Quantitative Epidemiology

1996 ◽  
Vol 17 (4) ◽  
pp. 249-255 ◽  
Author(s):  
Jonathan Freeman
Keyword(s):  
Quantitative Epidemiology

Quantitative Epidemiology

1996 ◽  
Vol 17 (4) ◽  
pp. 249-255
Author(s):  
Jonathan Freeman
Keyword(s):  
Cohort Study ◽  
Cohort Studies ◽  
Quantitative Methods ◽  
Case Control Study ◽  
Experimental Studies ◽  
Case Control ◽  
Case Control Studies ◽  
Hospital Epidemiology ◽  
Quantitative Epidemiology ◽  
Control Study

AbstractWe provide guidance for new practitioners in the vocabulary of modern epidemiology and the application of quantitative methods. Most hospital epidemiology involves surveillance (observational) data that were not part of a planned experiment, so the rubric and logic of controlled experimental studies cannot be applied. Forms of incidence and prevalence often are confused. The names “cohort study” and “case-control study” are unfortunate, as cohort studies rarely involve cohorts and case-control studies allow no active control by the investigator. Either type of study can be prospective or retrospective. Results of studies with discrete outcomes (infected or not, lived or died) often are represented best by a form of the risk ratio with 95% confidence intervals. The potential distorting effects of selection bias, misclassification, and confounding need to be considered.

Information science approach to biomathematics and its implications to ecology and quantitative epidemiology

1971 ◽  
Vol 3 (2) ◽  
pp. 198-200
Author(s):  
Tosio Kitagawa
Keyword(s):  
Mathematical Analysis ◽  
System Approach ◽  
Information Science ◽  
General System ◽  
Automaton Theory ◽  
Historical Approach ◽  
International Conference ◽  
Quantitative Epidemiology ◽  
Science Approach ◽  
Characteristic Features

The paper consists of three parts A, B and C. In part A, “A contribution to the methodology of biomathematics”, general considerations on mathematical analysis and automaton theory are given in order to show how and why new approaches for biomathematics are required from the standpoint of information science. In fact three requirements are mentioned: (1°) general system approach; (2°) historical approach; (3°) three indispensable constituent fields of biological science. Regarding (1°), the author refers to his work on the logic of information science explained in 1968 at the CAS Seminar in Dubrovnik and published in 1969 in a Japanese monograph. Regarding (2°), reference is made to his paper “A contribution to the methodology of futurology” published in International Conference on Futurology, April 1970, Kyoto. The three indispensable constitutent fields are given by (a) theoretical, (b) experimental and (c) engineering biologies. In view of these three requirements, characteristic features of biomathematics in connection with an information science approach are (1°) discrete, (2°) combinatorial, (3°) dynamical, (4°) evolutionary, and (5°) design mathematics.

scholarly journals Quantitative epidemiology of Banana Bunchy Top Virus Disease and its control

1998 ◽  
Vol 47 (2) ◽  
pp. 177-187 ◽  
Author(s):  
M. C. Smith ◽  
J. Holt ◽  
L. Kenyon ◽  
C. Foot
Keyword(s):  
Virus Disease ◽  
Banana Bunchy Top Virus ◽  
Quantitative Epidemiology

Assessing the impact of diseases and their control on livestock productivity: the rôle of quantitative epidemiology

1998 ◽  
Vol 21 ◽  
pp. 111-119
Author(s):  
B. D. Perry ◽  
J. J. McDermott
Keyword(s):  
Impact Assessment ◽  
Geographical Information Systems ◽  
Research Priorities ◽  
Field Studies ◽  
Geographical Information ◽  
Quantitative Epidemiology ◽  
Infection Dynamics ◽  
Increasing Demand ◽  
Livestock Productivity ◽  
The Impact

AbstractIn determining research priorities, as well as in evaluating the relative merits of different disease control options, there is an increasing demand for impact assessment, in order to justify and support research and development investments. In this process, quantitative epidemiology has a significant role to play. This paper first outlines the categories of impacts that diseases have on society and discusses how these can be measured. Using the example of tick-borne infections of livestock, the paper describes five quantitative epidemiological tools that can be used in impact assessment. These are: geographical information systems (GIS) to estimate disease distribution; mathematical models to quantify infection dynamics; observational field studies to link infection status with production loss; statistical models to enhance study design; and spreadsheet models for economic impact assessment. In each category, examples are given of recent studies.

Who symposium on quantitative epidemiology

1971 ◽  
Vol 3 (2) ◽  
pp. 193-193
Keyword(s):  
Public Health ◽  
Communicable Disease ◽  
Theoretical Development ◽  
Mathematical Work ◽  
Health It ◽  
Mathematical Research ◽  
Medical Sciences ◽  
Quantitative Epidemiology ◽  
Research Institutes

This Symposium was organized “to explore existing mathematical theories of disease and the possibilities offered to the medical sciences by further research in this field”. Because of the extreme breadth of these terms of reference, the discussion was limited to problems of communicable disease. The intention was to bring academic mathematical research into closer contact with applied problems in public health. It was thought that in this way, WHO would have greater access to theoretical development, while mathematical work in Universities and research institutes would achieve closer contact with practical problems.

Quantitative Epidemiology

1996 ◽  
Vol 17 (4) ◽  
pp. 249-255 ◽  
Author(s):  
Jonathan Freeman
Keyword(s):  
Quantitative Epidemiology

Information science approach to biomathematics and its implications to ecology and quantitative epidemiology

1971 ◽  
Vol 3 (02) ◽  
pp. 198-200
Author(s):  
Tosio Kitagawa
Keyword(s):  
Mathematical Analysis ◽  
System Approach ◽  
Information Science ◽  
General System ◽  
Automaton Theory ◽  
Historical Approach ◽  
International Conference ◽  
Quantitative Epidemiology ◽  
Science Approach ◽  
Characteristic Features

The paper consists of three parts A, B and C. In part A, “A contribution to the methodology of biomathematics”, general considerations on mathematical analysis and automaton theory are given in order to show how and why new approaches for biomathematics are required from the standpoint of information science. In fact three requirements are mentioned: (1°) general system approach; (2°) historical approach; (3°) three indispensable constituent fields of biological science. Regarding (1°), the author refers to his work on the logic of information science explained in 1968 at the CAS Seminar in Dubrovnik and published in 1969 in a Japanese monograph. Regarding (2°), reference is made to his paper “A contribution to the methodology of futurology” published in International Conference on Futurology, April 1970, Kyoto. The three indispensable constitutent fields are given by (a) theoretical, (b) experimental and (c) engineering biologies. In view of these three requirements, characteristic features of biomathematics in connection with an information science approach are (1°) discrete, (2°) combinatorial, (3°) dynamical, (4°) evolutionary, and (5°) design mathematics.

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