Systems and methods for monitoring disease in animal populations

Abstract

A computer implemented method of predicting the spread of a pathogen or parasite in an animal population. A plurality of indications of the presence of a pathogen or parasite are obtained from a first plurality of geographical locations 122. Based on the indications, the boundaries of one or more polygons are determined. Each polygon represents a geographical area in the region within which the pathogen or parasite is present 124. Updated indications of the presence of a pathogen or parasite are obtained from a second plurality of geographical locations 126. The boundaries of the polygons are adjusted based on the updated indications 128. Output data is provided to overlay the polygons and the adjusted polygons onto a map of the geographical region for predicting a further geographical area where the pathogen or parasite will be present at a future time 130. Optionally, indications comprise diagnostic data, biosensor data, data relating to the animal’s genomic profile, vaccine status or geographical information. Indications may also relate to vectors capable of transmitting the pathogen or parasite in the animal population.

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. Another aspect provides a computer implemented method of predicting the spread of a pathogen or parasite in an animal population within a geographical region, the method comprising: a) at a first time, obtaining a plurality of indications of the presence of a pathogen or parasite from a first plurality of geographical locations; b) determining, based on the indications, the boundaries of one or more polygons, each polygon representing a geographical area in the region, within which the pathogen or parasite is present at the first time; c) at a second time, obtaining updated indications of the presence of a pathogen or parasite from a second plurality of geographical locations; d) adjusting the boundaries of the polygons based on the updated indications; e) providing output data to overlay the polygons and the adjusted polygons onto a map of the geographical region, for predicting a further geographical area where the pathogen or parasite will be present at a future time. A plurality, e.g. some or all, of the indications of the presence of a pathogen or parasite may each be based on diagnostic data from an animal in the animal population. For example, the indications may be based on or derived from diagnostic testing, a clinical sample and / or biosensor data from an animal in the animal population, and / or other non-invasive diagnostic technologies. Technologies may include imaging technology, photonics, and / or other techniques for measuring one or parameters, e.g. temperature, of an animal. A plurality, e.g. some or all, of the indications of the presence of a pathogen or parasite may each be based on diagnostic data from one or more disease vectors which can transmit the pathogen or parasite to the animals in the animal population. For example, the indications may be based on or derived from diagnostic testing, a clinical sample or biosensor data from the one or more disease vectors, and / or other non-invasive diagnostic technologies. Such technologies may include imaging technology, photonics, and / or other techniques for measuring one or parameters, e.g. temperature, of one or more of the disease vectors. The method may further comprise overlaying an additional fixed polygon on the map. The additional polygon may represent a fixed geographical area within the region at the first and second time. The fixed polygon may comprise an indicator of whether or not the pathogen or parasite is present in the fixed geographical area. For example, the fixed polygon may represent a fixed geographical feature, such as a farm. The method may further comprise obtaining an indication that the pathogen or parasite is present in the fixed geographical area, and adjusting the indicator to indicate the presence of the pathogen or parasite within the fixed geographical area in response to the obtained indication. The indicator may be a visual indicator such as a colour of the polygon. Adjusting the indicator may comprise changing the colour of the polygon. The method may further comprise obtaining further indications of the presence of a pathogen or parasite from geographical areas within the region at a series of times, and, at each time, adjusting the polygons. Providing output data to overlay the polygons onto a map may comprise outputting data to a resource, e.g. a computer resource such as the memory of a computer, and may comprise comprising providing / sending such data to the resource over a network. Providing output data may comprise providing output data to overlay the polygons onto the map of the geographical region before the second time. The output data to overlay the adjusted polygons onto the map may be provided after output data to overlay the (first) polygons. The first time and / or second time may be or comprise a time period, and the indications may be obtained at different instances within the time periods. The method may further comprise predicting the further geographical area where the pathogen or parasite will be present at a future time. The method may further comprise providing output data for identifying the further geographical area. This output data may comprise means for showing the geographical area on a map, e.g. on the same map as the polygons. Such data may be provided to a resource in the same way as the output data to overlay the polygons described above. Predicting the further geographical area may be based on the polygons and data indicating one or more environmental parameters of the geographical region. The parameters may relate to the weather within the region or locations within the region, for example real-time temperature and wind speed and direction information, or predictions or forecasts of such information. The parameters may also or alternatively relate to the long-term climate of the geographical region. For example, the data may indicate long term trends in temperature and / or other environmental factors, which, in combination with the obtained data described herein, e.g. the polygons, may be used to predict the further area that the pathogen or parasite may spread to. Data indicating the one or more environmental parameters may be retrieved from a data store or from a server e.g. via a network. A plurality of animals in the population may each be associated with one or more parameter values. The parameter values may each correspond to a parameter of the animal, such as certain characteristics of the animal as described elsewhere in the application. The method may further comprise obtaining the one or more parameter values for each of the plurality of animals. The method may also further comprise determining, based on the obtained parameter values and the obtained indications of whether each animal is infected with the pathogen or parasite, one or more relationship values indicating an association between the prevalence of the pathogen or parasite and the one or more 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; Figure 11 shows a series of maps illustrating the spread of a disease over time. Figure 12 is a flow chart illustrating an example method of predicting the 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 be 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, e.g. are infected with, a particular pathogen or parasite 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. Figure 11 illustrates a method of tracking the spread or movement of a disease, pathogen, or parasite in an animal population within a geographical region over time. In particular, Figure 11 illustrates a first map A of a geographical area at a first time. Overlaid on the map 10 are a set of polygons 12 to 17. Each of the polygons 12 to 17 correspond to a geographical area within the region, from which indications that a particular pathogen or parasite is present have been obtained. The indications may be based on diagnostic data from the animals in the population, such as the results of laboratory testing of clinical samples from animals in the population, or biosensor data from animals in the population, which indicate that said animals have or are infected with the pathogen / parasite. Such indications may be obtained as described above with reference to e.g. Figures 1 to 8. Alternatively or additionally, the indications may be based on diagnostic data from one or more disease vectors, such as biosensor data or the results of laboratory testing from a clinical sample of disease vectors which are known to be able to transmit the pathogen or parasite to the animals in the population. Such indications of whether the vectors are carrying / infected with the pathogen or parasite may be obtained after trapping the vectors. As one example, the vectors may be mosquitos carrying a mosquito-borne disease which could infect the animal population, or birds carrying a disease such as avian flu. The polygons 12-17 may all share a characteristic indicator, e.g. a colour, such as red, to indicate that they each correspond to an area within which the disease (e.g. pathogen or parasite) is present. Map A also includes a fixed polygon 18. The fixed polygon 18 represents a fixed geographical area or location, for example a particular farm in the region. The fixed polygon provides a visual indication of whether or not the pathogen or parasite is present, for example a colour, the fixed polygon 18 may have a visual indication that is different from the other polygons to indicate that the pathogen or parasite is not present. In the example shown in Figure 11a is a different colour (blue) from the other polygons (red) to indicate that the pathogen or parasite is not present. Maps B, C, and D, show the region at a second, third, and fourth time respectively. As shown, the boundaries of the polygons 12 to 17 (for example their shape, size and / or position) are adjusted over time. The polygons are adjusted based on updated indications of if and where the pathogen or parasite is present at the second, third and fourth times. The updated indications may be based on diagnostic data, such as laboratory testing of samples or biosensor data relating to the animals in the population, or disease vectors, at a second plurality of locations within the region. Fixed polygon 18 is shown in blue in maps B and C, indicating that the pathogen or parasite is not present in the fixed area at the second or third time. At the fourth time, the fixed polygon is shown in red in map D, e.g. the same colour as the polygons 12-17, indicating that the pathogen or parasite is now present in the fixed area, e.g. farm. For example, an indication may be obtained, e.g. as described above, that the pathogen or parasite is present in the fixed geographical area, and in response the colour of the polygon, is adjusted (e.g. from blue to red) to indicate the presence of the pathogen or parasite within the fixed geographical area. The movement of the polygons 12-17 may enable a user to predict the spread of the disease, i.e. where the pathogen or parasite will be present at a future time, and take preventative action. Figure 12 is a flow diagram illustrating an example method which may incorporate polygons and maps such as those described above in relation to Figure 11. A first step 122 comprises obtaining a plurality of indications of the presence of a pathogen or parasite from a first plurality of geographical locations, at a first time. This may include obtaining indications from a clinical sample of animals in the population, or disease vectors, as described above. It may also include obtaining indications from reports of the disease, which may in turn be based on a clinical sample. A second step 124 comprises determining, based on the indications, the boundaries of one or more polygons. As discussed above, each polygon represents a geographical area in the region, within which the pathogen or parasite is present at the time at which the indications where obtained (the first time). The polygons shown in Figure 11, in particular map A, are one example of such polygons. At a third step 126 updated indications of the presence of a pathogen or parasite from a second plurality of geographical locations are obtained, at a second time. The second plurality of locations may be the same as the first plurality, or may be different, or there may be some overlap between them. At a fourth step 128, the boundaries of the polygons are adjusted based on the updated indications. At step 130, output data is provided to overlay the polygons and / or the adjusted polygons onto a map of the geographical region, for predicting a further geographical area where the pathogen or parasite will be present at a future time. Providing the output data may comprise outputting data to a resource, such as the memory of a computer. The output data may be configured (e.g. comprise instructions) to cause a computer to display the map and polygons to a user, e.g. so that each adjustment of the boundaries of the polygons is displayed sequentially over time. This in turn may enable the user to predict the spread of the parasite of pathogen into a further area. In some embodiments, the method comprises a further step of predicting the further geographical area where the pathogen or parasite will be present at a future time. The prediction may be based in part on environmental parameters, including weather conditions, such as the wind speed and direction, within in the region, or longer term climate data such as data indicating a trend in temperature in the region. Such data may retrieved from a data store or from a server over a network. The method may also include a further subsequent step, of outputting data suitable for identifying the further geographical area. Providing this output data may also comprise outputting the data to a resource, as described above. It will be appreciated that the provision of output data as described in the method above need not be provided in a single final step of the method. For example, output data to overlay the first polygons on a map may be provided after the step 124 of first determining the boundaries of the polygons and before updated indications are obtained. Further steps of obtaining indications and adjusting the polygons may be performed, and output data for overlaying the polygons on the map may be provided at each step. It will also be appreciated that the ‘times’ referred to in the above-described method, e.g. the first time and second time, need not correspond to a single instance but may refer to a given time period or interval. Moreover, each step of obtaining indications described above may consist of several individual steps of obtaining the indications, e.g. at different times within a particular time period corresponding to the ‘first time’, ‘second time’ etc. Output data to overlay the polygons and adjust them on a map as described above may be provided at each of these individual steps. For example the maps may be updated (e.g. the polygon boundaries adjusted) each time that indications of whether a pathogen / parasite is present are obtained. Features of the aspect and methods described above in relation to Figures 11 and 12 can be combined with those describe above in relation to other aspects. For example, in the case that indications of the presence of a pathogen or parasite are derived from animals in the population, obtaining such indications may constitute at least part of the step of obtaining, based on diagnostic data, e.g. from a clinical sample or a biosensor of an animal in the animal population, an indication of whether the animal is infected with a pathogen or parasite, which is described above in relation to Figures 1 to 10. These indications may then be used, in combination with parameter values associated with each animal to characterise a relationship or correlation (e.g. to determine relationship values) between the prevalence of the pathogen or parasite and said parameters of the animal. The relationship values between the prevalence of the disease (parasite / pathogen) and one or more parameters associated with the animal may be either determined as described above or may be previously known, e.g. retrieved from data storage. These values may be used in the above-described prediction of where the disease may spread to at a future time. For example, a prediction of whether or not a disease will spread to a given area may be determined based on the movement / adjustment of the polygons, in combination with a known characteristic of some or all of a population of animals (e.g. as characterised by the relationship values), and the locations of said animals in the geographical region. As just one example, the movement of the polygons towards a population of animals which are not yet infected, in combination with relationship values which indicate the extent that said population are susceptible to infection, may be used to predict the likelihood and / or rate of infection of animals in the population. 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. Embodiments of the disclosure are set out in the following numbered clauses: 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; 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. 2. The method of clause 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 clause 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 clause, 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 clause, wherein the parameter comprises an indication of whether the animal has received a vaccine against the pathogen. 6. The method of clause 5, wherein the one or more relationship values indicates the efficacy of the vaccine against the pathogen. 7. The method of any preceding clause, 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 clause, 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 clause 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 clause, further comprising outputting a signal indicating the one or more values to a network. 11. The method of clause 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 clause, 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 clause, 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; and c) 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 clause 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; and determining 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 clause 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 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. 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 clause 18, further comprising treating the animal with the antimicrobial or 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 clause 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 clause 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; 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. 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; 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 and the one or more parameters. 25. 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.

Claims

1. A computer implemented method of predicting the spread of a pathogen or parasite in an animal population within a geographical region, the method comprising:a) at a first time, obtaining a plurality of indications of the presence of a pathogen or parasite from a first plurality of geographical locations;b) determining, based on the indications, the boundaries of one or more polygons, each polygon representing a geographical area in the region, within which the pathogen or parasite is present at the first time;c) at a second time, obtaining updated indications of the presence of a pathogen or parasite from a second plurality of geographical locations;d) adjusting the boundaries of the polygons based on the updated indications;e) providing output data to overlay the polygons and the adjusted polygons onto a map of the geographical region, for predicting a further geographical area where the pathogen or parasite will be present at a future time.

2. The method of claim 1, wherein a plurality of the indications of the presence of a pathogen or parasite are each based on diagnostic data from an animal in the animal population.

3. The method of claim 1 or 2, wherein a plurality of the indications of the presence of a pathogen or parasite are each based on diagnostic data from one or more disease vectors which can transmit the pathogen or parasite to the animals in the animal population.

4. The method of any preceding claim, further comprising overlaying an additional fixed polygon on the map, the additional polygon representing a fixed geographical area within the region at the first and second time, wherein the fixed polygon comprises an indicator of whether or not the pathogen or parasite is present in the fixed geographical area.

5. The method of claim 4, further comprising obtaining an indication that the pathogen or parasite is present in the fixed geographical area, and adjusting the indicator to indicate the presence of the pathogen or parasite within the fixed geographical area in response tothe obtained indication.

6. The method of claim 5, wherein the indicator is a colour of the polygon, and adjusting the indicator comprises changing the colour of the polygon.

7. The method of any preceding claim, wherein the second plurality of geographical locations are the same as the first plurality of geographical locations.

8. The method of any of claims 1 to 6, wherein a subset of the second plurality of geographical locations are the same as a subset first plurality of geographical locations.

9. The method of any of claims 1 to 6, wherein each of the second plurality of geographical locations are distinct from each of the first plurality of geographical locations.

10. The method of any preceding claim, comprising obtaining further indications of the presence of a pathogen or parasite from geographical areas within the region at a series of times, and, at each time, adjusting the polygons.

11. The method of any preceding claim, further comprising predicting the further geographical area where the pathogen or parasite will be present at a future time, and providing output data for identifying the further geographical area.

12. The method of any preceding claim, wherein predicting the further geographical area is based on the polygons and data indicating one or more environmental parameters of the geographical region.

13. The method of any preceding claim, wherein a plurality of animals in the population are each associated with one or more parameter values, the parameter values each corresponding to a parameter of the animal; the method further comprising obtaining the one or more parameter values for each of the plurality of animals.

14. The method of claim 13, further comprising determining, based on the obtained parameter values and the obtained indications of whether each animal is infected with the pathogen or parasite, one or more relationship values indicating an association betweenthe prevalence of the pathogen or parasite and the one or more parameters.

15. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of any of claims 1 to 5 14.

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