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Malaria in the Era of COVID-19 | EK47


Application of mathematical models of malaria transmission to inform national strategic planning in Nigeria


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Application of mathematical models of malaria transmission to inform national strategic planning in Nigeria Ifeoma D. Ozodiegwu1, Monique Ambrose2, Beatriz Galatas3, Aadrita Nandi1, Manuela Runge1, Kamaldeen Okuneye1, Neena Parveen Dhanoa4, Ibrahim Maikore5, Perpetua Uhomoibhi5, Caitlin Bever2, Abdisalan Noor3, Jaline Gerardin1

1Department of Preventive Medicine and Institute for Global Health, Northwestern University, Chicago IL USA
2Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle WA USA
3Global Malaria Programme, World Health Organization,
4Weinberg College of Arts and Sciences, Northwestern University, Evanston, IL,
5National Malaria Elimination Programme, Nigeria

Nigeria is one of the 10 highest burden malaria countries in Africa, accounting for roughly a quarter of all global malaria cases and deaths. For the 2020 – 2025 National Strategic Plan, the Nigerian Malaria Elimination Program (NMEP) developed a targeted approach to intervention deployment that considered spatial heterogeneity in malaria transmission at the local government level. We created an analytical framework for predicting the impact of the NMEP’s proposed intervention strategy on malaria morbidity and mortality in each of Nigeria’s 774 local government areas (LGAs). Our framework was built on EMOD, an agent-based model of Plasmodium falciparum transmission, and incorporates survey data, routine data, and programmatic data from Nigeria. Capturing the seasonality of malaria incidence and intrinsic potential of each LGA to support transmission was not possible due to data limitations at the LGA level. We therefore clustered LGAs 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 was used to determine seasonality in each archetype. We set each LGA’s baseline malaria transmission intensity by calibrating to parasite prevalence in children under the age of five years measured in the 2010 Demographic and Health Surveys (DHS), using archetype parasite prevalence to supplement LGA-level data. Intervention coverage in the 2010-2020 period was also obtained from DHS. Effect sizes of case management, vector control, and chemoprevention were previously calibrated to field data or obtained from the literature. The resultant model was used to make attributions for historical interventions and to make subnational predictions of the impact of intervention mixes proposed by the NMEP for funding by the Global Fund. Our modeling approach illustrates how dynamic models can be applied to support strategic planning for malaria and can be adopted by other high-burden countries.

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