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Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning.

Tatem AJ, Huang Z, Narib C, Kumar U, Kandula D, Pindolia DK, Smith DL, Cohen JM, Graupe B, Uusiku P, Lourenço C - Malar. J. (2014)

Bottom Line: Communities that were strongly connected by relatively higher levels of movement were then identified, and net export and import of travellers and infection risks by region were quantified.These maps can aid the design of targeted interventions to maximally reduce the number of cases exported to other regions while employing appropriate interventions to manage risk in places that import them.The approaches presented can be rapidly updated and used to identify where active surveillance for both local and imported cases should be increased, which regions would benefit from coordinating efforts, and how spatially progressive elimination plans can be designed.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Geography and Environment, University of Southampton, Southampton, UK. A.J.Tatem@soton.ac.uk.

ABSTRACT

Background: As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.

Methods/results: Here, using the example of Namibia, a method for targeting of interventions using surveillance data, satellite imagery, and mobile phone call records to support elimination planning is described. One year of aggregated movement patterns for over a million people across Namibia are analyzed, and linked with case-based risk maps built on satellite imagery. By combining case-data and movement, the way human population movements connect transmission risk areas is demonstrated. Communities that were strongly connected by relatively higher levels of movement were then identified, and net export and import of travellers and infection risks by region were quantified. These maps can aid the design of targeted interventions to maximally reduce the number of cases exported to other regions while employing appropriate interventions to manage risk in places that import them.

Conclusions: The approaches presented can be rapidly updated and used to identify where active surveillance for both local and imported cases should be increased, which regions would benefit from coordinating efforts, and how spatially progressive elimination plans can be designed. With improvements in surveillance systems linked to improved diagnosis of malaria, detailed satellite imagery being readily available and mobile phone usage data continually being collected by network providers, the potential exists to make operational use of such valuable, complimentary and contemporary datasets on an ongoing basis in infectious disease control and elimination.

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Related in: MedlinePlus

Malaria risk zone maps and the size of populations to target according to the different categorizations. The refinement of the mapped areas shows how the method can be used to target high-risk areas and populations, providing a method for prioritizing the delivery of limited resources.
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Figure 13: Malaria risk zone maps and the size of populations to target according to the different categorizations. The refinement of the mapped areas shows how the method can be used to target high-risk areas and populations, providing a method for prioritizing the delivery of limited resources.

Mentions: Finally, Figure 13 demonstrates the utility of the combined mapping and movement quantification approach outlined here, through highlighting how high risk areas and populations could be prioritized for further investigation, surveillance and control. Existing national guidelines categorize the entire northern ‘zone 1’ region as the high-risk area where interventions should be focused. Through the rapid risk mapping approach, areas and populations within it can be highlighted that appear to be in particularly higher risk areas for cases. This refinement reduces the population to target from 1.29 million residing in the zone 1 region, to 0.24 million in the predicted higher risk zones. Within these zones, population movements mean that some areas are likely to be larger exporters (sources) of infections (Figures 8 and 9) than others, and the targeting of these can have a bigger effect on surrounding areas than the targeting of sinks (Figures 10, 11 and 12). Targeting only those populations residing in the major ‘source’ areas of the high risk zones, measured in this case by those phone catchment areas that are the top 50 largest sources (Figure 9), further reduces the focus population to 0.19 million.


Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning.

Tatem AJ, Huang Z, Narib C, Kumar U, Kandula D, Pindolia DK, Smith DL, Cohen JM, Graupe B, Uusiku P, Lourenço C - Malar. J. (2014)

Malaria risk zone maps and the size of populations to target according to the different categorizations. The refinement of the mapped areas shows how the method can be used to target high-risk areas and populations, providing a method for prioritizing the delivery of limited resources.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3927223&req=5

Figure 13: Malaria risk zone maps and the size of populations to target according to the different categorizations. The refinement of the mapped areas shows how the method can be used to target high-risk areas and populations, providing a method for prioritizing the delivery of limited resources.
Mentions: Finally, Figure 13 demonstrates the utility of the combined mapping and movement quantification approach outlined here, through highlighting how high risk areas and populations could be prioritized for further investigation, surveillance and control. Existing national guidelines categorize the entire northern ‘zone 1’ region as the high-risk area where interventions should be focused. Through the rapid risk mapping approach, areas and populations within it can be highlighted that appear to be in particularly higher risk areas for cases. This refinement reduces the population to target from 1.29 million residing in the zone 1 region, to 0.24 million in the predicted higher risk zones. Within these zones, population movements mean that some areas are likely to be larger exporters (sources) of infections (Figures 8 and 9) than others, and the targeting of these can have a bigger effect on surrounding areas than the targeting of sinks (Figures 10, 11 and 12). Targeting only those populations residing in the major ‘source’ areas of the high risk zones, measured in this case by those phone catchment areas that are the top 50 largest sources (Figure 9), further reduces the focus population to 0.19 million.

Bottom Line: Communities that were strongly connected by relatively higher levels of movement were then identified, and net export and import of travellers and infection risks by region were quantified.These maps can aid the design of targeted interventions to maximally reduce the number of cases exported to other regions while employing appropriate interventions to manage risk in places that import them.The approaches presented can be rapidly updated and used to identify where active surveillance for both local and imported cases should be increased, which regions would benefit from coordinating efforts, and how spatially progressive elimination plans can be designed.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Geography and Environment, University of Southampton, Southampton, UK. A.J.Tatem@soton.ac.uk.

ABSTRACT

Background: As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.

Methods/results: Here, using the example of Namibia, a method for targeting of interventions using surveillance data, satellite imagery, and mobile phone call records to support elimination planning is described. One year of aggregated movement patterns for over a million people across Namibia are analyzed, and linked with case-based risk maps built on satellite imagery. By combining case-data and movement, the way human population movements connect transmission risk areas is demonstrated. Communities that were strongly connected by relatively higher levels of movement were then identified, and net export and import of travellers and infection risks by region were quantified. These maps can aid the design of targeted interventions to maximally reduce the number of cases exported to other regions while employing appropriate interventions to manage risk in places that import them.

Conclusions: The approaches presented can be rapidly updated and used to identify where active surveillance for both local and imported cases should be increased, which regions would benefit from coordinating efforts, and how spatially progressive elimination plans can be designed. With improvements in surveillance systems linked to improved diagnosis of malaria, detailed satellite imagery being readily available and mobile phone usage data continually being collected by network providers, the potential exists to make operational use of such valuable, complimentary and contemporary datasets on an ongoing basis in infectious disease control and elimination.

Show MeSH
Related in: MedlinePlus