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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

The influence of connectivity through human mobility on the spatial impact of interventions. Map of a ‘target effectiveness’ metric, which measures the relative reduction in importation risk elsewhere through controlling at each specific location, with red locations representing the areas where reducing parasite exportation to zero has the largest effects elsewhere, through to green, where minimal effects are seen. Health district names are shown in Figure 5.
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Figure 12: The influence of connectivity through human mobility on the spatial impact of interventions. Map of a ‘target effectiveness’ metric, which measures the relative reduction in importation risk elsewhere through controlling at each specific location, with red locations representing the areas where reducing parasite exportation to zero has the largest effects elsewhere, through to green, where minimal effects are seen. Health district names are shown in Figure 5.

Mentions: Health districts are mapped in Figure 5. The shortened column titles refer to the following: Pop 2011 = number of people estimated to be residing in each health district in 2011; % Phone users = % of Pop 2011 population that were estimated to be captured in the CDR dataset, based on numbers of unique anonymous user IDs; Mean risk = mean population weighted predicted malaria case risk on a 0-1 scale; Mean RoG = mean radius of gyration [24] of movements derived from the phone data (See Additional file 2 for more details); Mean trip length = mean length of trip taken in days away from home phone catchment area (See Additional file 2 for more details); Mean no. trips = Mean number of trips taken per year away from home phone catchment area (See Additional file 2 for more details); Move comm = movement community that the majority of the area of each health district belongs to (Additional file 2); Risk comm = malaria risk community that the majority of the area of each health district belongs to (Additional file 2); Pop in risk >50% and top 50 source = Number of people residing in areas where risk values are >0.5, and that are in the top 50 ‘source’ regions (Figure 9); Mean effect index = mean value of the effect index mapped in Figure 12.


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)

The influence of connectivity through human mobility on the spatial impact of interventions. Map of a ‘target effectiveness’ metric, which measures the relative reduction in importation risk elsewhere through controlling at each specific location, with red locations representing the areas where reducing parasite exportation to zero has the largest effects elsewhere, through to green, where minimal effects are seen. Health district names are shown in Figure 5.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 12: The influence of connectivity through human mobility on the spatial impact of interventions. Map of a ‘target effectiveness’ metric, which measures the relative reduction in importation risk elsewhere through controlling at each specific location, with red locations representing the areas where reducing parasite exportation to zero has the largest effects elsewhere, through to green, where minimal effects are seen. Health district names are shown in Figure 5.
Mentions: Health districts are mapped in Figure 5. The shortened column titles refer to the following: Pop 2011 = number of people estimated to be residing in each health district in 2011; % Phone users = % of Pop 2011 population that were estimated to be captured in the CDR dataset, based on numbers of unique anonymous user IDs; Mean risk = mean population weighted predicted malaria case risk on a 0-1 scale; Mean RoG = mean radius of gyration [24] of movements derived from the phone data (See Additional file 2 for more details); Mean trip length = mean length of trip taken in days away from home phone catchment area (See Additional file 2 for more details); Mean no. trips = Mean number of trips taken per year away from home phone catchment area (See Additional file 2 for more details); Move comm = movement community that the majority of the area of each health district belongs to (Additional file 2); Risk comm = malaria risk community that the majority of the area of each health district belongs to (Additional file 2); Pop in risk >50% and top 50 source = Number of people residing in areas where risk values are >0.5, and that are in the top 50 ‘source’ regions (Figure 9); Mean effect index = mean value of the effect index mapped in Figure 12.

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