Application of mathematical modelling to inform national malaria intervention planning in Nigeria

Abstract

Background For their 2021–2025 National Malaria Strategic Plan (NMSP), Nigeria’s National Malaria Elimination Programme (NMEP), in partnership with the World Health Organization (WHO), developed a targeted approach to intervention deployment at the local government area (LGA) level as part of the High Burden to High Impact response. Mathematical models of malaria transmission were used to predict the impact of proposed intervention strategies on malaria burden. Methods An agent-based model of Plasmodium falciparum transmission was used to simulate malaria morbidity and mortality in Nigeria’s 774 LGAs under four possible intervention strategies from 2020 to 2030. The scenarios represented the previously implemented plan (business-as-usual), the NMSP at an 80% or higher coverage level and two prioritized plans according to the resources available to Nigeria. LGAs were clustered into 22 epidemiological archetypes using monthly rainfall, temperature suitability index, vector abundance, pre-2010 parasite prevalence, and pre-2010 vector control coverage. Routine incidence data were used to parameterize seasonality in each archetype. Each LGA’s baseline malaria transmission intensity was calibrated to parasite prevalence in children under the age of five years measured in the 2010 Malaria Indicator Survey (MIS). Intervention coverage in the 2010–2019 period was obtained from the Demographic and Health Survey, MIS, the NMEP, and post-campaign surveys. Results Pursuing a business-as-usual strategy was projected to result in a 5% and 9% increase in malaria incidence in 2025 and 2030 compared with 2020, while deaths were projected to remain unchanged by 2030. The greatest intervention impact was associated with the NMSP scenario with 80% or greater coverage of standard interventions coupled with intermittent preventive treatment in infants and extension of seasonal malaria chemoprevention (SMC) to 404 LGAs, compared to 80 LGAs in 2019. The budget-prioritized scenario with SMC expansion to 310 LGAs, high bed net coverage with new formulations, and increase in effective case management rate at the same pace as historical levels was adopted as an adequate alternative for the resources available. Conclusions Dynamical models can be applied for relative assessment of the impact of intervention scenarios but improved subnational data collection systems are required to allow increased confidence in predictions at sub-national level.

Description of the design of a mixed-methods study to assess the burden and determinants of malaria transmission for tailoring of interventions (microstratification) in Ibadan and Kano metropolis

“…This calibration aims to capture transmission intensity, considering seasonal variations in vectoral larval habitats in both the Ibadan and Kano metropolis. This study will follow a comparable approach to the one outlined by Ozodiegwu and colleagues [ 12 ], while upholding the current general parameters. The model will integrate data on intervention effect sizes, coverages, and distribution schedules.…”

“…The Nigerian Malaria Elimination Programme (NMEP) heeded the call for a more targeted response to malaria control and, in the 2021–2025 National Malaria Strategic Plan, interventions were tailored for each Local Government Area (LGA) [ 11 ]. Mathematical models supported the selection of optimal intervention mixes for each LGA [ 11 , 12 ]. Similarly, cities need intervention tailoring, particularly targeting informal settlements, slums and neighbourhoods situated close to farms, areas where residents may be at higher malaria risk as compared to planned settlements with high-quality housing and improved environmental conditions.…”

Understanding the effective reproduction number ofPlasmodium falciparummalaria with seasonal variation at sub-national level in Nigeria

“…Several mathematical models have been deployed to model the spread of malaria across the globe [19, 20, 21, 22, 23, 24, 25] among others. For example, [19] formulated a malaria transmission model with variables based on the climate.…”

“…In order to simulate malaria morbidity and mortality across Nigeria’s 774 LGAs under four potential in-tervention strategies from 2020 to 2030, an agent-based model of Plasmodium falciparum transmission was utilized by [20]. The scenarios included the currently implemented plan (business as usual), the NMSP with a coverage level of 80% or above, and two plans that were prioritized based on the resources available to Nigeria.…”