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001			ocn812289802
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005			20221128211910.0
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007			cr cnu---unuuu
008			121009s2013 njua ob 101 0 eng d
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020			_a1400845629 _q(electronic bk.)
020			_a1283578751
020			_a9781283578752
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037			_aCL0500000186 _bSafari Books Online
037			_a22573/cttp257s _bJSTOR
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084			_aSCI008000 _aMAT003000 _aMED022090 _2bisacsh
049			_aMAIN
100	1		_aDiekmann, O., _eauthor.
245	1	0	_aMathematical tools for understanding infectious diseases dynamics _c _h[E-Book]
246	3		_aMathematical tools for understanding infectious disease dynamics
260			_aPrinceton : _bPrinceton University Press, _c2013.
300			_a1 online resource (xiv, 502 pages) : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aPrinceton series in theoretical and computational biology
504			_aIncludes bibliographical references (pages 491-496) and index.
520			_a"Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods. Mathematical Tools for Understanding Infectious Disease Dynamics fully explains how to translate biological assumptions into mathematics to construct useful and consistent models, and how to use the biological interpretation and mathematical reasoning to analyze these models. It shows how to relate models to data through statistical inference, and how to gain important insights into infectious disease dynamics by translating mathematical results back to biology. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided. Covers the latest research in mathematical modeling of infectious disease epidemiology Integrates deterministic and stochastic approaches Teaches skills in model construction, analysis, inference, and interpretation Features numerous exercises and their detailed elaborations Motivated by real-world applications throughout "-- _cProvided by publisher.
588	0		_aPrint version record.
505	0	0	_tFrontmatter -- _tContents -- _tPreface -- _tPart I. The bare bones: Basic issues in the simplest context -- _tPart II. Structured populations -- _tPart III. Case studies on inference -- _tPart IV. Elaborations -- _tBibliography -- _tIndex
590			_aWorldCat record variable field(s) change: 650
650		0	_aEpidemiology _xMathematical models _vCongresses.
650		0	_aEpidemiology _xMathematical models.
650		0	_aCommunicable diseases _xMathematical models.
650		0	_aEpidemiology.
650		0	_aMathematical models.
650		0	_aCommunicable diseases.
650		2	_aEpidemiology
650		2	_aModels, Theoretical _97017
650		2	_aCommunicable Diseases
700	1		_aHeesterbeek, Hans, _d1960- _eeditor.
700	1		_aBritton, Tom, _eeditor.
830		0	_aPrinceton series in theoretical and computational biology.
856	4	0	_uhttps://search.ebscohost.com/login.aspx?custid=ns123844&authtype=ip,shib&direct=true&scope=site&db=nlebk&db=nlabk&AN=482163 _yKingston Hospital NHS Foundation Trust OpenAthens account holders click here for access
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938			_aebrary _bEBRY _nebr10597120
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999			_c90613 _d90613