to perform environmental monitoring based on the infection dynamics of the virus.

[0113] Worked Example 9: Traceability between environments and stages

[0114] In this example, the relationship between the presence of Salmonella in the air and subsequent hatching and egg-laying stages was observed, with contamination from the farm air found to continue to the hatchery and eventually to the surface of the eggs. Environmental monitoring (including air sampling and cloaca sampling) was performed on four laying hens, testing for Salmonella spp. (and subsequent typing of S. Enteritidis and S. Typhimurium), while air monitoring was also performed in the hatchery and controls were performed on the surface of the eggs. This example shows the progression from no Salmonella in the environment and in the cloaca of the animals in the first week, to the first detection of Salmonella in the farm air during the next sampling date, and the presence of Salmonella in the hatchery air, to the observation of Salmonella in more farm and hatchery environments, as well as on the surface of eggs.

[0115] Worked Example 10: Risk Warning

[0116] Viruses and bacteria may interact during infection, leading to greater production losses, as co-infections or back-to-back infections can exacerbate disease. In the example below, Mycoplasma synoviae (Mycoplasma S) and IBV SAI strains were first detected on the farm. The co-occurrence of these two pathogens, known to be synergistic, coincided with a peak in mortality between weeks 35 and 36 that did not correspond to a heat spike or other factors. In week 35, an alert was issued for IBV-SAI in the presence of Mycoplasma synoviae, which became a management tool for producers to avoid the consequences of a co-infection peak (as shown, after the detection of IBV-SAI Mycoplasma in the environment rose again, other pathogens such as Salmonella also began to rise in the following weeks). In subsequent events, producers applied antibiotics to respond to secondary infections by recording the peak in IBV-SAI detection, avoiding the greater impact of bacterial infection in the case of non-vaccine BVI. This suggests that weekly environmental monitoring of pathogens could provide early warning at least a week before a peak in deaths, allowing for more effective treatments such as antibiotics and analgesics.

[0117] Worked example 11: Environmental benchmarking in a hatchery

[0118] Air environment data obtained through counting can be used to reconstruct pollution levels at the facility and generate corresponding benchmarks.

[0119] In this example, counts of aerobic microorganisms (UFC / m 3 Based on these data, a scatter plot was constructed based on the sampling date, and the critical value benchmark was set according to the following criteria: 16 UFC / m 3(black line), the benchmark is based on the 3xRIC upper boundary: Q3-3xRIC, where Q1 and Q3 are the 25th and 75th quartiles, respectively, and RIC is the interquartile range. Data monitoring in this way can alert producers in reports that contamination levels may be outside the normal range. In the following example, only two weeks of reports had outliers (above the baseline). It is worth noting that in this monitoring, the data values ​​for the facility "after cleaning" were lower, and the monitoring and alert system settings allowed the cleaning plan to be adjusted.

[0120] Worked Example 12: Longitudinal traceability of a single-site farm

[0121] In this example, air environmental sampling was performed at six different production stages (including pregnancy, farrowing, fattening, and breeding stages) on the same pig farm. The physical distance between the production stages ranged from tens to hundreds of meters, but they all belonged to the same connected management unit. Through large-scale sequencing, the diversity of airborne bacteria at different taxonomic levels was examined, and the sharing between different stages was analyzed. The environmental diversity exhibited by each stage ranged from dozens to hundreds of species, which were classified as major categories, and these categories accounted for more than 80% of the relative abundance in all samples and were shared among all production stages. At the species level, the 10 to 20 most abundant species were shared between different stages, but their relative abundance and ranking order varied. The extent of bacterial sharing in this environmental analysis reveals microbial phenomena across production stages at the farm community level.

[0122] When compared with databases created for different pig production stages, these metagenomic analysis results allowed reporting of airborne biota indices for each stage.

[0123] Worked Example 13: Metrics Tree and Report

[0124] The following diagram details the different types of indicators used to create epidemiological reports, which in turn can help users take preventive or corrective actions.

[0125] References

[0126] Hosseini, S., Vazquez-Villegas, P., Rito-Palomares, M., Martinez-Chapa, S.O. (2018) Advantages, Disadvantages and Improvements of Traditional ELISA. In: Enzyme-Linked Immunosorbent Assay (ELISA). SpringerBriefs Applied Science and Technology Series (). Springer Publishers, Singapore. https: / / doi.org / 10.1007 / 978-981-10-6766-2_5

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

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Claims

1. A method for predicting disease risk in a production animal epidemiological unit, the method comprising: a) Determine the environmental infection pressure (EPI); b) Establishing the Environmental Infection Pressure Threshold (EPIt); c) Building an Environmental Baseline (EBL); and d) Generate disease risk warning.

2. The method according to claim 1, wherein the determination of the environmental infection pressure comprises determining a value X for calculating an epidemiological unit for production animals. A , where X is the concentration value and A is the pathogen value.

3. The method according to claim 2, wherein the value X A Expresses pathogen concentration measured in parts per cubic meter.

4. The method according to claim 2, wherein the value X A It is obtained by applying qPCR to DNA or RNA extracted from air samples.

5. The method according to claim 1, wherein the determination of the environmental infection pressure threshold (EPIt) comprises calculating a value u of a production animal epidemiological unit A , where U is the concentration value, indicating the concentration at which the pathogen is able to infect / affect the health of the animal, and A is the pathogen value.

6. The method according to claim 5, wherein the value u A Represents the threshold concentration of a pathogen measured in parts of the pathogen per cubic meter.

7. The method according to claim 5, wherein the value u A is the key production value from a representative epidemiological unit with pathogen X A The key production value is obtained from the correlation between events, and is selected from mortality, body weight, number of births, feed conversion rate.

8. The method according to claim 5, wherein the value u A =Equivalent to the environmental infection pressure threshold of pathogen A at the concentration that affects animal production, where EPIt is X A =u A .

9. The method according to claim 1, wherein the determination of the environmental baseline value (EBL) comprises a value X for calculating the change of a disease in the epidemiological unit of production animals over time (t). A , where X is the concentration value and A is the pathogen value. Specifically, if there is no X at time (t), A data, the environmental baseline is u A .

10. The method according to claim 1, wherein the determination of the environmental baseline value (EBL) comprises a value X for calculating the change of a disease in an epidemiological unit of production animals over time (t). A , where X is the concentration value and A is the pathogen value, and there is no X at time (t) A data, and the environmental baseline value (EBL) is u A .

11. The method according to claim 10, wherein for the second sampling period, EBL2 = Q32 + C1 x RIC2, wherein Q32 is the cumulative value of 75% of the ordered data in the second sampling period, RIC2 is the interquartile range of the ordered data in the second sampling period, and C1 = (u A -Q31) / RIC1, where u A is the determined pathogen concentration, Q31 is the cumulative value of 75% of the ordered data in the first sampling period, and RIC1 is the interquartile range of the ordered data in the first sampling period.

12. The method according to claim 11, wherein for the third sampling period, EBL3 = Q33((C1+C2) / 2)xRIC3, ​​wherein Q33 is the cumulative value of 75% of the ordered data in the third sampling period, RIC3 is the interquartile range of the ordered data in the third sampling period, and C1 = (u A -Q31) / RIC1, where u A is the pathogen concentration, Q31 is the cumulative value of 75% of the ordered data in the first sampling period, RIC1 is the interquartile range of the ordered data in the first sampling period, C2 = (u A -Q32) / RIC2, where u A is the pathogen concentration, Q32 is the cumulative value of 75% of the ordered data in the second sampling period, and RIC2 is the interquartile range of the ordered data in the second sampling period.

13. The method according to claim 12, wherein for each sampling period after the third sampling period, EBL i =Q3 i +C i x RIC i , of which Q3 i is the cumulative value of 75% of the ordered data in the i-th sampling period, IQR i is the interquartile range of the ordered data in the i-th sampling period, C i is the reference coefficient in the i-th sampling period, where C i =(C1+C2+...+C n-1 ) / n-1.

14. The method of claim 1, wherein generating an alert comprises: A Notification is issued when the value of a sample differs from a previously determined environmental baseline value.

15. The method of claim 14, wherein generating an alert comprises alerting one or more users of an epidemiology unit, the alerts being individual, aggregated, geo-located, hierarchical, time-based, clustered, general, comparative, and informative.

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