Systems and methods for monitoring disease in animal populations

Abstract

The disclosure provides a computer-implemented method of determining the prevalence of a parasite or pathogen in an animal population. The method comprises: a) obtaining, based on a clinical sample or

Description

Technical Field The present invention relates to systems and methods for tracking the spread of diseases through animal populations, as well as determining the efficacy of veterinary interventions in the animal population. Background Antibiotics are critical drugs in the area of infection management. However, drugs that used to be standard treatments for bacterial infections are now less effective or don't work at all. When an antibiotic drug no longer has an effect on a certain strain of bacteria, those bacteria are said to exhibit antimicrobial resistance (AMR). The overuse and misuse of antibiotics are key factors contributing to antibiotic resistance. In spite of a growing awareness of antibiotic resistance in recent years, overuse still occurs. The bulk of antimicrobials is, in fact, not consumed by humans, but rather given to animals, mostly food-producing animals such as poultry and cattle. Thus farm animals can be exposed to considerable quantities of antimicrobials during their lifetime and can potentially act as an important reservoir of AMR genes. The diagnosis of bacterial infection and the appropriate antibiotic treatment requires a labbased test which takes 2-3 days. Given the costs and the need to treat an unwell animal, farmers instinctively reach for their stock of antibiotics and treat without prior analysis. This approach to treatment can lead to inappropriate or ineffective administration of antibiotics, which promotes the development of AMR. In addition, gastrointestinal nematodes (GINs) are a major cause of morbidity, mortality and lost production in grazing flocks globally. For decades anthelmintics have been used as the primary control measure for worm infections in livestock. The classes of broad-spectrum anthelmintics range from benzimidazoles, imidazothiazoles / tetrahydro pyrimidines and macrocyclic lactones, plus salicylamides, phenolic substitutes and organophosphates. Broad-spectrum anthelmintics are more commonly used in ruminants because they are capable of eliminating large numbers of parasites, besides being of easy administration and safe to the hosts. However, there has been continuous development of Anthelmintic Resistance (AR) by the parasitic worms infecting livestock. AR can be defined as the ability of parasites to survive doses of drugs that would normally kill parasites of the same species and stage. It is inherited and selected for because the survivors of treatments pass genes for resistance onto their offspring. These resistant genes are initially rare in the population or arise as rare mutations, but as selection continues, their proportion in the population increases as does the proportion of resistant parasites. The annual cost associated with parasitic diseases has been estimated at $1 billion in Australia, $7.11 billion in Brazil, and believed to be tens of billions of dollars worldwide. Global public health is also facing two other fundamental challenges: • The impact of climate change and massive migration waves appear to be driving the presentation of infectious diseases in parts of the world where they had not previously been present. • Numerous academic studies have indicated a growing trend of zoonotic infection, i.e. an infectious disease that has transitioned from a vertebrate animal to humans. Zoonotic pathogens can be bacterial, viral or parasitic, and can spread to humans by direct contact with domestic, agricultural or wild animals, or through food and water. They can cause many different types of illness in people ranging from mild to serious, and even death. Some of the most dangerous zoonotic diseases include COVID-19 and avian flu, salmonellosis and the Ebola virus. It is against these growing threats that the current response to the spread of infectious diseases needs to be evaluated. In recent years, there has been an increase in the implementation of both cloud-based technology and application software, to address a range of technical problems. Furthermore, through the provision of increased computing power and access to increasingly large quantities of suitable training data, machine learning techniques may provide improved solutions to many technical problems. Biosensors, either in a wearable or implantable form, are becoming important tools in the support of precision livestock farming, providing timely diagnosis of disease and a decrease in economic losses for the farmer. Summary of Invention Embodiments of the present invention aim to address at least some of the above problems. In an aspect there is provided a computer-implemented method of determining the prevalence of a pathogen or parasite in an animal population, the method comprising: a) obtaining, based on a clinical sample or biosensor data from an animal in the animal population, an indication of whether the animal is infected with a pathogen or parasite; b) obtaining an identifier of the animal; and c) obtaining one or more parameter values associated with the status of the animal; repeating steps a) to c) for a plurality of animals in the animal population; determining, based on the obtained data, one or more relationship values indicating an association between the prevalence of the pathogen or parasite and the one or more parameters. This may enable the correlations between various parameters or characteristics of animals and the prevalence of a pathogen (or parasite) to be determined and tracked. This information may in turn be used to inform treatment decision making. The method may further comprise determining a treatment and / or a treatment plan based on the determined relationship values. For example the method may comprise determining a treatment and / or a treatment plan for an animal and / or a subgroup of animals of the population, for example based on the parameter values associated with those animals and the determined relationship values. The method may further comprise administering the treatment or treatment plan to the animal(s). The method may further comprise predicting the prevalence of the pathogen or parasite in a second animal population based on the association between the prevalence of the pathogen or parasite and the one or more parameters. The parameter may comprise an indication of whether the animal has been administered with one or more antimicrobials or anthelmintics. The one or more relationship values may indicate the efficacy of one or more antimicrobials or anthelmintics against the pathogen or parasite. The parameter may comprise an indication of whether the animal has been administered with one or more anthelmintics. The one or more relationship values may indicate the efficacy of one or more anthelmintics against the parasite. The parameter may comprise an indication of whether the animal has received a vaccine against the pathogen. The one or more values may indicate the efficacy of the vaccine against the pathogen. The parameter may comprise the genomic profile of the animal. The one or more relationship values may indicate the susceptibility of infection by the pathogen for one or more genomic profiles. The parameter may comprise an indication of the geographical location of the animal, which may comprise precise location, topography, soil type and predominant vegetation of the location. The one or more relationship values may indicate the extent of the geographical spread of the pathogen or parasite within the population. The parameter may comprise other geographical or geological information relating to the animal or linked to the geographical location. For example, the parameter may comprise the topography of the location, the soil type in the location, and / or the prevalent vegetation in the geographical location of the animal. The determined one or more relationship values may indicate the prevalence of the spread of the pathogen or parasite within the population with respect to these parameters. The method may further comprise outputting a signal indicating the one or more values to a network. The output signal may comprise an alert, for example the alert may be provided in response to a determination that the prevalence of the pathogen or parasite is above a threshold level. The method may further comprise identifying, based on the parameter values and / or the determined values, one or more animals in the population to be treated with an antimicrobial or anthelmintic. The method may further comprise identifying, based on the parameter values and / or the determined values, a particular antimicrobial with which to treat one or more animals in the population. The method may further comprise identifying, based on the parameter values and / or the determined values, a particular anthelmintic with which to treat one or more animals in the population. The method may further comprise, for each of the plurality of animals: d) associating predictive algorithms as digital proxies for phenotypic outcomes. The determining of one or more relationship values may be further based on said associating. In another aspect there is provided a method of determining the efficacy of an antimicrobial against a pathogen in an animal population, the method comprising: a) obtaining, based on a clinical sample from an animal in the animal population, an indication of whether the animal is infected with the pathogen; b) obtaining an identifier of the animal; and c) obtaining an indication of whether the animal has been administered with one or more antimicrobial drugs; repeating steps a) to c) for a plurality of animals in the animal population; determining, based on the obtained data, the efficacy of the one or more antimicrobial drugs against the pathogen. The method may comprise for each of the plurality of animals, a value for one or more further parameters associated with the status each animal; and determining based on the obtained values, a relationship between the efficacy of the one or more antimicrobial drugs and each of the one or more further parameters. The method may further comprise identifying an animal to be treated with the one or more antimicrobial drugs based on the determined relationship. Identifying an animal to be treated may comprise obtaining, for the animal, a value for one or more of the further parameters associated with that animal. In another aspect there is provided a method of determining the efficacy of an anthelmintic against a parasite in an animal population, the method comprising: a) obtaining, based on a clinical sample from an animal in the animal population, an indication of whether the animal is infected with the parasite; b) obtaining an identifier of the animal; and c) obtaining an indication of whether the animal has been administered with one or more anthelmintic drugs; repeating steps a) to c) for a plurality of animals in the animal population; determining, based on the obtained data, the efficacy of the one or more anthelmintic drugs against the pathogen. The method may comprise for each of the plurality of animals, a value for one or more further parameters associated with the status each animal; and determining based on the obtained values, a relationship between the efficacy of the one or more anthelmintic drugs and each of the one or more further parameters. The method may further comprise identifying an animal to be treated with the one or more anthelmintic drugs based on the determined relationship. Identifying an animal to be treated may comprise obtaining, for the animal, a value for one or more of the further parameters associated with that animal. In another aspect there is provided a method of determining the efficacy of a vaccine against a pathogen in an animal population, the method comprising: a) obtaining, based on a clinical sample from an animal in the animal population, an indication of whether the animal is infected with the pathogen; b) obtaining an identifier of the animal; and c) obtaining an indication of whether the animal has been administered the vaccine; repeating steps a) to c) for a plurality of animals in the animal population; determining, based on the obtained data, the efficacy of the vaccine against the pathogen. In another aspect there is provided a method of determining an antimicrobial treatment for an animal in an animal population, the method comprising: obtaining an identifier for the animal; obtaining, based on the identifier, a value for one or more parameters associated with the animal; obtaining, based on a clinical sample from the animal, an indication of whether the animal is infected with a pathogen; determining, based on the indication and a predetermined relationship between each of the one or more parameters and the efficacy of the antimicrobial, whether to treat the animal with an antimicrobial. The method may further comprise treating the animal with the antimicrobial. In another aspect there is provided a method of testing an animal for determining treatment, the method comprising: obtaining from an animal test subject, a clinical sample and an associated identifier for the animal; determining, based on the clinical sample, whether the animal is infected with a pathogen; obtaining, based on the identifier, a data set comprising values for one or more parameters associated with the status of the animal; adding an indication of whether the animal is infected with the pathogen to the data set. The method may further comprise determining, based on the data set and a predetermined relationship between one or more of the parameters and the efficacy of a treatment, whether to provide that treatment to the animal, and / or may comprise determining a treatment plan for the animal. The method may further comprise administering the determined treatment or treatment plan to the animal. The one or more parameters may comprise at least one of whether the animal has been administered with one or more antimicrobials; whether the animal has received one or more vaccines; the species of animal, the genomic profile or subgroup of the animal; the geographical location of the animal, the size, age, gender of the animal, amongst others. In examples relating to parasites, in particular internal parasites such as worming, the parameters may be: whether a particular worming treatment that has been administered to the animal, a measured anthelmintic resistance of the animal, geography of resistance and geographic location of the animal, genetic susceptibility or resistance to parasitic burden. In examples relating to parasites, in particular external parasites such as fleas / ticks / flies, the parameters may be: whether a particular ectoparasite treatment has been administered, a measured anti-ectoparasite resistance, geography of resistance and geographic location of the animal, genetic susceptibility or resistance to parasitic burden. In another aspect there is provided a method of training an artificial neural network for determining the efficacy of a treatment against a pathogen or parasite, the method comprising: implementing, as a neural network, a model configured to provide an association in an animal population between: a pathogen or parasite which has infected a portion of the animal population; one or more parameters associated with the status of the animals in the animal population; obtaining a data set for an animal in the population, the data set comprising: one or more values of the parameters for that animal; and an indication of whether the animal has been infected by the pathogen or parasite; and modifying the model based on the obtained data set. In another aspect there is provided a computer-implemented method of determining a characteristic of a pathogen or parasite in an animal population, the method comprising: a) obtaining, based on a clinical sample from an animal in the animal population, an indication of the genomic profile of a pathogen or parasite with which the animal is infected; b) obtaining an identifier of the animal; and c) obtaining one or more values of parameters associated with the status of the animal; repeating steps a) to c) for a plurality of animals in the animal population; determining, based on the obtained data, one or more values indicating an association between the genomic profile of the pathogen or parasite and the one or more parameters. In another aspect there is provided a computer-implemented method of determining one or more fertility characteristics of an animal population, the method comprising: a) obtaining, based on a clinical sample from an animal in the animal population, an indication of whether an animal in the population is suitable for breeding; b) obtaining an identifier of the animal; obtaining an identifier of the animal; repeating steps a) to c) for a plurality of animals in the animal population; determining, based on the obtained data, one or more values indicating an association between the fertility and the parameters. In some examples of the above aspects, the animal population may be a population of cattle. In such examples the pathogen may be one of: BVD, Leptospirosis, Johnes, TB IBR, Salmonella, Mastitis, E. coli, Klebsiella, Streptococcus uberis, Staph aureus, Coagulase negative staphs, Bovine Respiratory Disease, Mannheimia spp, Mycoplasma, Bluetongue Virus, Rabies, Q fever (coxiella burnetii), EBL, FMD. In examples which involve tracking a parasite in a cattle population, the parasite may be one of: Ostertagia ostertagi, Cooperia oncophera, Dictyocaulus viviparus (lungworm), Fasciola hepatica (fluke), Lice (various species), Ticks (ixodes spp), Midges (culicoides spp), Mange mites (chorioptes etc), Flies In other examples of the above aspects, the animal population may be a population of sheep. In such examples the pathogen may be one of: Gl Parasites, OPA, Border Disease, MV (Maedi Visna), Johnes disease. In examples which involve tracking a parasite in a sheep population the parasite may be one of: Ostertagia ostertagi, Cooperia oncophera Dictyocaulus viviparus, Fasciola hepatica (fluke), Lice (various species), Ticks (ixodes spp), Midges (culicoides spp), Mange mites (psoroptes ovis-scab), Flies (blowfly strike). In other examples of the above aspects, the animal population may be a population of pigs. In such examples the pathogen may be one of: PRRS, Enzootic pneumonia, Swine Dysentry, African Swine Fever, Porcine Epidemic Diarrhoea (PED). In examples which involve tracking a parasite in a pig population the parasite may be one of: Ascaris roundworms, Trichuris, Oesophagostomum spp, Sarcoptic mange. In other examples of the above aspects, the animal population may be a population of horses. In such examples the pathogen may be one of: Strangles, Strep Equi. In examples which involve tracking a parasite in a horse population, the parasite may be one of: Strongyle redworms, Small redworms (cyathostomiasis), Tapeworm, Mange mites, Flies, Lice. In other examples of the above aspects, the animal population may be a population of birds. In such examples the pathogen may be avian flu. In examples which involve tracking a parasite in a bird population, the parasite may be lice. In other examples of the above aspects, the animal population may be a population of dogs. In such examples the pathogen may be one of: Rabies, Parvovirus, Brucella canis, Canine Herpes virus, Parainfluenza virus, Bordetella bronchisepta, Leptospirosis, Leishmania, Ehrlichia, Salmonella, Campylobacter, canine adenovirus, Borrelia Burgdorferi, Neospora. In examples which involve tracking a parasite in a dog population, the parasite may be one of: Roundworms, Tapeworms, Fleas, Ticks, Lice, Lungworm, Heartworm, Coccidia, Giardia, Sarcoptes. In other examples of the above aspects, the animal population may be a population of cats. In such examples the pathogen may be one of: FeLV, FIV, Feline Herpes Virus, Feline Calicivirus, Chlamydia felis, Mycoplasma haemofelis, Feline Coronavirus (FIP). In examples which involve tracking a parasite in a cat population, the parasite may be one of: Roundworms, Tapeworms, Fleas, Ticks, Lice, Lungworm, Tritrichomonas foetus, Toxoplasma, Coccidia. In addition, although the present disclosure refers to animal populations, it will be appreciated that the disclosed methods could also be applied to human populations. Aspects of the present disclosure may provide computer program products comprising computer program instructions configured to program a controller to perform any method disclosed herein. Brief Description of Figures Some examples of the present disclosure will now be described, by way of example only, with reference to the figures, in which: Figure 1 is a flow chart illustrating an example method of determining the prevalence of a pathogen in an animal population; Figure 2 is a flow chart illustrating an example method of determining the efficacy of an antimicrobial against a pathogen in an animal population; Figure 3 is a flow chart illustrating an example method of determining the efficacy of a vaccine against a pathogen in an animal population; Figure 4 is a flow chart illustrating an example method of determining an antimicrobial treatment for an animal in an animal population; Figure 5 is a flow chart illustrating an example method of testing an animal for determining treatment; Figure 6 is a flow chart illustrating an example method of training an artificial neural network for determining the efficacy of a treatment against a pathogen; Figure 7 is a flow chart illustrating an example method of determining a characteristic of a pathogen in an animal population; Figure 8 is a flow chart illustrating an example method of determining one or more fertility characteristics of an animal population; Figure 9 is a diagram which illustrates example combinations of parameters which may be used in the methods described; Figure 10 is an example of a map which may aid in indicating the geographical spread of a disease. Specific Description The present disclosure relates to systems and methods which enable the tracking of various characteristics of an animal population, for example the spread of a pathogen. One or more parameters relating to characteristics of each animal in the population may be recorded, and a clinical sample taken from multiple animals may be analysed and used to track the prevalence or spread of a pathogen or parasite with respect to the various parameters. This information can subsequently be used to inform diagnostic and treatment decision making. Each of the following methods described with reference to the figures may be performed by a computer or a controller at a server, which may for example be connected to a plurality of devices at which relevant data can be inputted, e.g. over a Wide Area Network (WAN). The controller may obtain the information discussed below from such distributed devices, and / or from local computer storage means. It will be appreciated that each of the methods below which are described in relation to pathogens in an animal population can also be applied with respect to parasites in an animal population. Figure 1 illustrates an example method 100 for determining the prevalence of a particular pathogen in an animal population, e.g. one of the pathogens mentioned above. The method 100 includes steps that involve obtaining information regarding multiple individual animals in an animal population. At a first step 102a-n for each of ‘n’ animals in a population, an indication is obtained of whether the animal is infected with a pathogen. This information is obtained based on the analysis of a clinical sample that is taken from an animal in the population, e.g. a blood test. A second step 104a-n includes obtaining an identifier of the animal. In some examples, the identifier may be based on a means of identifying an individual animal which is provided on each of clinical samples discussed above, e.g. a barcode on a vessel containing the clinical sample. In other examples, the indication of whether the animal is infected may be based on data from a biosensor, e.g. a biosensor worn by or implanted into the animal, instead of a clinical sample. Step 106a-n involves obtaining one or more parameter values associated with the status of the animal. For example, this step may involve obtaining an indication of whether the animal has a particular characteristic or has been administered with a certain type of medical treatment. Some other examples of these parameters are mentioned above, and discussed in more detail below. For example, a parameter value may an indication of be whether or not the animal has been administered with an antimicrobial, an indication of whether the animal has received a vaccine against the pathogen, an indication of a genomic profile of the animal, an indication of the geographical location of the animal. These parameter values may be obtained from an external device or from internal storage means. For example, they may have been recorded previously for the given animal. In so doing, for each identified animal, an associated status as well as an indication of whether they have a particular pathogen is obtained. This process is performed for a plurality of animals in the population. Based on this aggregated information, at step 108, one or more relationship values indicating an association between the prevalence of the pathogen and the parameters is determined. Such values could for example correspond to a likelihood of animals with a certain characteristic of having or being susceptible to the pathogen. The values can be updated as, over time, more animals in the population are tested and each of steps 102-106 are performed for that animal. Some examples of what these calculated relationship values could be or represent include: the efficacy of one or more antimicrobials against the pathogen, the efficacy of one or more vaccines against the pathogen, the susceptibility of animals to being infected by the pathogen based on their genomic profile or subgroup, the geographic spread of the pathogen within the population. As well as determining the prevalence of the pathogen with respect to one or more such parameters, the above method may be used to predict the further spread of the pathogen in the population or in another population, e.g. based on the known characteristics of that population in combination with the calculated association between the characteristic and the prevalence of the pathogen. In addition, this information can be used to provide an alert, e.g. over a network, if it is determined that the level of infection in a certain area, or amongst a certain group of animals which share a characteristic, exceeds a predetermined threshold level. The determined relationship values may also be used to determine a treatment or treatment plan for a given animal or group of animals in the population. For example, based on the calculated relationship values it may be determined that a certain animal or group of animals in the population with certain parameter values (e.g. characteristics) should be treated with a treatment such as an antimicrobial, antiparasitic, or vaccine. The method can further include administering the treatment or treatment plan to the animal or group of animals. Figure 2 illustrates a specific implementation of the method described above with reference to Figure 1. In particular, Figure 2 illustrates an example method 200 of determining the efficacy of an antimicrobial against a pathogen in an animal population. In this case, the method includes at a first step 202a-n obtaining an indication of whether a given animal is infected with a pathogen, where said pathogen may typically be treated with one or more antimicrobials. This indication may be obtained from or based on a clinical sample, such as a blood test. At steps 204a-n an identifier for each animal is obtained, e.g. as described above with reference to Figure 1. At step 206a-n, an indication of whether the animal has been administered a particular antimicrobial is obtained. Such an indication may be considered as one of the possible “parameter values” discussed above in relation to Figure 1. Thus for each identified animal a-n, an indication of whether they are infected with a pathogen, and whether they have been administered an antimicrobial is obtained. In this step, parameter values of the animal may also be obtained, in addition to or as an alternative to the indication of whether the animal has been administered a particular antimicrobial. At step 208, an indication of the efficacy of the antimicrobial in the population is determined, based on the aggregated information from each animal a-n. It will also be appreciated that, in addition to an overall determination of the efficacy of the antimicrobial in the population, the method may also comprise determining the efficacy of the antimicrobial against the pathogen with respect to certain other parameters / characteristics of the animals, e.g. those discussed above, where values associated with those parameters have been obtained. Figure 3 illustrates another specific implementation of the method described above with reference to Figure 1. In particular, Figure 3 illustrates an example method 300 of determining the efficacy of a vaccine against a pathogen in an animal population. In this case, the method includes at a first step 302a-n obtaining an indication of whether a given animal is infected with a pathogen. This indication may be obtained from or based on a clinical sample, such as a blood test. At steps 304a-n an identifier for each animal is obtained, e.g. as described above with reference to Figure 1. At step 306a-n, an indication of whether the animal has been administered a particular vaccine is obtained. Such an indication may be considered as one of the possible “parameter values” discussed above in relation to Figure 1. Thus for each identified animal a-n, an indication of whether they are infected with a pathogen, and whether they have been administered a vaccine is obtained. At step 208, an indication of the efficacy of the vaccine in the population is determined, based on this aggregated information. It will also be appreciated that, in addition to an overall determination of the efficacy of the vaccine in the population, the method may also comprise determining the efficacy of the vaccine with respect to certain other parameters / characteristics of the animals, e.g. those discussed above. The determined efficacy values may be used to determine a treatment or treatment plan for a given animal or group of animals in the population. For example, based on the calculated efficacy values it may be determined that a certain animal or group of animals in the population with certain parameter values (e.g. characteristics) should be administered the vaccine. The method can further include administering the vaccine to the animal or group of animals. Figure 4 illustrates an example method 400 of determining an antimicrobial treatment for an animal in an animal population. Method 400 thus relates to treatment decision making, and for example may use the information determined in the methods of one or more of Figures 1 to 3. At step 402 an identifier of the animal is obtained. For example, as set out above the identifier could be obtained based on identifying means provided with or on a clinical sample. At step 404 values for one or more parameters associated with the animal are obtained. Obtaining this data may be based on the identifier. For example, the identifier may enable the system or controller to retrieve the values relating to the parameters from storage or an external device. Step 406 comprises obtaining, based on a clinical sample from the animal, (e.g. the sample with which the identifier is associated) an indication of whether the animal is infected with a pathogen. Step 408 comprises determining, based on the indication of whether the animal is infected with a pathogen and a predetermined relationship between each of the one or more parameters and the efficacy of the antimicrobial, whether to treat the animal with an antimicrobial. The predetermined relationship may be based on the indication of an association between the efficacy of the antimicrobial and a particular parameter, as described above in relation to Figure 2. If a given animal is found to be infected with a pathogen, and they have one or more characteristics which indicate that a particular antimicrobial will be effective at treating the pathogen, it may be determined to treat that animal with the antimicrobial. Figure 5 illustrates an example method of testing an animal for determining treatment. At step 502 a clinical sample is obtained from the animal. At step 504 it is determined whether an animal is infected with a pathogen. At step 506 a data set is obtained indicating the status of the animal. For example, such data may be retrieved from storage and may indicate certain characteristics about the animal. At step 508 an indication of whether the animal is infected with the pathogen is added to the data set. As a further step, it may be determined based on the data set and a predetermined relationship between one or more of the parameters and the efficacy of a treatment (e.g. as described above in relation to an antimicrobial) whether to provide that treatment to the animal. Said method may enable the building of the data set and thus may enable more accurate treatment recommendations and decision making in future, in addition to providing a treatment decision for the animal in question. It will be appreciated with respect to each of methods 100, 200, 300, 400 and 500 of Figures 1 to 5 discussed above, that the “obtaining” steps, in particular those relating to the obtaining or retrieving of data / information / indications, need not be performed in the order shown in the figures. Said steps could instead be performed in any order, or concurrently. Figure 6 illustrates an example method 600 of implementing machine learning to improve the determination of the efficacy of a treatment against a pathogen. For example, the method may provide an improvement to the methods of determining efficacy of treatments that are described above. The method 600 includes implementing 602, as a neural network, a model configured to provide an association in an animal population between: a pathogen which has infected a portion of the animal population, and one or more parameters associated with the status of the animals in the animal population. Said model provides a determination of the efficacy of a treatment against a pathogen, e.g. based on various parameter values, for example as described above in relation to the earlier figures. The method further comprises obtaining 604 a data set for an animal in the population. The data set comprises one or more values of the parameters for that animal (e.g. the parameters discussed above), and an indication of whether the patient has been infected by the pathogen. The method further comprises modifying the model 606 based on the obtained data set. For example, the association provided by the model may be updated by the neural network if an animal with a particular set of characteristics has or has not been infected. Figure 7 illustrates an example method 700 of determining a characteristic of a pathogen in an animal population. Method 700 is similar to method 100 except that it is a characteristic of the pathogen itself which is tracked. In particular, the method 700 enables the genomic profile of the pathogen to be tracked, and for associations between the genomic profile of the pathogen and parameters associated with the animal population to be determined. The method 700 comprises obtaining 702a-n, based on a clinical sample from each of multiple animals in the animal population, an indication of the genomic profile of a pathogen with which the animal is infected. The clinical sample may be obtained in the same as described above, except that the sample may be analysed to determine genomic information about the pathogen (if present) rather than simply an indication of whether the animal is infected. The genomic profile for the pathogen may be obtained from the sample using whole genome sequencing and / or next generation sequencing techniques (WGC / NGC). Step 704a-n comprises obtaining an identifier of the animal, and step 706a-n of the method comprises obtaining one or more values of parameters associated with the status of the animal. These steps may be performed substantially as described above in relation to the earlier figures. Based on this information as obtained from multiple animals, the method further comprises determining 708 values indicating an association between the genomic profile of the pathogen and one or more parameters. As just one example, the parameters in this case may include an indication of whether the animal is or has been infected by the pathogen, and / or has been treated with a particular antimicrobial and / or vaccine. The method may therefore enable the infectiousness of a pathogen with a particular genomic profile, or part of a particular genomic sub-group, to be determined, as well as for the effectiveness of a given antimicrobial and / or vaccine against pathogens with different genomic profiles to be determined. Each of the methods described above relate specifically to the spread of pathogens. However, it will be appreciated that these methods could equally be applied to the spread of parasites through an animal population. In particular, mentions of pathogens could be replaced by parasites in the description of all the methods described herein. The above-described methods could alternatively be applied to determine characteristics relating to the fertility of animals in the population. Figure 8 illustrates an example method 800 of determining one or more fertility characteristics of an animal population. The method comprises, for each of animals a-n of the population, obtaining 802, based on a clinical sample from an animal in the animal population, an indication of whether an animal in the population is suitable for breeding. The method further comprises obtaining an identifier of the animal 804, obtaining an identifier of the animal 806. This data from each animal is used to determine 808 one or more values indicating an association between fertility of the animals and the various parameters. Certain parameters which may be measured and used in this method may include the level of particular substances found in the animals. For example, these may include the level of: progesterone, oestrogen, Interferon tau (IFN-T) Insulin-like growth factor -1 &2 (IGF-1 and 2), anti-mullerian hormone (AMH), pregnancy associated glycoprotein (PAG). Values for these parameters may also be obtained from the sample, from a biosensor, or may have been previously measured and retrieved from a data store. The indication of whether the animal is suitable for breeding may be based, at least in part, on the measurement of one or more of the above parameters. Figure 9 illustrates an example of possible combinations of parameters which may be used in the methods described herein. As mentioned above, one of the possible parameters associated with an animal, for which values are obtained, is the location of the animal. By obtaining this information as well as an indication of whether an animal is infected with a pathogen or parasite, a map can be generated which visually indicates the infection rate in different areas (e.g. the proportion of animals which have tested positive for the pathogen in a given time frame). An example of such a map is shown in Figure 10, in which differently shaded areas correspond to differing infection rates. The provision of such a map may enable targeted medical intervention to treat the population, and may provide information on how the prevalence of a given pathogen or parasite is dependent on environment. In addition, such a map may be used in determining if and / or for which animal populations an alert indicating that the level of infection is above a threshold level. As will be appreciated by the skilled reader in the context of the present disclosure, each of the examples described herein may be implemented in a variety of different ways. Any feature of any aspects of the disclosure may be combined with any of the other aspects of the disclosure. For example method aspects may be combined with apparatus aspects, and features described with reference to the operation of particular elements of apparatus may be provided in methods which do not use those particular types of apparatus. In addition, each of the features of each of the embodiments is intended to be separable from the features which it is described in combination with, unless it is expressly stated that some other feature is essential to its operation. Each of these separable features may of course be combined with any of the other features of the embodiment in which it is described, or with any of the other features or combination of features of any of the other embodiments described herein. Furthermore, equivalents and modifications not described above may also be employed without departing from the invention. Certain features of the methods described herein may be implemented in hardware, and one or more functions of the apparatus may be implemented in method steps. It will also be appreciated in the context of the present disclosure that the methods described herein need not be performed in the order in which they are described, or necessarily in the order in which they are depicted in the drawings. Accordingly, aspects of the disclosure which are described with reference to products or apparatus are also intended to be implemented as methods and vice versa. The methods described herein may be implemented in computer programs, or in hardware or in any combination thereof. In some examples a controller may be provided, for example by a general purpose processor, which may be configured to perform a method according to any one of those described herein. In some examples the controller may comprise digital logic, such as field programmable gate arrays, FPGA, application specific integrated circuits, ASIC, a digital signal processor, DSP, or by any other appropriate hardware. In some examples, one or more memory elements can store data and / or program instructions used to implement the operations described herein. Embodiments of the disclosure provide tangible, non-transitory storage media comprising program instructions operable to program a processor to perform any one or more of the methods described and / or claimed herein and / or to provide data processing apparatus as described and / or claimed herein. The controller may comprise an analogue control circuit which provides at least a part of this control functionality. An embodiment provides an analogue control circuit configured to perform any one or more of the methods described herein. In some examples, one or more memory elements can store data and / or program instructions used to implement the operations described herein. Embodiments of the disclosure provide tangible, non-transitory storage media comprising program instructions operable to program a processor to perform any one or more of the methods described and / or claimed herein and / or to provide data processing apparatus as described and / or claimed herein. Other examples and variations of the disclosure will be apparent to the skilled addressee in the context of the present disclosure.

Claims

1. A computer-implemented method of determining the prevalence of a parasite or pathogen in an animal population, the method comprising:a) obtaining, based on a clinical sample or biosensor data from an animal in the animal population, an indication of whether the animal is infected with a pathogen or parasite;b) obtaining an identifier of the animal; andc) obtaining one or more parameter values associated with the status of the animal; repeating steps a) to c) for a plurality of animals in the animal population;determining, based on the obtained data, one or more relationship values indicating an association between the prevalence of the pathogen or parasite and the one or more parameters.

2. The method of claim 1, further comprising predicting the prevalence of the pathogen or parasite in a second animal population based on the determined relationship values indicating the association between the prevalence of the pathogen or parasite and the one or more parameters.

3. The method of claim 1 or 2, wherein one or more the parameter values comprises an indication of whether the animal has been administered with one or more antimicrobials or anthelmintics.

4. The method of any preceding claim, wherein the one or more relationship values indicate the efficacy of one or more antimicrobials or anthelmintics against the pathogen or parasite.

5. The method of any preceding claim, wherein the parameter comprises an indication of whether the animal has received a vaccine against the pathogen.

6. The method of claim 5, wherein the one or more relationship values indicates the efficacy of the vaccine against the pathogen.

7. The method of any preceding claim, wherein the parameter comprises the genomic profile of the animal, for example wherein the one or more relationship values indicates the susceptibility of infection by the pathogen or parasite for one or more genomic profiles.

8. The method of any preceding claim, wherein the parameter comprises an indication of the geographical location of the animal, for example wherein the geographical location comprises the specific location of the animal, the topography of the location, the soil type at the location, and / or the vegetation at the location.

9. The method of claim 8, wherein the one or more relationship values indicate the extent of the geographical spread of the pathogen within the population.

10. The method of any preceding claim, further comprising outputting a signal indicating the one or more values to a network.

11. The method of claim 10, wherein the output signal comprises an alert, wherein the alert is provided in response to a determination that the prevalence of the pathogen is above a threshold level.

12. The method of any preceding claim, further comprising identifying, based on the parameter values and / or the determined relationship values, one or more animals in the population to be treated with an antimicrobial or anthelmintic.

13. The method of any preceding claim, further comprising identifying, based on the parameter values and / or the determined values, a particular antimicrobial or anthelmintic with which to treat one or more animals in the population.

14. A method of determining the efficacy of an antimicrobial or anthelmintic against a pathogen or parasite in an animal population, the method comprising:a) obtaining, based on a clinical sample or biosensor data from an animal in the animal population, an indication of whether the animal is infected with the pathogen or parasite;b) obtaining an identifier of the animal; andc) obtaining an indication of whether the animal has been administered with one or more antimicrobial drugs or anthelmintics;repeating steps a) to c) for a plurality of animals in the animal population;determining, based on the obtained data, the efficacy of the one or more antimicrobial drugs or anthelmintics against the pathogen or parasite.

15. The method of claim 14, further comprising obtaining, for each of the plurality of animals, a value for one or more further parameters associated with the status each animal; anddetermining based on the obtained values, a relationship between the efficacy of the one or more antimicrobial drugs and / or anthelmintics, and each of the one or more further parameters,16. The method of claim 15, further comprising identifying an animal to be treated with the one or more antimicrobial drugs or anthelmintics based on the determined relationship, for example wherein identifying an animal to be treated comprises obtaining, for the animal, a value for one or more of the further parameters associated with that animal.

17. A method of determining the efficacy of a vaccine against a pathogen in an animalpopulation, the method comprising:a) obtaining, based on a clinical sample from an animal in the animal population, an indication of whether the animal is infected with the pathogen;b) obtaining an identifier of the animal; andc) obtaining an indication of whether the animal has been administered the vaccine; repeating steps a) to c) for a plurality of animals in the animal population;determining, based on the obtained data, the efficacy of the vaccine against the pathogen.

18. A method of determining an antimicrobial or anthelmintic treatment for an animal in an animal population, the method comprising:obtaining an identifier for the animal;obtaining, based on the identifier, a value for one or more parameters associated with the animal;obtaining, based on a clinical sample from the animal, an indication of whether the animal is infected with a pathogen or parasite;determining, based on the indication and a predetermined relationship between each of the one or more parameters and the efficacy of the antimicrobial or anthelmintic, whether to treat the animal with an antimicrobial or anthelmintic.

19. The method of claim 18, further comprising treating the animal with the antimicrobialor anthelmintic.

20. A method of testing an animal for determining treatment, the method comprising:obtaining from an animal test subject, a clinical sample and an associated identifier for the animal;determining, based on the clinical sample, whether the animal is infected with a pathogen or parasite;obtaining, based on the identifier, a data set comprising values for one or more parameters associated with the status of the animal;adding an indication of whether the animal is infected with the pathogen to the data set.

21. The method of claim 20, further comprising determining, based on the data set and a predetermined relationship between one or more of the parameters and the efficacy of a treatment, whether to provide that treatment to the animal.

22. The method of claim 20 or 21, wherein the one or more parameters comprise at least one of:whether the animal has been administered with one or more antimicrobials;whether the animal has been administered with one or more anthelmintics;whether the animal has received one or more vaccines;the genomic profile of the animal;the geographical location of the animal.

23. A method of training an artificial neural network for determining the efficacy of a treatment against a pathogen or parasite, the method comprising:implementing, as a neural network, a model configured to provide an association in an animal population between:a pathogen which has infected a portion of the animal population;one or more parameters associated with the status of the animals in the animal population;obtaining a data set for an animal in the population, the data set comprising:one or more values of the parameters for that animal; andan indication of whether the animal has been infected by the pathogen or parasite; andmodifying the model based on the obtained data set.

24. A computer-implemented method of determining a characteristic of a pathogen or parasite in an animal population, the method comprising:a) obtaining, based on a clinical sample from an animal in the animal population, an indication of the genomic profile of a pathogen with which the animal is infected;b) obtaining an identifier of the animal; andc) obtaining one or more values of parameters associated with the status of the animal;repeating steps a) to c) for a plurality of animals in the animal population;determining, based on the obtained data, one or more values indicating an association between the genomic profile of the pathogen and the one or more parameters.

25. A computer-implemented method of determining one or more fertilitycharacteristics of an animal population, the method comprising:a) obtaining, based on a clinical sample from an animal in the animal population, an indication of whether an animal in the population is suitable for breeding;b) obtaining an identifier of the animal; obtaining an identifier of the animal;repeating steps a) to c) for a plurality of animals in the animal population;determining, based on the obtained data, one or more values indicating an association between the fertility and the parameters.

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