Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Michael, Edwin; Smith, Morgan; Katabarwa, Moses; Byamukama, Edson; Griswold, Emily; Habomugisha, Peace; Lakwo, Thomson; Tukahebwa, Edridah; Miri, Emmanuel; Eigege, Abel; Ngige, Evelyn; Unnasch, Thomas; Richards, Frank
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 13
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-06657-5
• Languages: English
• ISSNs: 2041-1723

Abstract

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. The decision when to stop an intervention is a critical component of parasite elimination programmes, but reliance on surveillance data alone can be inaccurate. Here, Michael et al. combine parasite transmission model predictions with disease survey data to more reliably determine when interventions can be stopped.