Limits...
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.

Show MeSH

Related in: MedlinePlus

The influence of connectivity through human mobility on the spatial impact of interventions. The percentage reduction in importation risk through reducing parasite exportation numbers to zero in the phone catchment area marked in blue, which is of similar population size and risk level to the focus phone catchment area of Figure 10, but has lower movement rates.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 11: The influence of connectivity through human mobility on the spatial impact of interventions. The percentage reduction in importation risk through reducing parasite exportation numbers to zero in the phone catchment area marked in blue, which is of similar population size and risk level to the focus phone catchment area of Figure 10, but has lower movement rates.

Mentions: The quantification of source/sink regions shown in Figure 3 enables theoretical scenarios on the impact targeting of control on malaria risk connectivity to be tested to guide attack strategies. Figures 10 and 11 demonstrate how differences in movement patterns can make a substantial difference in terms of regional impact on relative rates of case importation seen elsewhere through intervening in different areas. In Figure 10, a scenario is shown where the predicted malaria case risk at the phone catchment area highlighted is reduced to zero. As this is one of the largest source areas (Figure 9), the relative impact of this intervention is substantial across a wide region, with most impact within the malaria movement community it belongs to (Additional file 2). In contrast, the same intervention in a phone catchment area of similar population size and malaria risk, but lower mobility in terms of numbers and range of trips made to other catchment areas, shows a substantially smaller impact, both in magnitude and geographic extent terms (Figure 11). These intervention effects on relative impacts of infection exportation can be summarized through a simple ‘target effectiveness’ metric that measures, for each area, the relative levels of case importation elsewhere that would be stopped if malaria risk was reduced to zero in the area. This metric is mapped in Figure 12 and summarized by health district in Table 1, and shows a heterogenous pattern, indicating that the targeting of surveillance and control in certain areas may have a much larger impact on the surrounding region than in other neighbouring areas.


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. The percentage reduction in importation risk through reducing parasite exportation numbers to zero in the phone catchment area marked in blue, which is of similar population size and risk level to the focus phone catchment area of Figure 10, but has lower movement rates.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 11: The influence of connectivity through human mobility on the spatial impact of interventions. The percentage reduction in importation risk through reducing parasite exportation numbers to zero in the phone catchment area marked in blue, which is of similar population size and risk level to the focus phone catchment area of Figure 10, but has lower movement rates.
Mentions: The quantification of source/sink regions shown in Figure 3 enables theoretical scenarios on the impact targeting of control on malaria risk connectivity to be tested to guide attack strategies. Figures 10 and 11 demonstrate how differences in movement patterns can make a substantial difference in terms of regional impact on relative rates of case importation seen elsewhere through intervening in different areas. In Figure 10, a scenario is shown where the predicted malaria case risk at the phone catchment area highlighted is reduced to zero. As this is one of the largest source areas (Figure 9), the relative impact of this intervention is substantial across a wide region, with most impact within the malaria movement community it belongs to (Additional file 2). In contrast, the same intervention in a phone catchment area of similar population size and malaria risk, but lower mobility in terms of numbers and range of trips made to other catchment areas, shows a substantially smaller impact, both in magnitude and geographic extent terms (Figure 11). These intervention effects on relative impacts of infection exportation can be summarized through a simple ‘target effectiveness’ metric that measures, for each area, the relative levels of case importation elsewhere that would be stopped if malaria risk was reduced to zero in the area. This metric is mapped in Figure 12 and summarized by health district in Table 1, and shows a heterogenous pattern, indicating that the targeting of surveillance and control in certain areas may have a much larger impact on the surrounding region than in other neighbouring areas.

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