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The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015


Since the year 2000, a concerted campaign against malaria has led to unprecedented levels of intervention coverage across sub-Saharan Africa. Understanding the effect of this control effort is vital to inform future control planning. However, the effect of malaria interventions across the varied epidemiological settings of Africa remains poorly understood owing to the absence of reliable surveillance data and the simplistic approaches underlying current disease estimates. Here we link a large database of malaria field surveys with detailed reconstructions of changing intervention coverage to directly evaluate trends from 2000 to 2015, and quantify the attributable effect of malaria disease control efforts. We found that Plasmodium falciparum infection prevalence in endemic Africa halved and the incidence of clinical disease fell by 40% between 2000 and 2015. We estimate that interventions have averted 663 (542–753 credible interval) million clinical cases since 2000. Insecticide-treated nets, the most widespread intervention, were by far the largest contributor (68% of cases averted). Although still below target levels, current malaria interventions have substantially reduced malaria disease incidence across the continent. Increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance, should form a cornerstone of post-2015 control strategies.

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Figure 1: Changes in infection prevalence 2000–2015.
Figure 2: Changing endemicity and effect of interventions 2000–2015.

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The authors acknowledge assistance from M. Renshaw in providing information from the Roll Back Malaria Harmonization Working Group Programmatic Gap Analysis and other guidance in the interpretation of our results. We thank members of the Roll Back Malaria Monitoring and Evaluation Reference Group and the World Health Organization Surveillance Monitoring and Evaluation Technical expert Group for their feedback and suggestions. We thank C. Burgert of the DHS (Demographic and Health Surveys) Program for her assistance with DHS Survey access and interpretation. P.W.G. is a Career Development Fellow (no. K00669X) jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and receives support from the Bill and Melinda Gates Foundation (BMGF; nos OPP1068048, OPP1106023). These grants also support E.C., S.B., B.M., U.D., D.J.W., D.B. and A.H. The Swiss TPH component was supported through the project no. OPP1032350 funded by the BMGF. D.L.S. is funded by the BMGF (OPP1110495). S.I.H. is funded by a Senior Research Fellowship from the Wellcome Trust (no. 095066), which also supports K.E.B., and grants from the BMGF (nos. OPP1119467, OPP1106023 and OPP1093011). S.I.H. and D.L.S. also acknowledge funding support from the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health. J.T.G. is funded by an MRC Fellowship (no. G1002284). E.A.W. and P.A.E. are funded by the Global Good Fund.

Author information

Authors and Affiliations



Conceived of and designed the research: P.W.G. and S.B. Drafted the manuscript: P.W.G. and S.B. Drafted the Supplementary Information: S.B., D.J.W., E.C., D.B., U.D., B.M. Prepared data: S.B., D.J.W., B.M., U.D., K.B., C.L.M., A.H., A.B., J.Y., T.P.E. Conducted the analyses: S.B., D.J.W., E.C., D.B., C.A.F., M.L., R.E.C. Supported the analyses: P.A.E., E.A.W., O.B., M.A.P., T.A.S., J.T.G., C.A.F., M.L., F.L., D.L.S. Supported interpretation and policy contextualization: S.B., A.B., T.P.E., J.Y., C.A.F., M.L., J.M.C., C.L.J.M., D.L.S., S.I.H., R.E.C., P.W.G. All authors discussed the results and contributed to the revision of the final manuscript.

Corresponding author

Correspondence to P. W. Gething.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Schematic overview of main input data, model components, and outputs.

Each component is detailed in the Supplementary Information.

Extended Data Figure 2 Fitted function representing effect of ITNs.

Curves illustrate the predicted effect of ITNs as a function of coverage (five example coverage levels are shown, specified as mean coverage over preceding 4-year period) and baseline transmission. The baseline PfPR is shown on the horizontal axis and the suppressed PfPR given the ITN coverage level shown on the vertical axis. The diagonal line (representing zero ITN effect) is shown in black, and parameter uncertainty around each ITN effect line is illustrated by the semi-transparent envelopes. Results shown are derived from a Bayesian geostatistical model fitted to n = 27,573 PfPR survey points; n = 24,868 ITN survey points; n = 96 national survey reports of ACT coverage; n = 688 country-year reports on ITN, ACT and IRS distribution by national programs; and n = 20 environmental and socioeconomic covariate grids.

Extended Data Figure 3 Changing incidence rate by country, 2000–2015.

Estimated country-level rates of all-age clinical incidence are shown for 2000 and 2015. For Sudan and South Sudan, we used the post-2011 borders throughout the time period to allow comparability. Results shown are derived from a Bayesian geostatistical model fitted to n = 27,573 PfPR survey points; n = 24,868 ITN survey points; n = 96 national survey reports of ACT coverage; n = 688 country-year reports on ITN, ACT and IRS distribution by national programs; n = 20 environmental and socioeconomic covariate grids; and n = 30 active-case detection studies reporting P. falciparum clinical incidence.

Extended Data Figure 4 Decline in infection prevalence attributable to main malaria control interventions.

ad, Each map shows absolute decline in PfPR2–10 between 2000 and 2015 within areas of stable transmission attributable to the combined effect of ITNs, ACTs, and IRS (a); and the individual effect of ITNs (b); ACTs (c); and IRS (d). Note that the colour scaling differs between the panels. Results shown in all panels are derived from a Bayesian geostatistical model fitted to n = 27,573 PfPR survey points; n = 24,868 ITN survey points; n = 96 national survey reports of ACT coverage; n = 688 country-year reports on ITN, ACT and IRS distribution by national programs; and n = 20 environmental and socioeconomic covariate grids. Maps in this figure are available from the Malaria Atlas Project ( under the Creative Commons Attribution 3.0 Unported License.

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Supplementary Information

This file contains Supplementary Text and Data, Supplementary Figures 1-5, Supplementary Tables 1-4 and Supplementary References. (PDF 13325 kb)

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Bhatt, S., Weiss, D., Cameron, E. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526, 207–211 (2015).

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