Human movement data for malaria control and elimination strategic planning

Abstract

Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.

Figure 1

Examples of human population movement (HPM) types relevant for malaria control and elimination; Human population movement (HPM) is stratified by spatial (distance travelled) and temporal (frequency of travel) characteristics. The blue and red triangles represent between country HPM (non-contiguous international and contiguous international) whilst the purple and green triangles represent within country HPM (between rural areas and between rural and urban areas). The size of the triangle illustrates the importance of the HPM category for estimating malaria importation, based on infection importation risk of the individual traveller and aggregate flow of travellers in each HPM category. Table 1 and 2 provide details of data types that may be used to quantify the different HPM categories.

Figure 2

Census data records showing place of birth of population enumerated in the Tanzania 2002 census; Resolution of current location of individuals was recorded at a ward level, whilst place of birth was recorded at a country level, representing origin of current Tanzanian residents. Wards are colour-coded according to place of birth of majority of non-Tanzanian enumerated individuals.

Figure 3

Comparing HPM in different demographics from Kenya 1999 census microdata; HPM between two district locations recorded if previous residence differs from current residence. Circles at the centre of the district represent locations and circle size is proportional to the population size of the district. Flows between locations are represented by a line between two circles and line width is proportional to the number of people that move between two locations. a) HPM flows in the male population between the ages of 15 and 24 years, b) HPM flows in children under the age of 5 years. Origin–destination pairs with less than 10 HPM flows were omitted from illustration.

Figure 4

A road network map of Kenya with road traffic data shown for sub-sections of the roads; Green: all roads with no traffic data, darker colour represents areas with large traffic volumes. Table 2 provides details of data collection and compilation.

Figure 5

Steps to estimate the impact of contiguous human population movement (HPM) on malaria importation; Steps to estimate the impact of contiguous human population movement (HPM) between country A and country B (country A with relatively higher transmission than country B), onPlasmodium falciparum(Pf) malaria importation in country B, using Geographical Information System (GIS) tools and mathematical modelling techniques.