title: Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria.
abstract: BACKGROUND
  The serial interval is a fundamentally important quantity in infectious disease epidemiology that has numerous applications to inferring patterns of transmission from case data. Many of these applications are apropos of efforts to eliminate falciparum malaria from locations throughout the world, yet the serial interval for this disease is poorly understood quantitatively.
  
  
  METHODS
  To obtain a quantitative estimate of the serial interval for falciparum malaria, the sum of the components of the falciparum malaria transmission cycle was taken based on a combination of mathematical models and empirical data. During this process, a number of factors were identified that account for substantial variability in the serial interval across different contexts.
  
  
  RESULTS
  Treatment with anti-malarial drugs roughly halves the serial interval due to an abbreviated period of human infectiousness, seasonality results in different serial intervals at different points in the transmission season, and variability in within-host dynamics results in many individuals whose serial intervals do not follow average behaviour. Furthermore, 24.5 % of secondary cases presenting clinically did so prior to the primary cases being identified through active detection of infection.
  
  
  CONCLUSIONS
  These results have important implications for epidemiological applications that rely on quantitative estimates of the serial interval of falciparum malaria and other diseases characterized by prolonged infections and complex ecological drivers.
authors.affiliation: Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
authors.forename: John H
authors.initials: JH
authors.lastname: Huber
authors.affiliation: Apple Inc., Cupertino, CA, USA.
authors.forename: Geoffrey L
authors.initials: GL
authors.lastname: Johnston
authors.affiliation: Department of Medicine, University of California, San Francisco, CA, USA.
authors.forename: Bryan
authors.initials: B
authors.lastname: Greenhouse
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
authors.forename: David L
authors.initials: DL
authors.lastname: Smith
authors.affiliation: Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA. taperkins@nd.edu.
authors.forename: T Alex
authors.identifier: http://orcid.org/0000-0002-7518-4014
authors.initials: TA
authors.lastname: Perkins
keywords: Epidemiology; Malaria elimination; Mathematical model; Statistical inference