Login / Signup

A simulation-based method to inform serosurvey designs for estimating the force of infection using existing blood samples.

Anna ViccoClare P McCormackBelen PedriqueJohn H AmuasiAnthony Afum-Adjei AwuahChristian ObirikorangNicole S StruckEva LorenzJürgen MayIsabela RibeiroGathsaurie Neelika MalavigeChristl A DonnellyIlaria Dorigatti
Published in: PLoS computational biology (2023)
The extent to which dengue virus has been circulating globally and especially in Africa is largely unknown. Testing available blood samples from previous cross-sectional serological surveys offers a convenient strategy to investigate past dengue infections, as such serosurveys provide the ideal data to reconstruct the age-dependent immunity profile of the population and to estimate the average per-capita annual risk of infection: the force of infection (FOI), which is a fundamental measure of transmission intensity. In this study, we present a novel methodological approach to inform the size and age distribution of blood samples to test when samples are acquired from previous surveys. The method was used to inform SERODEN, a dengue seroprevalence survey which is currently being conducted in Ghana among other countries utilizing samples previously collected for a SARS-CoV-2 serosurvey. The method described in this paper can be employed to determine sample sizes and testing strategies for different diseases and transmission settings.
Keyphrases
  • dengue virus
  • cross sectional
  • zika virus
  • aedes aegypti
  • sars cov
  • single molecule
  • big data
  • respiratory syndrome coronavirus
  • deep learning
  • data analysis