Mapping multiple components of malaria risk for improved targeting of elimination interventions

Abstract

There is a long history of considering the constituent components of malaria risk and the malaria transmission cycle via the use of mathematical models, yet strategic planning in endemic countries tends not to take full advantage of available disease intelligence to tailor interventions. National malaria programmes typically make operational decisions about where to implement vector control and surveillance activities based upon simple categorizations of annual parasite incidence. With technological advances, an enormous opportunity exists to better target specific malaria interventions to the places where they will have greatest impact by mapping and evaluating metrics related to a variety of risk components, each of which describes a different facet of the transmission cycle. Here, these components and their implications for operational decision-making are reviewed. For each component, related mappable malaria metrics are also described which may be measured and evaluated by malaria programmes seeking to better understand the determinants of malaria risk. Implementing tailored programmes based on knowledge of the heterogeneous distribution of the drivers of malaria transmission rather than only consideration of traditional metrics such as case incidence has the potential to result in substantial improvements in decision-making. As programmes improve their ability to prioritize their available tools to the places where evidence suggests they will be most effective, elimination aspirations may become increasingly feasible.

Simulating the council-specific impact of anti-malaria interventions: A tool to support malaria strategic planning in Tanzania

“…Administrative councils represent the operational units of the Tanzanian NMCP, and the geographical heterogeneities mean that the most appropriate intervention strategies are different by council, so a stratification and intervention allocation at this level is essential. In contrast, sub-council spatial heterogeneity was not considered, even though there is important smaller-area variation in intervention usage, components of vectorial capacity [82,83] and malaria endemicity [84,85]. While the EIR can be estimated based on only a few key parameters [86,87], the available mosquito bionomic data are inadequate for detailed parameterisations at council level or lower, and the workflow did not make use of the uncertainty or sub-council variation in the predictions from the geostatistical models.…”

Risk Factors for Malaria Infection in Central Madagascar: Insights from a Cross-Sectional Population Survey

“…7 These two complimentary malariometrics allow a more comprehensive description of the overall status of malaria than a single one in isolation. 8 Here, we present a study investigating the parasite reservoir and infection risk factors in an area of central Madagascar known to be co-endemic for both Plasmodium falciparum and Plasmodium vivax clinical malaria. 9,10 Four human Plasmodium parasite species have been detected in Madagascar, with P. falciparum being predominant.…”

Spatio-temporal dynamics of Plasmodium falciparum transmission within a spatial unit on the Colombian Pacific Coast

“…However, in practical terms, there is often little knowledge of the different parameters that define malaria transmission systems on the part of control programmes, making the control effort ineffectual or not optimal from a cost-effectiveness point of view. An example of this is the lack of knowledge of many intrinsic (parasite, mosquito, human) and extrinsic (control and prevention, social, demographic, behavioral, economic, political, environmental) or biotic and abiotic factors defining the malaria burden 11,12 .…”

“…The stability of these hotspots 15 and the relationship between transmission intensity and malaria hotspots are areas of ongoing study 16 . Using different malaria transmission variables at the micro-epidemiological level, maps and models have been produced to guide National Control Programmes in Africa and Southeast Asia 12,[17][18][19][20][21][22] . By contrast in the Americas, where the dynamics of malaria transmission is different from Africa and Southeast Asia due to differences in vector bionomics, social conditions or parasite genetic structure among others, relatively few studies have attempted to identify transmission heterogeneity, whether using epidemiological data 23 , serological tools 24,25 or assessing the effect of ecological differences on malaria transmission 26 .…”