Using mathematical models to optimise mosquito net distribution

Malaria remains a significant global health concern, responsible for over 600,000 deaths in 2024, with most fatalities occurring in sub-Saharan Africa (World Health Organization, 2025b). The disease is primarily transmitted by the bite of an infected Anopheles mosquito, with Plasmodium falciparum being the most common parasite causing most cases and deaths (source).

Insecticide-treated nets (ITNs) have been a highly effective intervention in combating malaria, averting 68% of cases between 2000 and 2015 (Bhatt et al., 2015). These nets are hung over beds and treated with insecticides to kill mosquitoes that come into contact with them (source).

Despite their effectiveness, ITN distribution faces several challenges. The nets have a limited lifespan due to physical damage and reduced insecticidal effectiveness, requiring regular replacement. Traditionally, mass distribution campaigns have been conducted every three years, allowing for periodic net replacement. However, individuals may cease using their ITNs before the three-year interval, potentially leaving gaps in protection that could be mitigated with more frequent campaigns (source).

Furthermore, owning an ITN does not guarantee its usage. While 68% of households in malaria-endemic Sub-Saharan African countries owned at least one ITN by 2024, only 47% of people reported sleeping under one (source). This discrepancy between ownership and usage underscores the need for more targeted distribution strategies.

National malaria programs are under increasing pressure to optimize ITN distribution within limited budgets, taking into account local conditions and transmission dynamics. In a study published in eLife, researchers from Imperial College London, REACH Malaria, Université Cheikh Anta Diop, and The Global Fund to Fight AIDS, Tuberculosis and Malaria developed a mathematical model using Demographic and Health Survey (DHS) data to assess ITN use and accessibility across six African countries: Burkina Faso, Ghana, Malawi, Mali, Mozambique, and Senegal (Glover et al., 2026).

The model analyzed data from 2005 to 2024, measuring ITN use, access, and retention across the study regions (Glover et al., 2026). By accounting for differences in survey timing relative to ITN distribution campaigns, the researchers aimed to avoid misleading trends in ITN usage (Glover et al., 2026).

The model's findings revealed that ITNs are typically kept for an average of 27 months but used for only 21 months, significantly less than the three-year interval between mass campaigns (Glover et al., 2026). Reducing the interval to two years would increase average ITN usage by 8.5 percentage points, from 45.4% to 53.9% (Glover et al., 2026).

However, the model also suggests that distributing dual-active-ingredient ITNs, containing both pyrethroids and chlorfenapyr, during triennial mass campaigns could avert more malaria cases compared to biennial campaigns using pyrethroid-only ITNs (Glover et al., 2026). The model emphasizes the importance of tailoring interventions to regional transmission patterns and behavior, as the most effective approach may vary depending on local conditions (Glover et al., 2026).

While the WHO previously recommended that at least 80% of at-risk populations should use ITNs, the study found that none of the surveyed regions achieved this average usage level (Roll Back Malaria Partnership, 2008; Smith et al., 2009). The authors argue that discontinuing mass campaigns and relying solely on continuous distribution channels could lead to a resurgence of malaria cases, even when distributing more effective dual-ingredient ITNs (Glover et al., 2026).

Moreover, regions with higher ITN usage among those with access are disproportionately affected by the cessation of mass campaigns, highlighting the critical role of targeted distribution strategies in optimizing malaria control efforts in resource-constrained settings (Glover et al., 2026). The study underscores the value of mathematical models in shaping informed decision-making for effective malaria prevention and control.