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Reconstructing the SARS-CoV-2 epidemic in eastern Uganda through longitudinal serosurveillance in a malaria cohort.

medRxiv : the preprint server for health sciences | 2022

Estimating the true burden of SARS-CoV-2 infection has been difficult in sub-Saharan Africa due to asymptomatic infections and inadequate testing capacity. Antibody responses from serologic surveys can provide an estimate of SARS-CoV-2 exposure at the population level.

Pubmed ID: 36172117 RIS Download

Research resources used in this publication

None found

Antibodies used in this publication

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Associated grants

  • Agency: NIAID NIH HHS, United States
    Id: K24 AI144048
  • Agency: NIAID NIH HHS, United States
    Id: U19 AI089674
  • Agency: NIAID NIH HHS, United States
    Id: U01 AI150741
  • Agency: NIAID NIH HHS, United States
    Id: K23 AI076614
  • Agency: NIAID NIH HHS, United States
    Id: K23 AI166009

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This is a list of tools and resources that we have found mentioned in this publication.


R Project for Statistical Computing (tool)

RRID:SCR_001905

Software environment and programming language for statistical computing and graphics. R is integrated suite of software facilities for data manipulation, calculation and graphical display. Can be extended via packages. Some packages are supplied with the R distribution and more are available through CRAN family.It compiles and runs on wide variety of UNIX platforms, Windows and MacOS.

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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Stan (tool)

RRID:SCR_018459

Probabilistic programming language for specifying statistical models. Defines log probability function over parameters conditioned on specified data and constants. Platform for statistical modeling and high performance statistical computation. Provides full Bayesian inference for posterior expectations including parameter estimation and posterior predictive inference by defining appropriate derived quantities of interest.

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