Analytic epidemiological studies aim to investigate and identify factors associated with the presence of disease within populations, through the investigation of factors which may vary between individual members of these populations. Details on study designs appropriate for these investigations are given elsewhere. Conceptually, this involves investigating the disease experience amongst different 'groups' of animals within an overall population, distinguished according to the factor(s) of interest. These factors can be classified as one of the components of the 'epidemiological triad' of Host, Agent and Environment, many of which are closely interrelated with each other.

Systematic error, or 'bias' is of particular importance in any epidemiological investigation, and should be avoided wherever possible. Biases will reduce the validity of any results obtained, whether it be by overestimating or underestimating the frequency of disease in a population or the association between an exposure and disease. The forms of bias covered here can only be minimised through careful study design and execution - they cannot be accounted for in the analysis. Although confounding is considered by many authors as a form of bias, it can be accounted for during analysis, and so is covered separately.

The issue of confounding is of central importance in any analytic epidemiological study (as well as in those descriptive studies aiming to compare different populations), especially in the case of observational studies. Confounding results from non-random differences between the groups of animals being compared in relation to a second, 'confounding' exposure which is independently associated with both the exposure of interest (although not a consequence of this) and the outcome of interest (although not an effect of this). This results in the effect of the exposure of interest is 'mixed up' with the effect of the confounding exposure, and therefore an incorrect estimate of the true association. As such, confounding is viewed by many authors as a form of bias - however, unlike forms of selection and information bias, it is a natural feature of the data (in the case of an observational study), and techniques are available to account for it during analysis.

All epidemiological investigations require some form of data description. A number of methods are available for describing data, and the most appropriate one will depend upon both the type of data available and the aims of the investigation. If these issues are not considered, useful information may be lost, or more seriously, a misleading estimate may be made.

Epidemiological investigation requires a good understanding of different data types, as this will strongly influence data analysis and interpretation. Data can broadly be classified as qualitative and quantitative, and within each of these groups, data can be further categorised as shown below. Although different grouping systems are available, it is important to consider the type of data being dealt with prior to any analysis. If desired, data can often be changed into different types through manipulation (for example, the quantitative variable weight can be converted to qualitative variables such as low/medium/high or low/not low).

Descriptive epidemiology aims to describe the distribution of disease in terms of animal, place and time, as shown below. In a purely descriptive study, no attempt is made to formally investigate reasons for the patterns of disease observed, although hypotheses regarding possible reasons will commonly be generated and developed as a result of these investigations. A description of the different types of descriptive studies is provided elsewhere.

A diagnostic test is an objective method of deciding whether an animal has a disease, or not. Decisions made following diagnostic testing are usually dichotomous e.g. treat or do not treat the animal, therefore diagnostic tests are usually interpreted as dichotomous outcomes (diseased or non-diseased). In this case, if a diagnostic test is measuring a continuous outcome e.g. antibody titre then a cut-off for classifying animal’s as positive or negative must be selected. The figure below shows that whereever the cut-off is selected there is usually some overlap between results i.e. some diseased animals will have the same value as non-diseased animals and resulting in some false-positive and false-negative results.

Hypothesis tests are very commonly used in epidemiological investigations, and a wide number of tests are available. These can be classified into groups according to the data types in question, according to whether a specific underlying distribution is assumed when performing the test (in which case, the test is known as a parametric test), and according to whether or not the data are matched or independent (i.e. whether comparisons are being made at the individual level or the group level). As described earlier, qualitative data are not numerical in nature, and include categorical and ordinal data (such as the breed of dog, or the body condition score of a cow). Quantitative data are numerical, and include variables such as weight, age and height.

In many epidemiological studies, it is not possible to include every individual in a population. Rather, a sample of individuals is collected. This may be take the form of a survey, a cross-sectional study, a randomised controlled trial, and so on. The important issue is that not every individual in the source population is included, which means that random, or sampling, error and biases may be introduced. These affect our ability to extrapolate our results (whether descriptive or analytic in nature) to the source population. However, the aim of most studies is to draw some conclusion about the source population, using the results obtained from the sample. This requires the use of statistical methodology in a process known as inferential statistical analysis, and is commonly used in epidemiological investigations.

A very common aim of epidemiological investigation is to estimate the frequency of disease in a population. This is of particular importance in the case of surveillance and disease monitoring systems, and is commonly the central aim of many descriptive studies. There are two main measures of disease frequency used by epidemiologists - the prevalence and the incidence of disease, which each measure different aspects of disease. The survival time, which is closely associated with the incidence, is another measure commonly used. Counts of disease are not commonly used in epidemiological studies, although they can be useful when deciding upon resource requirements when implementing disease control strategies.

Measures of effect and impact can be calculated using the same contingency table used to calculated measures of strength of association.

An important consideration when sampling from a population is that of random error (also known as sampling error), which results from chance variation in the members of any sample taken from a larger population. Random error may affect the conclusions you draw from a study by affecting the precision of a descriptive study, or the power of an analytic study. However, although the magnitude of random error can be quantified to some degree, its direction cannot be predicted due to its random nature. Random errors can be accounted for to some degree through the application of inferential statistics when presenting and interpreting results.

Risk assessment is a tool for the objective evaluation of risk, and is commonly performed by veterinary epidemiologists. Its use in the setting of veterinary epidemiology has increased in recent years, particularly as a tool for the objective consideration of the risk of movement of pathogens through international trade in animals and animal products. Risk assessment is only one component in an overarching risk analysis process, which also incorporates risk management (the process whereby procedures are implemented in order to reduce the risk) and risk communication (which involves the ongoing dissemination of relevant information to stakeholders). However, these other components of the risk analysis process will not be covered in further detail here as they are predominantly the responsibility of risk managers and policy makers.

The information gained through the study of disease in populations which will be increased if more members of the population are sampled. However, the sampling of every individual in a population is rarely feasible from either a logistical or an economic perspective (except in the case of very small-scale studies). Censuses are a form of descriptive study which aims to systematically collect information about every member of the population of interest (the source population), and are carried out in many countries for both livestock as well as for humans (although information regarding disease may not be collected). Statistical surveys are another type of descriptive study, which aim to select a sample (known as the study sample) from the source population, with the intention of extrapolating the information about these individuals to the source population. Similarly, in most analytic studies, a sample of the population must be selected for the same reasons.

Epidemiological studies can be described as belonging to one of two categories: descriptive or analytical. Descriptive studies involve detailed investigations of individuals in order to improve knowledge of disease. Descriptive studies often have no prior hypotheses and are opportunistic studies of disease whereas analytical studies are used to test hypotheses by selection and comparison of groups. However, data obtained from analytical studies can be used in a descriptive manner and vice versa.

Monitoring of the epidemiological patterns (animal, place, time) of diseases and pathogens within populations provides a vital system for the identification of changes in disease status within this population (whether this relates to all animals worldwide, or those within a single country, region, village or farm). For this reason, most countries have systems in place for the intermittent collection and collation of data relating to disease. Monitoring of production levels also provides a method of informing farmers about the productivity of their animals. These processes can be described as monitoring systems.

Veterinary epidemiology is principally concerned with the study of disease within populations (although it may also be used for investigation of issues such as animal welfare and productivity). Put simply, it involves the investigation of patterns of disease within a population, in relation to which animals are affected, the spatial distribution (i.e. location) of affected animals, and the temporal distribution of affected animals (i.e. patterns of disease through time).

The principles of veterinary epidemiology are identical to those of human epidemiology, with the exception that they are applied to animal populations rather than human populations. As such, veterinary and human epidemiology can be viewed as forms of the same overarching discipline of epidemiology. Epidemiology is principally concerned with the investigation of disease within populations (although the same principles are also applicable to investigation of other characteristics, such as animal welfare or productivity), and is based on the concept that disease often does not occur in a random fashion. That is, various characteristics of the animal, the pathogenic agent (or agents) and the environment interact in order to alter the probability of disease occurrence. Epidemiology aims to identify these factors and to describe disease in the population.