Freedom From Infection (FFI): A paradigm shift towards evidence-based decision-making for malaria elimination.

Abstract

Abstract Background: Assessing elimination of malaria locally requires a surveillance system with high sensitivity and specificity to detect its presence without ambiguity. Currently, the absence of locally acquired cases for three consecutive years is used as confirmation of elimination. However, relying on routine health data to prove the absence of infection presents challenges, as even one missed case can lead to incorrect inferences and potential resurgence. Overcoming this challenge requires innovative approaches to model the coupled processes of malaria transmission and its clinical observation. Methods: We propose a novel statistical framework based on a state-space model to probabilistically demonstrate the absence of malaria, using routinely collected health system data (which is extensive but inherently imperfect). By simultaneously modelling the transmission dynamics within the population and the probability of detection, our approach was designed to provide a robust estimate of the surveillance system's sensitivity and the corresponding probability of local elimination (PFree). Findings: Our study reveals a critical limitation of the traditional criterion for declaring malaria freedom, highlighting its inherent bias and potential for misinterpreting ongoing transmission. Importantly, our research demonstrates the high sensitivity of this approach to observation biases, where even a single missed infection can lead to erroneous conclusions. We show that the traditional criterion can fail to identify ongoing transmission, even in the absence of reported cases. Interpretation: Our approach represents a significant advancement in programmatic decision-making and malaria interventions. This methodological advancement has far-reaching implications, not only for malaria control but also for infectious disease control in general. By enhancing surveillance systems and optimizing resource allocation, our approach creates opportunities to address the limitations of traditional criteria for declaring disease freedom. Our findings emphasize the urgent need to reassess existing methods to accurately confirm malaria elimination, and the importance of incorporating comprehensive modelling techniques to improve the design and implementation of surveillance systems, ultimately leading to more effective strategies for infectious disease control. The scalability and feasibility of our integrative modelling approach further support its potential to revolutionize surveillance systems and enhance public health outcomes. Funding: Bill and Melinda Gates Foundation, Indonesia Endowment Fund for Education.