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Additional file 1. In Depth Interview Guide. This file contains the questions and prompts used by interviewers during data collection. The guide was developed based on a literature review and pilot testing.

Additional file 1: Figure S1. Assignment of 774 LGAs in Nigeria into 22 epidemiological archetypes. Figure S2. Simulated seasonality of clinical malaria by archetype compared with the Rapid Impact Assessment health facility data for years 2014 – 2018. Thin red lines show 50 stochastic realizations and solid red dots and line show the mean over the realizations. Figure S3. a: Case management among children under the age, insecticide treated nets use and PfPR among children under the age of five years in 2010 by LGA. b: Two plots are shown for each archetype with archetype names at the top of each plot. The left plot is the larval habitat multiplierand likelihood evaluation against archetype U5 PfPR 2010 MIS. The red dot is maximum likelihood estimate of LHM. The right plot is the simulated U5 PfPR within each archetype compared with monthly U5 PfPR from the 2010 MIS. The thick red line indicates the best match while thin red lines show PfPR under other larval habitat scale factors. Each line is the mean of 10 stochastic realizations. 12 out of the 22 archetypes are shown here, remainder are shown in Fig S3b. Figure S4. ITN coverage among pregnant women attending ANC in 2018. Figure S5. Estimated ITN kill rate for a 12% reduction in annual malaria incidence among children under the age of five years. Figure S6. The relationship between permethrin bioassay mortality and ITN killing rate in the Churcher et al. model and EMOD. Scale factors was calculated by dividing the EMOD kill rateby the kill rate from the Churcher et al. model. Figure S7. Fitted splines showing estimated IPTp coverage through time for a random subset of LGAs. Points show DHS/MIS data and lines show the fitted splines, with each color indicating a different LGA. Figure S8. Fraction of IPTp-receiving individuals who reported receiving one, two, or three or more doses in each DHSor MIS. Figure S9. LGAs designated as eligible to receive IPTi. Figure S10. Mean DTP1-3 vaccine coverage per LGA in the 2018 DHS survey. Figure S11. Archetype level scatterplot highlighting beta-regression model equations used to compute predicted average yearly change CM coverages per archetype. Annual case management is the proportion of children with fever in the 2 weeks period prior to the survey that received an ACT. Figure S12. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2014. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S13. Comparison of DHIS2 and simulation seasonality trends in 2014 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series. Figure S14. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2015. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S15. Comparison of DHIS2 and simulation seasonality trends in 2015 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series. Figure S16. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2016. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S17. Comparison of DHIS2 and simulation seasonality trends in 2016 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series. Figure S18. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2017. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S19. Comparison of DHIS2 and simulation seasonality trends in 2017 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series. Figure S20. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2018. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S21. Comparison of DHIS2 and simulation seasonality trends in 2018 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series.

Additional file 1: ANC1 Surveillance Questions.

Additional file 1: Methodological supplement and additional results.

Additional file 1. Table S1. Summary of insecticide-treated bed net products.

Additional file 2. Annex S1. Country-specific context.

Malaria burden is heavily concentrated in sub-Saharan Africa (SSA) where cases and deaths associated with COVID-19 are rising. In response, countries are implementing societal measures aimed at curtailing transmission. Despite this, the COVID-19 epidemic could still result in millions of deaths as local health facilities become overwhelmed. Advances in malaria control this century have been largely due to distribution of long-lasting insecticidal nets (LLINs), with many SSA countries planning campaigns for 2020. Here we use COVID-19 and malaria transmission models to estimate the impact of disruption of malaria prevention activities and other core health services under different COVID-19 epidemic scenarios. If activities are halted, the malaria burden in 2020 could be more than double that of 2019. Mitigating this negative impact is possible, and LLIN distributions in particular should be prioritised alongside access to antimalarial treatment to prevent substantial malaria epidemics.

Additional file 1.

Nigeria National Malaria Operational Research Agenda Online Survey questionnaire. (DOC 144 kb)

Nigeria National Malaria Operational Research Agenda Key Informant Interview Guide. (PDF 44 kb)

List of Reviewed Documents from the National Malaria Elimination Programme (available at www.nmcp.gov.ng/archives , accessed 2016 October 7). (DOCX 29 kb)

Nigeria National Malaria Operational Research Agenda Key Informant Interview Guide. (PDF 44 kb)

List of Reviewed Documents from the National Malaria Elimination Programme (available at www.nmcp.gov.ng/archives , accessed 2016 October 7). (DOCX 29 kb)