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Abdullah M. Fungal pathogens are among the most damaging biotic factors for plants. More than 10, fungal species attack plant roots, stems, leaves, flowers, seeds or fruits, and cause diseases that vary in their severity and economic impact.

In contrast, many fungal species act as biocontrol agents, producers of antibiotics, promoters of plant growth and development, and decomposers of waste material. Fungi occur in temperate, tropical and dry environments.

Pakistan Journal of Agricultural Research

When the conditions are not suitable for fungi, they tend to survive in soil or plant debris as solid structures. Spores of fungal pathogens usually germinate in response to exudates secreted by plants, resulting in hyphal growth towards plants and subsequent infection and disease development.

Development of disease epidemics depends on several factors, including aggressiveness of the pathogen, reproduction rate, mode of reproduction, susceptibility of the hosts, and the prevailing environmental conditions. Successful management of fungal diseases depends on how much we understand about the epidemiology of fungal pathogens under certain environmental conditions and cultural practices.

This chapter focuses on the epidemiology of fungal pathogens and plant-pathogen interactions in dry environments, and discusses some of the most common fungal diseases in these environments, with a particular focus on wheat root rot, spot blotch and rust diseases. Epidemiology and management of fungal diseases in dry environments. N2 - Fungal pathogens are among the most damaging biotic factors for plants.

AB - Fungal pathogens are among the most damaging biotic factors for plants. Crop Sciences. Abstract Fungal pathogens are among the most damaging biotic factors for plants. Fingerprint fungal disease. Plant Exudates. To resolve these issues and advance population health science in the era of molecular precision medicine , " molecular pathology " and "epidemiology" was integrated to create a new interdisciplinary field of " molecular pathological epidemiology " MPE , [26] [27] defined as "epidemiology of molecular pathology and heterogeneity of disease".

In MPE, investigators analyze the relationships between A environmental, dietary, lifestyle and genetic factors; B alterations in cellular or extracellular molecules; and C evolution and progression of disease.

Epidemiology - Wikipedia

A better understanding of heterogeneity of disease pathogenesis will further contribute to elucidate etiologies of disease. The MPE approach can be applied to not only neoplastic diseases but also non-neoplastic diseases. By it was recognized that many pathogens' evolution is rapid enough to be highly relevant to epidemiology, and that therefore much could be gained from an interdisciplinary approach to infectious disease integrating epidemiology and molecular evolution to "inform control strategies, or even patient treatment.

Epidemiologists employ a range of study designs from the observational to experimental and generally categorized as descriptive, analytic aiming to further examine known associations or hypothesized relationships , and experimental a term often equated with clinical or community trials of treatments and other interventions. In observational studies, nature is allowed to "take its course," as epidemiologists observe from the sidelines.

Conversely, in experimental studies, the epidemiologist is the one in control of all of the factors entering a certain case study. The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology. Modern epidemiologists use informatics as a tool. Observational studies have two components, descriptive and analytical. Descriptive observations pertain to the "who, what, where and when of health-related state occurrence". The term 'epidemiologic triad' is used to describe the intersection of Host , Agent , and Environment in analyzing an outbreak.

Case-series may refer to the qualitative study of the experience of a single patient, or small group of patients with a similar diagnosis, or to a statistical factor with the potential to produce illness with periods when they are unexposed. The former type of study is purely descriptive and cannot be used to make inferences about the general population of patients with that disease. These types of studies, in which an astute clinician identifies an unusual feature of a disease or a patient's history, may lead to a formulation of a new hypothesis.

Using the data from the series, analytic studies could be done to investigate possible causal factors. These can include case-control studies or prospective studies. A case-control study would involve matching comparable controls without the disease to the cases in the series. A prospective study would involve following the case series over time to evaluate the disease's natural history. The latter type, more formally described as self-controlled case-series studies, divide individual patient follow-up time into exposed and unexposed periods and use fixed-effects Poisson regression processes to compare the incidence rate of a given outcome between exposed and unexposed periods.

This technique has been extensively used in the study of adverse reactions to vaccination and has been shown in some circumstances to provide statistical power comparable to that available in cohort studies. Case-control studies select subjects based on their disease status. It is a retrospective study. A group of individuals that are disease positive the "case" group is compared with a group of disease negative individuals the "control" group. The control group should ideally come from the same population that gave rise to the cases. The case-control study looks back through time at potential exposures that both groups cases and controls may have encountered.

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If the OR is significantly greater than 1, then the conclusion is "those with the disease are more likely to have been exposed," whereas if it is close to 1 then the exposure and disease are not likely associated. If the OR is far less than one, then this suggests that the exposure is a protective factor in the causation of the disease. Case-control studies are usually faster and more cost effective than cohort studies , but are sensitive to bias such as recall bias and selection bias.

The main challenge is to identify the appropriate control group; the distribution of exposure among the control group should be representative of the distribution in the population that gave rise to the cases. This can be achieved by drawing a random sample from the original population at risk. This has as a consequence that the control group can contain people with the disease under study when the disease has a high attack rate in a population. As the odds ratio approached 1, approaches 0; rendering case control studies all but useless for low odds ratios.

For instance, for an odds ratio of 1. Cohort studies select subjects based on their exposure status. The study subjects should be at risk of the outcome under investigation at the beginning of the cohort study; this usually means that they should be disease free when the cohort study starts.

The cohort is followed through time to assess their later outcome status. An example of a cohort study would be the investigation of a cohort of smokers and non-smokers over time to estimate the incidence of lung cancer. As with the OR, a RR greater than 1 shows association, where the conclusion can be read "those with the exposure were more likely to develop disease. Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status.

Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed. Although epidemiology is sometimes viewed as a collection of statistical tools used to elucidate the associations of exposures to health outcomes, a deeper understanding of this science is that of discovering causal relationships.

For epidemiologists, the key is in the term inference. Correlation, or at least association between two variables, is a necessary but not sufficient criteria for inference that one variable causes the other. Epidemiologists use gathered data and a broad range of biomedical and psychosocial theories in an iterative way to generate or expand theory, to test hypotheses, and to make educated, informed assertions about which relationships are causal, and about exactly how they are causal.

Epidemiologists emphasize that the " one cause — one effect " understanding is a simplistic mis-belief. If a necessary condition can be identified and controlled e. In , Austin Bradford Hill proposed a series of considerations to help assess evidence of causation, [41] which have come to be commonly known as the " Bradford Hill criteria ".

Principles of Infectious Diseases and Epidemiology

In contrast to the explicit intentions of their author, Hill's considerations are now sometimes taught as a checklist to be implemented for assessing causality. Epidemiological studies can only go to prove that an agent could have caused, but not that it did cause, an effect in any particular case:. This question, sometimes referred to as specific causation, is beyond the domain of the science of epidemiology.

Epidemiology has its limits at the point where an inference is made that the relationship between an agent and a disease is causal general causation and where the magnitude of excess risk attributed to the agent has been determined; that is, epidemiology addresses whether an agent can cause a disease, not whether an agent did cause a specific plaintiff's disease. In United States law, epidemiology alone cannot prove that a causal association does not exist in general. Conversely, it can be and is in some circumstances taken by US courts, in an individual case, to justify an inference that a causal association does exist, based upon a balance of probability.

The subdiscipline of forensic epidemiology is directed at the investigation of specific causation of disease or injury in individuals or groups of individuals in instances in which causation is disputed or is unclear, for presentation in legal settings. Epidemiological practice and the results of epidemiological analysis make a significant contribution to emerging population-based health management frameworks.

Modern population-based health management is complex, requiring a multiple set of skills medical, political, technological, mathematical etc. This task requires the forward looking ability of modern risk management approaches that transform health risk factors, incidence, prevalence and mortality statistics derived from epidemiological analysis into management metrics that not only guide how a health system responds to current population health issues, but also how a health system can be managed to better respond to future potential population health issues.

Each of these organizations use a population-based health management framework called Life at Risk that combines epidemiological quantitative analysis with demographics, health agency operational research and economics to perform:. Applied epidemiology is the practice of using epidemiological methods to protect or improve the health of a population. Applied field epidemiology can include investigating communicable and non-communicable disease outbreaks, mortality and morbidity rates, and nutritional status, among other indicators of health, with the purpose of communicating the results to those who can implement appropriate policies or disease control measures.

As the surveillance and reporting of diseases and other health factors becomes increasingly difficult in humanitarian crisis situations, the methodologies used to report the data are compromised. One study found that less than half Among the mortality surveys, only 3.

As nutritional status and mortality rates help indicate the severity of a crisis, the tracking and reporting of these health factors is crucial. Vital registries are usually the most effective ways to collect data, but in humanitarian contexts these registries can be non-existent, unreliable, or inaccessible. As such, mortality is often inaccurately measured using either prospective demographic surveillance or retrospective mortality surveys.

Prospective demographic surveillance requires lots of manpower and is difficult to implement in a spread-out population. Retrospective morality surveys are prone to selection and reporting biases. Other methods are being developed, but are not common practice yet. Different fields in epidemiology have different levels of validity. One way to assess the validity of findings is the ratio of false-positives claimed effects that are not correct to false-negatives studies which fail to support a true effect. To take the field of genetic epidemiology, candidate-gene studies produced over false-positive findings for each false-negative.

By contrast genome-wide association appear close to the reverse, with only one false positive for every or more false-negatives. By contrast other epidemiological fields have not required such rigorous reporting and are much less reliable as a result. Random error is the result of fluctuations around a true value because of sampling variability.

Random error is just that: random. It can occur during data collection, coding, transfer, or analysis.

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Examples of random error include: poorly worded questions, a misunderstanding in interpreting an individual answer from a particular respondent, or a typographical error during coding. Random error affects measurement in a transient, inconsistent manner and it is impossible to correct for random error. There is random error in all sampling procedures. This is called sampling error. Precision in epidemiological variables is a measure of random error.

Precision is also inversely related to random error, so that to reduce random error is to increase precision. Confidence intervals are computed to demonstrate the precision of relative risk estimates. The narrower the confidence interval, the more precise the relative risk estimate. There are two basic ways to reduce random error in an epidemiological study.

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The first is to increase the sample size of the study. In other words, add more subjects to your study. The second is to reduce the variability in measurement in the study. This might be accomplished by using a more precise measuring device or by increasing the number of measurements. Note, that if sample size or number of measurements are increased, or a more precise measuring tool is purchased, the costs of the study are usually increased.

There is usually an uneasy balance between the need for adequate precision and the practical issue of study cost. A systematic error or bias occurs when there is a difference between the true value in the population and the observed value in the study from any cause other than sampling variability. An example of systematic error is if, unknown to you, the pulse oximeter you are using is set incorrectly and adds two points to the true value each time a measurement is taken. The measuring device could be precise but not accurate. Because the error happens in every instance, it is systematic.

Conclusions you draw based on that data will still be incorrect. But the error can be reproduced in the future e. A mistake in coding that affects all responses for that particular question is another example of a systematic error. The validity of a study is dependent on the degree of systematic error. Validity is usually separated into two components:. Selection bias occurs when study subjects are selected or become part of the study as a result of a third, unmeasured variable which is associated with both the exposure and outcome of interest.

Sackett D cites the example of Seltzer et al. Information bias is bias arising from systematic error in the assessment of a variable. Confounding has traditionally been defined as bias arising from the co-occurrence or mixing of effects of extraneous factors, referred to as confounders, with the main effect s of interest. The counterfactual or unobserved risk R A0 corresponds to the risk which would have been observed if these same individuals had been unexposed i. Some epidemiologists prefer to think of confounding separately from common categorizations of bias since, unlike selection and information bias, confounding stems from real causal effects.

To date, few universities offer epidemiology as a course of study at the undergraduate level. One notable undergraduate program exists at Johns Hopkins University , where students who major in public health can take graduate level courses, including epidemiology, their senior year at the Bloomberg School of Public Health.

Although epidemiologic research is conducted by individuals from diverse disciplines, including clinically trained professionals such as physicians, formal training is available through Masters or Doctoral programs including Master of Public Health MPH , Master of Science of Epidemiology MSc.

Many other graduate programs, e. Reflecting the strong historical tie between epidemiology and medicine, formal training programs may be set in either schools of public health and medical schools. Some epidemiologists work 'in the field'; i. Epidemiologists can also work in for-profit organizations such as pharmaceutical and medical device companies in groups such as market research or clinical development.

From Wikipedia, the free encyclopedia. For other uses, see Epidemiology disambiguation. See also: History of emerging infectious diseases. Main article: Study design. Main article: Causal inference. Main article: Bradford Hill criteria. Medicine portal. A Dictionary of Epidemiology 6th ed. New York: Oxford University Press.

Retrieved 16 July Ecosys Health. Airs, Waters, Places. Scientific Publication No. Pan American Health Organization. Washington, DC. A history of epidemiologic methods and concepts. Chapter 2. Merrill Introduction to Epidemiology. Chapter 2: Historic Developments in Epidemiology. Jones and Bartlett Publishing, Retrieved 3 February Encyclopedia of the Black Death. Retrieved 24 February Wordl Health Organisation. Archived from the original on 8 June Gro Harlem Brundtland, M. Geneva, Switzerland Talk, Washington, D. John Snow. John Snow, Inc.