Handbook of infectious disease data analysis / edited by Leonhard Held, Niel Hens, Philip D. O'Neill, Jacco Wallinga.

<P><STRONG>I Introduction</STRONG></P><P><STRONG>1. Introduction</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga</EM></P><P><BR><STRONG>II Basic Concepts </STRONG></P><P><STRONG>1. Population dynamics of pathogens</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Ottar Bjornstad</EM></P><P><STRONG>2. Infectious disease data from surveillance, outbreak investigation and epidemiological studies</STRONG> <BR><EM>&nbsp;&nbsp;&nbsp; Susan Hahné, Richard Pebody</EM></P><P><STRONG>3. Key concepts in infectious disease epidemiology<BR></STRONG><EM>&nbsp;&nbsp;&nbsp; Nick Jewell</EM></P><P><STRONG>4. Key parameters in infectious disease epidemiology<BR></STRONG><EM>&nbsp;&nbsp;&nbsp; Laura White</EM></P><P><STRONG>5. Contact patterns for contagious diseases</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Jacco Wallinga, Jan van de Kassteele, Niel Hens</EM></P><P><STRONG>6. Basic stochastic transmission models and their inference</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Tom Britton</EM></P><P><STRONG>7. Analysis of vaccine studies and causal inference<BR></STRONG><EM>&nbsp;&nbsp;&nbsp; Betz Halloran</EM></P><P><BR><STRONG>III Analysis of Outbreak Data </STRONG></P><P><STRONG>1. Markov chain Monte Carlo methods for outbreak data</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Philip O’Neill, Theodore Kypraios</EM></P><P><STRONG>2. Approximate Bayesian Computation methods for epidemic models</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Peter Neal</EM></P><P><STRONG>3. Iterated filtering methods for Markov process epidemic models</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Theresa Stocks</EM></P><P><STRONG>4. Pairwise survival analysis of infectious disease transmission data</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Eben Kenah</EM></P><P><STRONG>5. Methods for outbreaks using genomic data</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Don Klinkenberg, Caroline Colijn, Xavier Didelot</EM></P><P><BR><STRONG>IV Analysis of Seroprevalence Data</STRONG></P><P><STRONG>1. Persistence of passive immunity, natural immunity (and vaccination)</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Amy Winter, Jess Metcalf</EM> </P><P><STRONG>2. Inferring the time of infection from serological data</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Maciej Boni, Kåre Mølbak, Karen Angeliki Krogfelt</EM> </P><P><STRONG>3. The use of seroprevalence data to estimate cumulative incidence of infection</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Ben Cowling, Jessica Wong</EM></P><P><STRONG>4. The analysis of serological data with transmission models<BR></STRONG><EM>&nbsp;&nbsp;&nbsp; Marc Baguelin</EM> </P><P><STRONG>5. The analysis of multivariate serological data<BR></STRONG><EM>&nbsp;&nbsp;&nbsp; Steven Abrams</EM> </P><P><STRONG>6. Mixture modelling</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Emanuele Del Fava, Ziv Shkedy</EM></P><P><BR><STRONG>V Analysis of Surveillance Data </STRONG></P><P><STRONG>1. Modelling infectious diseases distributions: applications of point process methods</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Peter J Diggle</EM> </P><P><STRONG>2. Prospective detection of outbreaks</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Benjamin Allevius, Michael Höhle</EM></P><P><STRONG>3. Underreporting and reporting delays</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Angela Noufaily</EM></P><P><STRONG>4. Spatio-temporal analysis of surveillance data</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Jon Wakefield, Tracy Q Dong, Vladimir N Minin</EM></P><P><STRONG>5. Analysing multiple epidemic data sources</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Daniela De Angelis, Anne Presanis</EM></P><P><STRONG>6. Forecasting based on surveillance data</STRONG><BR><EM>&nbsp;&nbsp;&nbsp; Leonhard Held, Sebastian Meyer</EM></P><P></P><P>&nbsp;</P>

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