Mapping Malaria Risk in Low Transmission Settings: Challenges and Opportunities

Abstract

As malaria transmission declines, it becomes increasingly focal and prone to outbreaks. Understanding and predicting patterns of transmission risk becomes an important component of an effective elimination campaign, allowing limited resources for control and elimination to be targeted cost-effectively. Malaria risk mapping in low transmission settings is associated with some unique challenges. This article reviews the main challenges and opportunities related to risk mapping in low transmission areas including recent advancements in risk mapping low transmission malaria, relevant metrics, and statistical approaches and risk mapping in post-elimination settings.

Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting

“…However, as countries approach zero cases, increasing focality in transmission and the impact of imported cases pose challenges to both reaching elimination 6 and measuring progress towards that goal. Increased spatial and temporal heterogeneity in malaria cases [7][8][9] in low transmission settings reduces the usefulness of national or regional level trends in incidence or prevalence, which can mask small areas of high transmission intensity. Furthermore, end-game surveillance and control measures are increasingly expensive per case.…”

“…Meeting these challenges requires measuring changes in transmission, often at fine spatial scales. However, existing methods used to quantify malaria transmission are poorly suited to elimination settings 9 . Parasite prevalence rates (PR) are not accurate below a PR of 1-5% 22,23 due to the large sample sizes necessary for precise estimates at low prevalence.…”

Model ensembles with different response variables for base and meta models: malaria disaggregation regression combining prevalence and incidence data

“…High-resolution maps of malaria risk are vital for elimination but mapping malaria in low burden countries presents new challenges as traditional mapping of prevalence from cluster-level surveys (Gething et al, 2011;Bhatt et al, 2017;Gething et al, 2012;Bhatt et al, 2015) is often not effective because, firstly, so few individuals are infected that most surveys will detect zero cases, and secondly, because of the lack of nationally representative prevalence surveys in low burden countries (Sturrock et al, 2016(Sturrock et al, , 2014. Routine surveillance data of malaria case counts, often aggregated over administrative regions defined by geographic polygons, is becoming more reliable and more widely available (Sturrock et al, 2016) and recent work has focussed on methods for estimating high-resolution malaria risk from these data (Sturrock et al, 2014;Wilson and Wakefield, 2017;Law et al, 2018;Taylor et al, 2017;Li et al, 2012). However, the aggregation of cases over space means that the data may be relatively uninformative, especially if the case counts are aggregated over large or heterogeneous areas, because it is unclear where within the polygon, and in which environments, the cases occurred.…”

The geography of malaria elimination in Bangladesh: combining data layers to estimate the spatial spread of parasites

“…A global decline of malaria in recent decades has led to a push for complete national elimination of human malaria parasites in 21 countries by 2020 (1). In elimination and pre-elimination settings, malaria transmission is often highly heterogeneous geographically (2)(3)(4), and policymakers must focus on reducing transmission in remaining endemic foci, while protecting areas where malaria has been effectively controlled from imported infections that threaten to reintroduce parasites and reignite transmission (5,6). These tasks require different interventions, so understanding how patterns of regular travel to and from malaria endemic regions of a country contribute to the spread of malaria is a critical component of elimination planning.…”