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

Abstract Eliminating malaria locally requires a surveillance system with high sensitivity and specificity to detect its presence without ambiguity. Traditionally, the absence of locally acquired cases for three consecutive years is used to estimate the probability of elimination. However, proving the absence of infection using routine health data is challenging as even one missed infection can lead to incorrect inferences. This could result in premature termination of control efforts and resurgences. To address this, we propose an innovative method for probabilistically demonstrating the absence of malaria. Using spatio-temporally extensive but imperfect reports of malaria, we developed a novel statistical framework to model both the state process (malaria transmission in the population) and the observation process (cases detected by the health system). Our state-space model provides a robust estimate of the surveillance system's sensitivity and the corresponding probability of elimination (PFree). It can also quantify challenging parameters related to malaria transmission and surveillance sensitivity. Our study highlights the effectiveness of data-driven tools in decision-making for malaria and suggests a reassessment of the conventional method for confirming elimination.