Freedom from Infection: Confirming Interruption of Malaria Transmission. Issue 5 (May 2017)

• Record Type: Journal Article
• Authors: Stresman, Gillian; Cameron, Angus; Drakeley, Chris
• Is Part Of: Trends in parasitology. Volume 33:Issue 5(2017:May)
• Page Start: 345
• Page End: 352
• Publication Date: 2017-05
• Subjects: elimination -- active surveillance -- passive surveillance -- negative reporting; Parasitology -- Periodicals; Parasitology -- Periodicals; Biology -- Periodicals; Parasitology; Biology; Parasitologie -- Périodiques; Online resources; 571.999
• DOI: 10.1016/j.pt.2016.12.005
• Languages: English
• ISSNs: 1471-4922

Abstract

Abstract : The global reductions in disease burden and the continued spread of drug and insecticide resistance make malaria elimination both viable and imperative, although this may be more easily achieved in some settings compared to others. Whilst the focus has been on optimal approaches to achieve elimination, less attention has been paid to how to measure the absence of malaria. Measuring the absence of transmission poses a specific challenge in that it involves proving a negative. The concept of freedom from infection, routinely used in veterinary epidemiology, can provide quantitative and reproducible estimates that, if infections were present above a predefined (low) threshold, they would be detected with a known uncertainty. Additionally, these methods are adaptable for both passively and actively collected data as well as combining information when multiple surveillance streams are available. Here we discuss the potential application of this approach to malaria. Trends: Evidence-based approaches for informing public health decision-making in the context of disease elimination are currently lacking. Tools developed in veterinary epidemiology can generate quantitative and reproducible estimates for the probability of detecting disease were it present at a pre-specified (low) level. Passive case detection can be augmented with actively collected data to generate an overall estimate of the sensitivity of the surveillance system and corresponding estimates of freedom from infection. Historical data can be incorporated into estimates of freedom with appropriate weighting according to the probability that infection is introduced into the population. For malaria control programs that are reorienting surveillance for elimination certification, freedom from infection estimates provide a potential standardized approach for informing decision-making.