Back

Insecticide-treated bednet coverage in Africa 2006-2024: a spatiotemporal analysis of net ownership, use, age and type.

Tan, E.; van den Berg, M.; Saddler, A.; Vargas, C.; Weiss, D.; Bertozzi-Villa, A.; Bhatt, S.; Symons, T. L.; Gething, P. W.

2025-11-27 public and global health
10.1101/2025.11.25.25341025 medRxiv
Show abstract

Since their rapid adoption from the mid 2000s, insecticide-treated nets (ITNs) have been one of the most ubiquitous and impactful malaria interventions in Africa. Facing insecticide resistance and ever-increasing resource constraints, there is a growing need to revise previous modelling efforts of ITN coverage to better reflect the current epidemiological reality. Comprehensive estimates of ITN coverage must account for factors such as net type, net age, and user behaviours. By leveraging improved data availability, we propose a mathematically rigorous and interpretable ITN coverage model that extends previous national-level ITN models to the subnational level across 44 countries in Africa. The presented Multitype-ITN (MITN) model is used to construct longitudinal estimates of net demography by age and type from 2006 to 2024, and provide insights on potential sources of ITN distribution inefficiencies. Our findings show a significant increase in the uptake of next-generation ITNs, and thus reflect an increasingly diverse market. However, longitudinal coverage suggests stagnation in key metrics of net ownership such as nets-per-capita, access and use, alongside a downwards trend in utilisation - number of users per net. Coupled with observed stationary levels of use rate - proportion of people with access that also use nets. There are also early signs of interventions failing to meet the demands of increasing populations. Our results provide more nuanced metrics of ITN coverage that account for factors such as type and age, which may inform the design of future net allocation policies.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.