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Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys

Author

Listed:
  • Edwin Michael

    (University of Notre Dame)

  • Morgan E. Smith

    (University of Notre Dame)

  • Moses N. Katabarwa

    (One Copenhill)

  • Edson Byamukama

    (The Carter Center, Uganda)

  • Emily Griswold

    (One Copenhill)

  • Peace Habomugisha

    (The Carter Center, Uganda)

  • Thomson Lakwo

    (Ministry of Health)

  • Edridah Tukahebwa

    (Ministry of Health)

  • Emmanuel S. Miri

    (The Carter Center, Nigeria)

  • Abel Eigege

    (The Carter Center, Nigeria)

  • Evelyn Ngige

    (Federal Sceretariat)

  • Thomas R. Unnasch

    (University of South Florida)

  • Frank O. Richards

    (One Copenhill)

Abstract

Stopping interventions is a critical decision for parasite elimination programmes. Quantifying the probability that elimination has occurred due to interventions can be facilitated by combining infection status information from parasitological surveys with extinction thresholds predicted by parasite transmission models. Here we demonstrate how the integrated use of these two pieces of information derived from infection monitoring data can be used to develop an analytic framework for guiding the making of defensible decisions to stop interventions. We present a computational tool to perform these probability calculations and demonstrate its practical utility for supporting intervention cessation decisions by applying the framework to infection data from programmes aiming to eliminate onchocerciasis and lymphatic filariasis in Uganda and Nigeria, respectively. We highlight a possible method for validating the results in the field, and discuss further refinements and extensions required to deploy this predictive tool for guiding decision making by programme managers.

Suggested Citation

  • Edwin Michael & Morgan E. Smith & Moses N. Katabarwa & Edson Byamukama & Emily Griswold & Peace Habomugisha & Thomson Lakwo & Edridah Tukahebwa & Emmanuel S. Miri & Abel Eigege & Evelyn Ngige & Thomas, 2018. "Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06657-5
    DOI: 10.1038/s41467-018-06657-5
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    Cited by:

    1. Morgan E Smith & Emily Griswold & Brajendra K Singh & Emmanuel Miri & Abel Eigege & Solomon Adelamo & John Umaru & Kenrick Nwodu & Yohanna Sambo & Jonathan Kadimbo & Jacob Danyobi & Frank O Richards &, 2020. "Predicting lymphatic filariasis elimination in data-limited settings: A reconstructive computational framework for combining data generation and model discovery," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-22, July.

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