Journal article

A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data

Maile T Phillips, James E Meiring, Merryn Voysey, Joshua L Warren, Stephen Baker, Buddha Basnyat, John D Clemens, Christiane Dolecek, Sarah J Dunstan, Gordon Dougan, Melita A Gordon, Deus Thindwa, Robert S Heyderman, Kathryn E Holt, Firdausi Qadri, Andrew J Pollard, Virginia E Pitzer

STATISTICS IN MEDICINE | WILEY | Published : 2021

Abstract

Decisions about typhoid fever prevention and control are based on estimates of typhoid incidence and their uncertainty. Lack of specific clinical diagnostic criteria, poorly sensitive diagnostic tests, and scarcity of accurate and complete datasets contribute to difficulties in calculating age-specific population-level typhoid incidence. Using data from the Strategic Typhoid Alliance across Africa and Asia program, we integrated demographic censuses, healthcare utilization surveys, facility-based surveillance, and serological surveillance from Malawi, Nepal, and Bangladesh to account for under-detection of cases. We developed a Bayesian approach that adjusts the count of reported blood-cultu..

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University of Melbourne Researchers

Grants

Awarded by Bill and Melinda Gates Foundation


Awarded by Wellcome Trust


Awarded by UK National Institute for Health Research (NIHR) Research Professorship in Global Health


Funding Acknowledgements

Bill and Melinda Gates Foundation, Grant/Award Number: OPP1141321; Public Health Research Programme; Wellcome Trust, Grant/Award Numbers: 106158/Z/14/Z, 206545/Z/17/Z; UK National Institute for Health Research (NIHR) Research Professorship in Global Health, Grant/Award Number: NIHR300039