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Spatial and spatio-temporal methods for mapping malaria risk: a systematic review
  1. Julius Nyerere Odhiambo1,
  2. Chester Kalinda1,2,
  3. Peter M Macharia3,
  4. Robert W Snow3,4,
  5. Benn Sartorius1,5
  1. 1 Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa
  2. 2 Faculty of Agriculture and Natural Resources, University of Namibia, Windhoek, Namibia
  3. 3 Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
  4. 4 Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
  5. 5 Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
  1. Correspondence to Julius Nyerere Odhiambo; nyererejulius7{at}


Background Approaches 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).

Methods A 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.

Results One 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.

Conclusions Our 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.

  • systematic review
  • malaria
  • review
  • geographic information systems
  • control strategies

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  • Handling editor Alberto L Garcia-Basteiro

  • Twitter @Pete_M_M

  • Contributors JNO, BS and RWS conceived and designed the systematic review. JNO, BS and CK conducted the literature search, study selection and data extraction. JNO wrote the first draft of the manuscript with assistance from CK and BS. PMM and RWS revised the draft critically for important intellectual content. All authors read and approved the final version of the manuscript.

  • Funding JNO acknowledges support from the University of KwaZulu Natal, College of Health Sciences postgraduate scholarship scheme. RWS is supported as a Wellcome Trust Principal Fellow (#103602 and 212176) that also supported PMM. PMM acknowledges support for his PhD under the IDeALs Project part of the DELTAS Africa Initiative (DEL-15-003). The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)'s Alliance for Accelerating Excellence in Science in Africa and supported by the New Partnership for Africa's Development Planning and Coordinating Agency with funding from the Wellcome Trust (107769) and the UK government. RWS and PMM are grateful to the support of the Wellcome Trust to the Kenya Major Overseas Programme (203077).

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement All data relevant to the study are included in the article or uploaded as online supplemental information. The data supporting conclusions made in this review are available in the detailed reference list.

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