Optimising malaria surveillance in Papua New Guinea by understanding spatial variability of key indicators

As malaria endemic countries move towards the goal of malaria elimination, malaria risk and transmission become unevenly distributed, resulting in areas with higher malaria burden, called hotspots. Transmission can remain persistent in these hotspots despite application of standard control measures. This thesis aims to determine how surveillance data from Papua New Guinea can be further enhanced by constructing risk maps to identify village-level hotspots of malaria transmission and to understand the local risk factors driving transmission in these areas. Such approaches may provide useful information to aid decision-making and support malaria control programs to prioritise resources or locally tailored interventions to highest burden areas.