Abstract
BackgroundThe most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces.MethodsWe undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression.ResultsSixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = − 0.989; 95% CI − 0.998, − 0.939; p < 0.001), i.e. steeper declines were associated with a stronger correlation between empirical observations and modelled predictions. Factors such as quality of study, reported measure of malaria and endemicity were only slightly predictive of such correlations.ConclusionsIn many locations, both local surveillance data and modelled estimates showed declines in malaria burden and hence similar trends. However, there was a weak association between individual surveillance datasets and the modelled predictions where stalling in progress or resurgence of malaria burden was empirically observed. Surveillance data were patchy, indicating a need for improved surveillance to strengthen both empiric reporting and modelled predictions.
Highlights
The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models
Our current understanding of the declining burden of malaria across much of sub-Saharan Africa (SSA) has relied on modelled estimations based on historical epidemiological data, predictions in time and space based on sparse parasite prevalence data, and presumed impacts of interventions [1, 7, 8]
These surfaces are used in conjunction with limited historical epidemiological data on disease incidence, malaria-specific mortality and case-fatality rates [1, 8] to predict both malaria clinical incidence and mortality at a 5 × 5 km grid surface across SSA based on an estimated function of Parasite rate (PR) [1, 8]
Summary
The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. In current models of malaria disease burden in African, empirical data on 27,573 spatially and temporally unique PR observations are first modelled in time and space using a range of environmental, population and intervention covariates to provide approximately nine hundred thousand 5 × 5 km gridded surfaces of estimated malaria prevalence for every year between 2000 and 2015 [1] These surfaces are used in conjunction with limited historical epidemiological data on disease incidence, malaria-specific mortality and case-fatality rates [1, 8] to predict both malaria clinical incidence and mortality at a 5 × 5 km grid surface across SSA based on an estimated function of PR [1, 8]. In 2019, the list was extended to include Djibouti [3]
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