Epidemiology

The epidemiology (of ancient Greek ἡ νούσος επιδημια, Epidemia nosos " epidemic , endemic disease " and -logie literally "the doctrine of what about the people coming") is that scientific discipline that deals with the distribution and the causes and consequences of health-related conditions and events in populations or populations . This is what distinguishes epidemiology from clinical medicine , which is about helping a single person in a specific case of illness. Even if medical professionals have previously dealt with the spread and causes of diseases, the beginning of scientific epidemiology is dated to the middle of the 19th century.

Example of an epidemic: illnesses and deaths (black) in the course of the Ebola fever epidemic in West Africa up to July 2014 (approximately logistical function )

The core of the epidemiological approach is the quantitative determination of the frequency of events and the burden of disease in a population. The frequency of occurrence can be determined using the observed magnitude of the incidence . The prevalence is the measure of the spread of diseases in the population of a place and time defined population. Epidemiology continues to study the factors that contribute to the health and disease of individuals and populations, and thus lays the basis for many measures taken in the interests of the health of the population . Epidemiological methods form the basis of clinical studies . Epidemiological studies also play a role in sociology and psychology , e.g. B. in behavioral disorders , autism and suicide . In this way, connections with the spread of these phenomena can be recorded and possibly influenced.

The term population or population does not exclusively refer to human populations; animals and plants also form populations. So veterinary epidemiology or epizootiology studies the spread of diseases in animal populations; botanical epidemiology studies diseases on plants. The One Health concept considers the health of all living things in their interdependence and examines their disorders together.

Subject of epidemiology

Epidemiology is part of the health sciences , the group of specialist disciplines that deal with health and diseases, causes of diseases and suitable preventive or therapeutic measures. In order to get to the bottom of the question of possible cause-effect relationships, statistical methods and measures are used. The specified cause and the disease of interest can be modeled as an effect using mathematical-statistical models. Analytical epidemiology combines these statistical methods with the knowledge and procedures of clinical medicine, descriptive epidemiology is also known as health statistics . If you notice that a disease is increasing or exceeding a certain level, you can take targeted measures. Defined situations are fought with defined actions. The quantification also enables an objective assessment of the efficiency of an intervention .

Like any other scientific discipline, epidemiology works in an interdisciplinary manner: outside of its own core stock of knowledge, terms and methods, it relies on the findings of other disciplines: medicine , veterinary medicine , statistics , biology , sociology , psychology and computer science and others.

Epidemiology deals with all types of diseases and with the factors that influence health and disease, and no longer, as in its early days, only with epidemics as a temporally and spatially limited increase in the incidence of infectious diseases . Epidemiology does practical work in the study of factors that affect health and disease. These factors can lie in the individual, his genetics, life history and behavior, as well as in the physical, biological and social world, the environment. Epidemiological knowledge of risk factors is the basis of health promotion . Epidemiology works with observational and experimental studies. For example, relationships between possible causes such as diet , social status , stress and environmental chemicals as well as consequences such as illness and well-being can be quantified.

Mathematical models are very important to determine the likelihood of future epidemics and their course. They also help with planning vaccination campaigns . See also Mathematical Modeling of Epidemiology .

Epidemiological studies are generally divided into descriptive , analytical and experimental activities. Some scientists work in the field of public health , others at scientific institutions, in clinics or in development aid. Epidemiologists are indispensable when new diseases such as SARS , H5N1 avian influenza and H7N9 avian influenza emerge .

Epidemiological sub-areas

List of the working groups of the German Society for Epidemiology (DGEpi): Infection epidemiology, epidemiology of allergic and dermatological diseases, epidemiology of the world of work, epidemiological methods, nutritional epidemiology, genetic epidemiology, cardiovascular epidemiology, cancer epidemiology, statistical methods in epidemiology, environmental medicine.

Other sub-areas are outbreak epidemiology , oral epidemiology , pharmacoepidemiology and social epidemiology .

Epidemiological indicators

These key figures facilitate an overview of the situation of the population or of the spread of a specific disease. If a key figure exceeds a certain level, you can take targeted measures. Defined situations are thus combated with defined actions. This also facilitates an objective assessment of the efficiency of an intervention .

Prevalence

→ Main article : Prevalence

The prevalence of a disease indicates the proportion of affected individuals in the population under consideration. After Checkoway u. a. 1989 can be distinguished more precisely between "Prevalence at a time point" or point prevalence ( English point prevalence ) and "prevalence over a period" or period prevalence ( English period prevalence ). Due to the problematic interpretation of the period prevalence, one usually concentrates on the point prevalence, which is usually what is meant when one only speaks of prevalence.

The prevalence is usually represented as a quotient - namely the number of current cases in a population (e.g. sick, deceased, malnourished, etc. regardless of duration) divided by the number of all members of this population. Prevalence as a measure of the incidence of a disease should not be confused with the incidence rate - the measure of the occurrence of new cases of disease in a population.

Example: As of January 1, 2002, 1,024 employees in a certain company had back problems. With a total of 15,000 employees, the prevalence is 0.068 or 6.8 percent.

risk

Than risk the probability is referred to for the occurrence of an event during a specified period; New illnesses or deaths are typically regarded as events. For example, if you followed a group of 1,000 people over a 15-year period and found that 20 people died during those 15 years, the 15-year risk would be 20 / 1,000.

The risk of developing new diseases is also known as the cumulative incidence . The lifetime risk describes the probability of falling ill (at least) once in a lifetime and is therefore a special cumulative incidence; however, an alternative term is lifetime prevalence .

In order to identify risk factors, populations are compared that differ in only one of the investigated properties, if possible; then (absolute) risk differences and relative risks can be calculated. Risk factors provide clues about the causes of diseases; however, there does not have to be a causal relationship; in particular in observational studies , the effect can also result from distortion (bias) or confounding .

Attributable risk

→ Main article : Attributable risk

The attributable risk helps to estimate how much a certain factor contributes to a certain disease. A specific question could be: How strong is the influence of 10 cigarettes a day on the risk of lung cancer ?

The answer to that is:

${\displaystyle {\text{Attributionelles Risiko}}={\text{Risiko}}_{10}-{\text{Risiko}}_{0}}$

In principle, the risks of people who smoke either 10 or 0 cigarettes per day are compared with one another. The risk of non-smokers is, so to speak, the “residual risk” that (often) cannot be avoided and therefore does not deserve any further attention.

Incidence density

→ Main article : Incidence (epidemiology)

The incidence density is the number of new cases divided by the time spent under the risk of disease people time in a population; Instead of diseases, other defined events can also be considered. The reciprocal of the incidence rate is the average time for an individual to develop the disease.

Ratio of prevalence and incidence

→ Main article : Incidence (epidemiology) # Relationship between incidence density and prevalence

Isn't the incidence as a key figure superfluous if the key figure for the prevalence already exists? No, the prevalence helps, for example, to calculate the health care costs of the accident victims in a specific occupational group (i.e. counting the accident victims who are in treatment / rehabilitation at time X). However, the incidence (rate) gives accident prevention different information: It does not matter how long someone has to be treated for accident damage (which is reflected in the prevalence), but how many accidents occur. A misfortune averted means that a treatment was saved that could have been very short as well as very long.

You can imagine these two indicators as a well. The inflow into the well trough is the incidence rate of the disease, and the contents of the well trough is the prevalence, i.e. the constant occurrence of the disease. The two outflows from the trough are the incidence rate of healing and the incidence rate of death. In steady state (the same amount flows into the well as it flows out, steady state ):

${\displaystyle {\text{Inzidenzrate}}_{\text{Erkrankung}}\cdot {\text{Anzahl}}_{\text{Gesunde}}=({\text{Inzidenzrate}}_{\text{Heilung}}+{\text{Inzidenzrate}}_{\text{Tod}})\cdot \mathrm {Anzahl} _{\text{Kranke}}}$

Reproduction number

→ Main article : Basic reproduction number

The basic reproduction number R ₀ (sometimes also called the basic reproduction rate ) indicates how many people an already sick person will infect on average if the affected population is neither vaccinated nor otherwise protected from transmission. The net reproduction number R _{t also} takes into account human immunity and the influence of control measures. In order to contain an epidemic, the net reproduction number must be reduced to the value 1 (every case of infection leads to a subsequent case, i.e. no increase in the number of sick people). To combat the spread of the disease, a net reproduction rate of less than 1 is necessary; the closer the value goes to 0, the more successful the fight.

Examples of basic reproduction numbers:

• Malaria : more than 1000
• Measles : 15 to 18
• Polio : 6 to 8

${\displaystyle R_{t}=R_{0}\cdot (100\,\%-n\%)/100\%}$

R 0 : basic reproduction number

R t : net reproduction number (effective reproduction number)

n%: Percentage of the population in which transmission does not take place because appropriate precautions have been taken to prevent transmission from person to person or because they have been vaccinated or otherwise immunized ( immunization rate )

From this formula it follows that for malaria 99.9%, for measles around 94% and for polio ( polio ) around 86% of the population have to be immune for the disease to persist in the endemic state or even to be eradicated. Falling below the vaccination coverage results in local epidemics.

Therefore, the question “Should I vaccinate my child?” Does not only concern the health of the individual child, but also that of the entire population. A sick child very rarely dies from a childhood disease such as rubella or measles, but the infection can spread.

An extreme example of different reproductive numbers of the same disease is given for malaria: In Africa the disease is devastating, in India it is a manageable problem ( see Anopheles: Malaria in Kenya and Punjab (India) ).

For more mathematical backgrounds and models see:

• Mathematical modeling of epidemiology
  • SI model (contagion without recovery)
  • SIS model (spread of infectious diseases without building up immunity)
  • SIR model (spread of infectious diseases with immunity formation)
  • SEIR model (spread of contagious diseases with immunity development, in which infected people are not immediately infectious)

Epidemiological methods and study types

In general, one would like to use epidemiological methods and studies to determine the relationship between exposure to risk factors and disease. A risk factor can be smoking, fatty food or a certain social environment that increases the likelihood of illness. Analogous to the risk factor, one speaks of the “protective factor”, which reduces it. Regular exercise and fruit are z. B. protective factors for cardiovascular diseases, breastfeeding protects babies from infections. In addition to disease status, underlying illnesses, age and gender, the general data collected often includes smoking behavior and educational level. A distinction is made between observational studies (cross-sectional study, cohort study, case-control study) and intervention studies.

• Cross-sectional studies (Engl. Cross sectional study ) identify a snapshot of the examined epidemiological data. Due to the temporal “snapshot” of the epidemiological data, the causal relationships between exposure and disease drawn from the study are weak and serve more to generate hypotheses than to verify them.
• Longitudinal studies (Engl. Longitudinal study ) is a generic term for studies that regularly collect data of the study population over a longer period of time. They correspond to cross-sectional studies carried out periodically.
• Cohort studies (Engl. Cohort studies ) study defined groups of people with and without exposure to a risk factor over a long period of time and measure at the end of the observation period the disease status. The risk of the exposed person for this disease can be measured from the number of sick people among those exposed divided by the total number of people exposed. The same applies to those not exposed. The ratio of the risk of the exposed to the risk of the non-exposed is the risk ratio (also called the relative risk or English risk ratio ) and indicates how much the exposure increases the risk of the disease. For example, smoking 20 cigarettes a day compared to non-smoking increases the risk of developing lung cancer by a factor of 15. In prospective cohort studies, the start of the study and the start of the observation period are close to one another The disease status is still unknown. Retrospective cohort studies already look at past cohorts, here the observations have already been completed and the disease status is already known. They are easier and cheaper to carry out than prospective cohort studies, but also more prone to bias , especially when recruiting study participants, which was in the past and can no longer be influenced. Examples of cohort studies would be the investigation of lung cancer in asbestos workers (exposed group) in a company and their office workers (non-exposed group).

	Number of sick people	Number of healthy people
Number of exposed persons	a	b
Number of non-exposed persons	c	d

${\displaystyle {\text{Risikoverhaeltnis}}={\frac {\frac {a}{a+b}}{\frac {c}{c+d}}}}$

• Case-control studies (Engl. Case control study ) go methodically the opposite way a cohort study. In a case-control study, disease status is known and exposure is unknown. It is particularly suitable for rare diseases, since a cohort study would have to have a large number of participants in order to reach a statistically sufficient number of patients. The study population of the case-control study consists of sick and healthy people, although for statistical reasons there can be two or more healthy people for one sick person (1: 2 matching, 1: n matching). The exposure is only recorded after allocation to the two groups in order to rule out any influence on the result by the observers. The chance ( odd ) of the sick person to be exposed is evaluated . It results from the number of sick people with exposure divided by the number of sick people without exposure ('not' the total number of sick people). Similarly, the chance of healthy people to be exposed is calculated. The chance of the sufferers by the chance of Healthy Division gives the odds ratio (Engl. Odds ratio ). It corresponds to the factor that increases the chance of getting sick from exposure. In a case-control study, one has to calculate the odds ratio and not the risk ratio, because choosing the number of controls would distort the denominator of the risk term (the sum of a + b). On the other hand, doubling the number of controls in the odds ratio would be mathematically reduced (twice as many in the numerator as in the denominator).

In rare diseases, the odds ratio corresponds to the risk ratio. Case-control studies are generally retrospective.

	Number of sick people	Number of healthy people
exposed	a	b
not exposed	c	d

${\displaystyle {\text{Odds Ratio}}={\frac {a\cdot d}{c\cdot b}}={\frac {\frac {a}{c}}{\frac {b}{d}}}}$

• Intervention studies, similar to a prospective cohort study, track a population over time, with the aim of measuring the influence of a specific intervention, usually a new treatment or a new drug, on the risk of disease. Before the study, the population is divided into the intervention branch and the control branch. This intervention (e.g. drug) is then actively given during the study, while the control population remains untreated or receives non-effective treatment (e.g. placebo ). The evaluation is carried out in a similar way to a case control study of odds ratios . The assignment to the treatment group and control group is the critical point of an intervention study, as the participants differ in their health parameters and one only wants to measure the influence of the intervention and not these parameters. If this selection is made randomly and therefore not directed, one speaks of a randomized controlled trial. These studies have a particularly strong causality with regard to intervention and disease status and are therefore used in drug testing.
• The palaeopathology provides facts to supply and spread and symptoms of disease in historic and prehistoric eras; Research into extinct strains of pathogens is also possible, especially on the basis of studies of old DNA . Also, thanks to skeletal remains, symptoms and diseases can be diagnosed, such as osteolytic inflammation.
• The ongoing epidemiological surveillance (surveillance) of the health authorities shows short and long-term developments in the spread of infectious and other diseases.
• Molecular epidemiology based on laboratory data.

Furthermore, a basic distinction can be made between the following epidemiological study types:

• Descriptive epidemiology
• Analytical epidemiology
• Experimental epidemiology
• Molecular Epidemiology
• Genetic Epidemiology

In connection with cancer registries , one also speaks of:

• applied epidemiology.

Endemic, epidemic and pandemic

→ Main articles : Endemic , epidemic  and pandemic

The endemic is the normal, usual occurrence of a particular disease in a particular population. A certain proportion of flu illnesses in the population is common and if a certain limit is exceeded - with flu around 10% - this is called an epidemic . From the definition of endemic it follows that the epidemic is the unusually strong and temporary occurrence of a disease.

The pandemic , like the epidemic, is a major outbreak of disease beyond expected levels, however the epidemic is still limited to specific areas. Pandemics, on the other hand, span countries and continents. The pandemic planning of the World Health Organization (WHO) has served to prevent and, if necessary, contain them since 1999 and, based on this, ideally a national pandemic plan for each country (see National Pandemic Plan for Germany ).

Please note that for the classification of diseases as endemic, epidemic or pandemic, only the frequency of occurrence is decisive and not the course or the severity of the diseases.

Epidemiological network of relationships

→ Main articles : Socially determined inequality of health opportunities , social medicine  and social epidemiology

Epidemiology also looks at the social , geographical and economic environment of diseases, while medicine mostly restricts itself to direct factors such as viruses and bodily harm. In epidemiology, it is not sufficient to simply state that the HIV virus causes the disease AIDS . Epidemiologists examine the wider environment in which each condition influences other factors.

For example:

• In this country, the climate enables food to be grown, which prevents malnutrition . Once people have become healthier with good nutrition, they can go to school more often instead of staying at home sick.
• Improved schooling can result in children getting better jobs and earning more as adults, which enables them to receive better health care or to move to an area free from malaria, for example.
• Free health care for everyone enables parents to have all their offspring cared for instead of just the eldest son, who will inherit the father's business in the future. In developing countries, epidemiologists often try to organize health care in such a way that the family as a whole remains as productive as possible.

history

Hippocrates (quoted from Galenus von Pergamon ) writes: “Epidemic is a disease that is particularly common in the same region at the same time. The opposite of this is formed by sporadic diseases. ”This old definition of the epidemic forms the historical starting point. The history of epidemiology begins with the search for the causes of epidemics. The outdated term loimology for infection epidemiology or epidemiology clearly points to this connection.

During the plague epidemic of 1483/84, Konrad Schwestermüller (around 1450–1520), the personal physician of Johann Cicero von Brandenburg, proved to be an excellent epidemiologist, who was also an advisor from the Mecklenburg court (under the dukes Magnus and Balthasar ) during the epidemic of 1490 / 92 was consulted. In 1484 he wrote a plague for the prevention and differentiated treatment of the epidemic, which was also addressed to the entire population in the 17th century by the urban epidemic prophylaxis in Berlin.

In the early 18th century, Giovanni Maria Lancisi (1654–1720), who worked in Rome as the Pope's personal physician , attributed the decline in various diseases - including malaria - to improved hygiene and the draining of swamps. This decline in infectious diseases as a result of hygiene measures is also known as the first epidemiological transition .

Cholera outbreak map prepared by Dr. Snow.

The beginning of modern epidemiology is dated to the middle of the 19th century: In 1854 John Snow successfully combated a cholera outbreak in London's Soho district because he recognized from a mapping of the disease cases that a public water catchment was the source of the infection . He had the polluted well closed, and the number of cases of illness then decreased significantly. Snow's findings on the cause of cholera, which he had developed together with doctor and microbiologist Arthur Hill Hassall , were only widely accepted after Snow's death. The British statistician William Farr was instrumental in this .

Polar area diagram , with the help of which Florence Nightingale represented the causes of death during the Crimean War:
blue: those who died from infectious diseases
red: those who died from wounds
black: other causes of death

Florence Nightingale (1820-1910), who is considered to be one of the founders of Western nursing , also belonged to William Farr . During the Crimean War she ensured a rudimentary hospital operation in Scutari , the central British military hospital during this war in the Selimiye barracks , and found out, among other things, that the majority of British victims of the Crimean War were not due to wounds but to infectious diseases. She used the fame that her work in the Crimean War brought her to influence numerous British health reforms. Due to an illness that she contracted during the Crimean War, she was unable to get an idea of ​​the situation in a barracks, hospital or poor house for herself. She therefore concentrated on collecting data, processing and analyzing it in order to then draw conclusions from it. Questionnaires were an essential tool for her, and she also made use of existing data, such as the official government reports known as blue books and statements by British authorities. Among other things, it documented serious problems in military health care: Although British soldiers were usually between 20 and 35 years old and thus belonged to an age group with a low mortality rate, in peacetime they had a mortality rate almost twice as high as civilians. Nightingale found clear words for this in her report to the British government. If 11 out of 1,000 civilians died annually, but 17, 19 and 20 out of 1,000 soldiers of the line infantry, artillery and guards stationed in England, then that would be as criminal as taking 1,100 men annually to Salisbury Plain and shooting them there. Florence Nightingale is considered to be one of the pioneers in the graphic preparation of such data.

Other pioneers were the Danish doctor Peter Anton Schleisner , who in 1849 worked to end the tetanus neonatorum epidemic on the Westman Islands through preventive measures, and the Hungarian doctor Ignaz Semmelweis , who in 1847 recognized the lack of hygiene as the cause of the often fatal puerperal fever and tried to combat it by introducing consistent hygiene measures. However, Semmelweis' findings were not accepted by the professional world for a long time, because at that time the assumption that there were microorganisms that cause illness - namely bacteria - was considered ridiculous.

The first physicians to apply the epidemiological approach not only to infectious diseases, but to cancer, were Walther Hesse and Friedrich Härting in the late 1870s . Walther Hesse was appointed district doctor for the Schwarzenberg district in the Ore Mountains in 1877 . Among other things, he was responsible for 83 villages in which mostly miners lived. Hesse was shocked by her poor health and the short age that miners typically reached. As early as 1567, Paracelsus had described the occurrence of lung diseases in this area, which he described as mountain addiction . However, the cause of the disease was unknown. Together with the mine doctor Härting, Hesse began to compile individual cases of illness, interview miners, take environmental measurements and ultimately also carry out 20 autopsies. At the end of their investigation it was clear that there was an accumulation of cancer cases among the miners, the cause of which was related to their work. Hesse and Härting suspected as the cause of so-called disease Schneeberger asbestos dust, only later scientists were able to prove that the trigger due to the particular geology closely with the resort BiCoNi ores overgrown uranium ores were. The work that Hesse and Härting had done in Schneeberg was exemplary for a number of other scientists. The best known of these is the work of Ludwig Rehn , who was able to prove in 1895 that there was a connection between work in an aniline processing industry and the occurrence of bladder cancer .

An important milestone in the history of epidemiology (and also of parasitology ) is the discovery of the hookworm , Ancylostoma duodenale , in 1880 during the construction of the Gotthard railway tunnel , as the cause of so-called St. Gotthard's disease - a parasitic anemia . On the basis of the epidemiological findings, the working and hygienic conditions were then improved.

The disinfectant was only widely applied in medicine, when the British surgeon Joseph Lister antiseptic agents discovered based on the work of Louis Pasteur .

See also

• Infection epidemiology
• immunology
• Incubation Time - Serial Interval
• Health reporting
• Global Health Security Index
• Positive predictive value - conditional probability - specificity
• sensitivity

Web links

Wiktionary: Epidemiology  - explanations of meanings, word origins, synonyms, translations

• Uni Basel: Introduction to Epidemiology
• German Society for Epidemiology (DGEpi)
• Austrian Society for Epidemiology (OeGEpi)
• German Society for Medical Informatics, Biometry and Epidemiology (GMDS)
• International Epidemiological Association (IEA)
• People's Epidemiology Library

literature

• KJ Rothman: Epidemiology: An introduction. Oxford University Press, 2002, ISBN 0-19-513554-7 .
• M. Porta, S. Greenland, M. Hernán, I. dos Santos Silva, JM Last (Eds.): A dictionary of epidemiology. 6th edition, Oxford University Press, New York 2014, ISBN 978-0-19-997673-7 .
• Lothar Kreienbrock, Siegfried Schach: Epidemiological Methods . 4th edition. Spectrum Academic Publishing House, 2005, ISBN 3-8274-1528-4 .
• Alexander Krämer, Ralf Reintjes (eds.): Infection epidemiology - methods, surveillance, mathematical models, global public health . Springer, Berlin 2003, ISBN 3-540-42764-3 (with CD-ROM).
• R. Beaglehole, R. Bonita, T. Kjellström: Introduction to Epidemiology . Huber, Bern 1997, ISBN 3-456-82767-9 .
• H. Checkoway, N. Pearce, DJ Crawdorf-Brown: Research methods in occupational epidemiology . Oxford University Press, New York 1989, ISBN 0-19-505224-2 .
• P. Armitage, G. Berry: Statistical Methods in Medical Research . Blackwell Scientific Publications, Oxford 1987.
• JWR Twisk: Applied Longitudinal Data Analysis for Epidemiology . Cambridge University Press, Cambridge 2003, ISBN 0-521-52580-2 .
• J. Hardin, J. Hilbe: Generalized Linear Models and Extensions. Stata Press, College Station TX 2001.
• Leon Gordis: epidemiology. Verlag im Kilian, ISBN 3-932091-63-9 .
• Wolfgang Ahrens, Iris Pigeot (Ed.): Handbook of Epidemiology. Springer, Berlin / Heidelberg 2005, ISBN 3-540-00566-8 .
• Christel Weiß: Basic knowledge of medical statistics. 5th edition (with epidemiology). Springer, Berlin / Heidelberg 2010, ISBN 978-3-642-11336-9 .

Individual evidence

1. ↑ Ludwig August Kraus: Kritisch-etymologisches medicinisches Lexikon , 3rd edition, Verlag der Deuerlich- und Dieterichschen Buchhandlung, Göttingen 1844, p. 371.
2. ↑ Lothar Kreienbrock, Iris Pigeot and Wolfgang Ahrens: Epidemiological Methods. 5th edition. Springer Spectrum, Berlin / Heidelberg 2012, ISBN 978-0-19-975455-7 , foreword.
3. ↑ Epidemic on Psychyrembel online
4. ↑ J.-B. du Prel1, B. Röhrig, G. Weinmayr1: What is epidemiology? on thieme-connect.de
5. ↑ Wolfgang Kiehl: Infection protection and infection epidemiology. Technical terms - definitions - interpretations. Ed .: Robert Koch Institute, Berlin 2015, ISBN 978-3-89606-258-1 , p. 16, keyword outbreak
6. ↑ Checkoway u. a .: Research methods in occupational epidemiology. 1989.
7. ↑ History and Epidemiology of Global Smallpox Eradication ( Memento of the original from July 15, 2007 in the Internet Archive ) Info: The archive link was inserted automatically and has not yet been checked. Please check the original and archive link according to the instructions and then remove this notice. 
8. ↑ Revisiting the Basic Reproductive Number for Malaria and Its Implications for Malaria Control doi: 10.1371 / journal.pbio.0050042 .
9. ↑ Preventive medicine, epidemiology and social medicine: for medicine and dentistry . Facultas, 2007, ISBN 978-3-7089-0094-0 , pp. 18–26 ( limited preview in Google Book search).
10. ↑ Psychiatry and Psychotherapy . Springer-Verlag, 2008, ISBN 978-3-540-33129-2 , pp. 57 ( limited preview in Google Book search).
11. ↑ Pathology: with over 200 tables . Elsevier, Urban & Fischer Verlag, 2008, ISBN 978-3-437-42382-6 , pp. 32–33 ( limited preview in Google Book search).
12. ↑ Repetitorium Pathologie: with 161 tables . Elsevier, Urban & Fischer Verlag, 2004, ISBN 978-3-437-43400-6 , pp. 8 ( limited preview in Google Book search).
13. ^ Influenza Pandemic Plan. The Role of WHO and Guidelines for National and Regional Planning. On: who.int , Geneva, April 1999.
14. ^ Ludwig August Kraus, at the place indicated.
15. ↑ Loimology on Pschyrembel online
16. ^ Konrad Schwestermüller: Regiment and lere against the swaren sickness of the pestilentz.
17. ^ Wolfgang Wegner: Schwestermüller, Konrad. In: Werner E. Gerabek , Bernhard D. Haage, Gundolf Keil , Wolfgang Wegner (eds.): Enzyklopädie Medizingeschichte. De Gruyter , Berlin, New York 2005, ISBN 3-11-015714-4 , p. 1312.
18. ^ Stephanie J. Snow: Death by Water. John Snow and the cholera in the 19th century. (PDF; 204 kB) Retrieved May 6, 2014 . 
19. ↑ Amanda J. Thomas: The Lambeth Cholera Outbreak of 1848–1849: The Setting, Causes, Course and Aftermath of an Epidemic in London. McFarland, 2009, ISBN 978-0-7864-5714-4 , p. 37 f.
20. ↑ Mark Bostridge: Florence Nightingale . Penguin Books, London 2009, ISBN 978-0-14-026392-3 .
21. ↑ Mark Bostridge: Florence Nightingale . Penguin Books, London 2009, ISBN 978-0-14-026392-3 , p. 407.
22. ↑ In the original this quote is [It is just as criminal]… to have a mortality of 17, 19 and 20 per thousand in the Line, Artillery and Guards in England, when that of Civil life is only 11 per 1,000, as it would be to take 1,000 men per annum out upon Salisbury Plain and shoot them , Florence Nightingale in Notes on matters affecting ... , quoted from Mark Bostridge: Florence Nightingale . Penguin Books, London 2009, ISBN 978-0-14-026392-3 , p. 314.
23. ^ ^{A b} Dan Fagin : Toms River: A Story of Science and Salvation . Bantam Books, New York 2014, ISBN 978-0-345-53861-1 , pp.  127 .
24. ^ Dan Fagin : Toms River: A Story of Science and Salvation . Bantam Books, New York 2014, ISBN 978-0-345-53861-1 . P. 125.
25. ^ Theophrastus Paracelsus von Hohenheim : On Bergsucht or Bergkranckheiten three books, in three tracts, written and described. This includes the origin and origin of the same diseases, as well as their successful preseruatiua and cures. All of them, miners, smelters, tasters, mills masters, goldsmiths, and alchemists, and all those who work in metals and minerals, are highly useful, comforting and indispensable. Ed .: Samuel Zimmermann. Sebaldus Mayer, Dillingen 1567.

This version was added to the list of articles worth reading on July 16, 2005 .