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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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Predicted probability of malaria cases in January-May 2011 for Omusati region. The residential location of RDT confirmed cases are mapped as crosses.
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Figure 1: Predicted probability of malaria cases in January-May 2011 for Omusati region. The residential location of RDT confirmed cases are mapped as crosses.

Mentions: De-identified data on cases of malaria confirmed using rapid diagnostic tests (RDTs) reporting to health facilities across the three highest transmission regions, Kavango, Omusati and Caprivi (Figures 1, 2, 3 and 4) for the malaria transmission season in January to May 2011 were collected by the Namibia National Vector-borne Diseases Control Programme (NVDCP) in the course of routine surveillance. The community of residence of each patient, as reported to nurses at health facilities at the time of treatment, was geolocated. A total of 109 cases from 74 unique locations in Kavango, 234 cases from 41 unique locations in Omusati and 332 cases from 47 unique locations in Caprivi were successfully geolocated. The average age of cases across settlements and districts showed no systematic differences or biases. This indicated that transmission was likely not high enough in any location for significant immunity to develop and result in lower case loads due to immunity effects, rather than environmental drivers. The procedure for producing high resolution risk maps from the case location data followed closely that outlined in Cohen et al. [16] and is described below. Further details are provided in Additional file 1.


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)

Predicted probability of malaria cases in January-May 2011 for Omusati region. The residential location of RDT confirmed cases are mapped as crosses.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Predicted probability of malaria cases in January-May 2011 for Omusati region. The residential location of RDT confirmed cases are mapped as crosses.
Mentions: De-identified data on cases of malaria confirmed using rapid diagnostic tests (RDTs) reporting to health facilities across the three highest transmission regions, Kavango, Omusati and Caprivi (Figures 1, 2, 3 and 4) for the malaria transmission season in January to May 2011 were collected by the Namibia National Vector-borne Diseases Control Programme (NVDCP) in the course of routine surveillance. The community of residence of each patient, as reported to nurses at health facilities at the time of treatment, was geolocated. A total of 109 cases from 74 unique locations in Kavango, 234 cases from 41 unique locations in Omusati and 332 cases from 47 unique locations in Caprivi were successfully geolocated. The average age of cases across settlements and districts showed no systematic differences or biases. This indicated that transmission was likely not high enough in any location for significant immunity to develop and result in lower case loads due to immunity effects, rather than environmental drivers. The procedure for producing high resolution risk maps from the case location data followed closely that outlined in Cohen et al. [16] and is described below. Further details are provided in Additional file 1.

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