Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Abstract

BackgroundApproaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).MethodsA systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.ResultsOne hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.ConclusionsOur review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Estimating the local spatio‐temporal distribution of malaria from routine health information systems in areas of low health care access and reporting

“…These platforms are increasingly using mobile tools for registering cases and track patient-level data at different levels of care in order to move towards electronic surveillance of communicable diseases [ 59 ]. Yet, a recent review showed that studies seldom used routine data to characterize spatio-temporal risk of malaria at subnational scales due to limited quality and systematic biases, and none used routine health facility data at a finer scale than the facility’s catchment [ 60 ]. Integration of the methods presented here into electronic surveillance systems would allow the use of these granular data, requiring little additional information and straightforward geostatistical techniques.…”

Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict

“…In our analysis, we consider the following spatially referenced candidate covariates for modelling P. falciparum , all of which have been used in most previous studies on malaria risk mapping [ 20 – 22 ]. Population density , obtained from the WorldPop database ( ), is used to account for the higher levels of malaria transmission in low-populated rural areas.…”

“…In our analysis, we consider the following spatially referenced candidate covariates for modelling P. falciparum, all of which have been used in most previous studies on malaria risk mapping [20][21][22].…”

Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification

“…However, because they lack adequate power for effective sub-national stratification on their own [ 9 ], they have been used in combination with other more opportunistic, assembled research survey data, sub-national monitoring data and school surveys, increasing the power of national data to provide estimates of risk at fine spatial scale. These data are often analysed using model-based geostatistical (MBG) methods [ 10 ] to predict risk in areas without data augmented by environmental geospatial covariates of transmission [ 11 ]. MBG incorporates measures of uncertainty of disease predictions at the population level.…”