Mathematical Tools for Understanding Infectious Disease Dynamics / / Odo Diekmann, Tom Britton, Hans Heesterbeek.
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...
Saved in:
| Superior document: | Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013 |
|---|---|
| VerfasserIn: | |
| Place / Publishing House: | Princeton, NJ : : Princeton University Press, , [2012] ©2013 |
| Year of Publication: | 2012 |
| Edition: | Course Book |
| Language: | English |
| Series: | Princeton Series in Theoretical and Computational Biology ;
7 |
| Online Access: | |
| Physical Description: | 1 online resource (520 p.) :; 53 line illus. 1 table. |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 06779nam a22015375i 4500 | ||
|---|---|---|---|
| 001 | 9781400845620 | ||
| 003 | DE-B1597 | ||
| 005 | 20210830012106.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr || |||||||| | ||
| 008 | 210830t20122013nju fo d z eng d | ||
| 020 | |a 9781400845620 | ||
| 024 | 7 | |a 10.1515/9781400845620 |2 doi | |
| 035 | |a (DE-B1597)453861 | ||
| 035 | |a (OCoLC)979910930 | ||
| 040 | |a DE-B1597 |b eng |c DE-B1597 |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a nju |c US-NJ | ||
| 050 | 4 | |a RA652.2.M3 |b D54 2017 | |
| 072 | 7 | |a SCI008000 |2 bisacsh | |
| 082 | 0 | 4 | |a 614.4 |2 23 |
| 100 | 1 | |a Diekmann, Odo, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Mathematical Tools for Understanding Infectious Disease Dynamics / |c Odo Diekmann, Tom Britton, Hans Heesterbeek. |
| 250 | |a Course Book | ||
| 264 | 1 | |a Princeton, NJ : |b Princeton University Press, |c [2012] | |
| 264 | 4 | |c ©2013 | |
| 300 | |a 1 online resource (520 p.) : |b 53 line illus. 1 table. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 0 | |a Princeton Series in Theoretical and Computational Biology ; |v 7 | |
| 505 | 0 | 0 | |t Frontmatter -- |t Contents -- |t Preface -- |t Part I. The bare bones: Basic issues in the simplest context -- |t Part II. Structured populations -- |t Part III. Case studies on inference -- |t Part IV. Elaborations -- |t Bibliography -- |t Index |
| 506 | 0 | |a restricted access |u http://purl.org/coar/access_right/c_16ec |f online access with authorization |2 star | |
| 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 | ||
| 530 | |a Issued also in print. | ||
| 538 | |a Mode of access: Internet via World Wide Web. | ||
| 546 | |a In English. | ||
| 588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021) | |
| 650 | 0 | |a Communicable diseases |x Mathematical models. | |
| 650 | 0 | |a Epidemiology |x Mathematical models |v Congresses. | |
| 650 | 0 | |a Epidemiology |x Mathematical models. | |
| 650 | 7 | |a SCIENCE / Life Sciences / Biology. |2 bisacsh | |
| 653 | |a Bayesian statistical inference. | ||
| 653 | |a ICU model. | ||
| 653 | |a Markov chain Monte Carlo method. | ||
| 653 | |a Markov chain Monte Carlo methods. | ||
| 653 | |a ReedІrost epidemic. | ||
| 653 | |a age structure. | ||
| 653 | |a asymptotic speed. | ||
| 653 | |a bacterial infections. | ||
| 653 | |a biological interpretation. | ||
| 653 | |a closed population. | ||
| 653 | |a compartmental epidemic systems. | ||
| 653 | |a consistency conditions. | ||
| 653 | |a contact duration. | ||
| 653 | |a demography. | ||
| 653 | |a dependence. | ||
| 653 | |a disease control. | ||
| 653 | |a disease outbreaks. | ||
| 653 | |a disease prevention. | ||
| 653 | |a disease transmission. | ||
| 653 | |a endemic. | ||
| 653 | |a epidemic models. | ||
| 653 | |a epidemic outbreak. | ||
| 653 | |a epidemic. | ||
| 653 | |a epidemiological models. | ||
| 653 | |a epidemiological parameters. | ||
| 653 | |a epidemiology. | ||
| 653 | |a general epidemic. | ||
| 653 | |a growth rate. | ||
| 653 | |a homogeneous community. | ||
| 653 | |a hospital infections. | ||
| 653 | |a hospital patients. | ||
| 653 | |a host population growth. | ||
| 653 | |a host. | ||
| 653 | |a human social behavior. | ||
| 653 | |a i-states. | ||
| 653 | |a individual states. | ||
| 653 | |a infected host. | ||
| 653 | |a infection transmission. | ||
| 653 | |a infection. | ||
| 653 | |a infectious disease epidemiology. | ||
| 653 | |a infectious disease. | ||
| 653 | |a infectious diseases. | ||
| 653 | |a infectious output. | ||
| 653 | |a infective agent. | ||
| 653 | |a infectivity. | ||
| 653 | |a intensive care units. | ||
| 653 | |a intrinsic growth rate. | ||
| 653 | |a larvae. | ||
| 653 | |a macroparasites. | ||
| 653 | |a mathematical modeling. | ||
| 653 | |a mathematical reasoning. | ||
| 653 | |a maximum likelihood estimation. | ||
| 653 | |a microparasites. | ||
| 653 | |a model construction. | ||
| 653 | |a outbreak situations. | ||
| 653 | |a outbreak. | ||
| 653 | |a pair approximation. | ||
| 653 | |a parasite load. | ||
| 653 | |a parasite. | ||
| 653 | |a population models. | ||
| 653 | |a propagation speed. | ||
| 653 | |a reproduction number. | ||
| 653 | |a separable mixing. | ||
| 653 | |a sexual activity. | ||
| 653 | |a stochastic epidemic model. | ||
| 653 | |a structured population models. | ||
| 653 | |a susceptibility. | ||
| 653 | |a vaccination. | ||
| 700 | 1 | |a Britton, Tom, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Heesterbeek, Hans, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t Princeton University Press eBook-Package Backlist 2000-2013 |z 9783110442502 |
| 776 | 0 | |c print |z 9780691155395 | |
| 856 | 4 | 0 | |u https://doi.org/10.1515/9781400845620?locatt=mode:legacy |
| 856 | 4 | 0 | |u https://www.degruyter.com/isbn/9781400845620 |
| 856 | 4 | 2 | |3 Cover |u https://www.degruyter.com/cover/covers/9781400845620.jpg |
| 912 | |a 978-3-11-044250-2 Princeton University Press eBook-Package Backlist 2000-2013 |c 2000 |d 2013 | ||
| 912 | |a EBA_BACKALL | ||
| 912 | |a EBA_EBACKALL | ||
| 912 | |a EBA_EBKALL | ||
| 912 | |a EBA_EEBKALL | ||
| 912 | |a EBA_ESTMALL | ||
| 912 | |a EBA_PPALL | ||
| 912 | |a EBA_STMALL | ||
| 912 | |a GBV-deGruyter-alles | ||
| 912 | |a PDA12STME | ||
| 912 | |a PDA13ENGE | ||
| 912 | |a PDA18STMEE | ||
| 912 | |a PDA5EBK | ||