Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria

John H. Huber, Geoffrey L. Johnston, Bryan Greenhouse, David L. Smith, T. Alex Perkins

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalMalaria Journal
Volume15
Issue number1
DOIs
StatePublished - Sep 22 2016
Externally publishedYes

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Keywords

  • Epidemiology
  • Malaria elimination
  • Mathematical model
  • Statistical inference

ASJC Scopus subject areas

  • Parasitology
  • Infectious Diseases

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