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200 10$aModeling to inform infectious disease control /$fNiels G. Becker, Australian National University, Canberra, Australia
205   $a1st ed.
210  1$aBoca Raton, Florida :$cCRC Press,$d[2015]
210  4$d©2015
215   $a1 online resource (217 p.)
225 1 $aChapman & Hall/CRC Biostatistics Series
225 0 $aChapman & Hall Book
300   $aA Chapman and Hall book--cover.
311 08$a1-4987-3106-6 
311 08$a1-4987-3107-4 
320   $aIncludes bibliographical references.
327   $aCover; Contents; Preface; CHAPTER 1: Introduction; CHAPTER 2: Minor outbreaks when infectives are homogeneous; CHAPTER 3: Minor outbreaks in a community of households; CHAPTER 4: Minor outbreaks when individuals differ; CHAPTER 5: Transmission intensity function; CHAPTER 6: Partially effective vaccines; CHAPTER 7: Social distancing; CHAPTER 8: Reducing epidemic size; CHAPTER 9: Dynamics of infection incidence; CHAPTER 10: Using data to inform model choice; Terminology and notation; References; Back Cover
330   $aEffectively Assess Intervention Options for Controlling Infectious DiseasesOur experiences with the human immunodeficiency virus (HIV), severe acute respiratory syndrome (SARS), and Ebola virus disease (EVD) remind us of the continuing need to be vigilant against the emergence of new infectious diseases. Mathematical modeling is increasingly used in the management of infectious disease control as a way to assess interventions relatively quickly, cheaply, and safely. Modeling to Inform Infectious Disease Control shows readers how to take advantage of these models when developing strategies to m
410  0$aChapman & Hall/CRC biostatistics series.
606   $aCommunicable diseases
606   $aInfection
606   $aMathematical models
615  0$aCommunicable diseases.
615  0$aInfection.
615  0$aMathematical models.
676   $a616.9
700   $aBecker$b Niels G.$f1942-$01870054
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