The University of Southampton
University of Southampton Institutional Repository

Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland.

Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland.
Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland.
Background
As successful malaria control programmes move towards elimination, they must identify residual transmission foci, target vector control to high-risk areas, focus on both asymptomatic and symptomatic infections, and manage importation risk. High spatial and temporal resolution maps of malaria risk can support all of these activities, but commonly available malaria maps are based on parasite rate, a poor metric for measuring malaria at extremely low prevalence. New approaches are required to provide case-based risk maps to countries seeking to identify remaining hotspots of transmission while managing the risk of transmission from imported cases.

Methods
Household locations and travel histories of confirmed malaria patients during 2011 were recorded through routine surveillance by the Swaziland National Malaria Control Programme for the higher transmission months of January to April and the lower transmission months of May to December. Household locations for patients with no travel history to endemic areas were compared against a random set of background points sampled proportionate to population density with respect to a set of variables related to environment, population density, vector control, and distance to the locations of identified imported cases. Comparisons were made separately for the high and low transmission seasons. The Random Forests regression tree classification approach was used to generate maps predicting the probability of a locally acquired case at 100 m resolution across Swaziland for each season.

Results
Results indicated that case households during the high transmission season tended to be located in areas of lower elevation, closer to bodies of water, in more sparsely populated areas, with lower rainfall and warmer temperatures, and closer to imported cases than random background points (all p < 0.001). Similar differences were evident during the low transmission season. Maps from the fit models suggested better predictive ability during the high season. Both models proved useful at predicting the locations of local cases identified in 2012.

Conclusions
The high-resolution mapping approaches described here can help elimination programmes understand the epidemiology of a disappearing disease. Generating case-based risk maps at high spatial and temporal resolution will allow control programmes to direct interventions proactively according to evidence-based measures of risk and ensure that the impact of limited resources is maximized to achieve and maintain malaria elimination.
1475-2875
61
Cohen, Justin M.
7de99049-a4c3-4fa1-8ff8-cc1bc5dcdfc9
Dlamini, Sabelo
9a85f328-c061-4759-a17c-d2cfab413171
Novotny, Joseph M.
c40ca80d-3b6e-4a68-927d-36eeaa7297c5
Kandula, Deepika
56b778d1-c466-49e7-83a5-6aa1d2ef79ed
Kunene, Simon
dc9b3e73-951a-49f8-a94c-ff8605de502c
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Cohen, Justin M.
7de99049-a4c3-4fa1-8ff8-cc1bc5dcdfc9
Dlamini, Sabelo
9a85f328-c061-4759-a17c-d2cfab413171
Novotny, Joseph M.
c40ca80d-3b6e-4a68-927d-36eeaa7297c5
Kandula, Deepika
56b778d1-c466-49e7-83a5-6aa1d2ef79ed
Kunene, Simon
dc9b3e73-951a-49f8-a94c-ff8605de502c
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Cohen, Justin M., Dlamini, Sabelo, Novotny, Joseph M., Kandula, Deepika, Kunene, Simon and Tatem, Andrew J. (2013) Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland. Malaria Journal, 12, 61. (doi:10.1186/1475-2875-12-61). (PMID:23398628)

Record type: Article

Abstract

Background
As successful malaria control programmes move towards elimination, they must identify residual transmission foci, target vector control to high-risk areas, focus on both asymptomatic and symptomatic infections, and manage importation risk. High spatial and temporal resolution maps of malaria risk can support all of these activities, but commonly available malaria maps are based on parasite rate, a poor metric for measuring malaria at extremely low prevalence. New approaches are required to provide case-based risk maps to countries seeking to identify remaining hotspots of transmission while managing the risk of transmission from imported cases.

Methods
Household locations and travel histories of confirmed malaria patients during 2011 were recorded through routine surveillance by the Swaziland National Malaria Control Programme for the higher transmission months of January to April and the lower transmission months of May to December. Household locations for patients with no travel history to endemic areas were compared against a random set of background points sampled proportionate to population density with respect to a set of variables related to environment, population density, vector control, and distance to the locations of identified imported cases. Comparisons were made separately for the high and low transmission seasons. The Random Forests regression tree classification approach was used to generate maps predicting the probability of a locally acquired case at 100 m resolution across Swaziland for each season.

Results
Results indicated that case households during the high transmission season tended to be located in areas of lower elevation, closer to bodies of water, in more sparsely populated areas, with lower rainfall and warmer temperatures, and closer to imported cases than random background points (all p < 0.001). Similar differences were evident during the low transmission season. Maps from the fit models suggested better predictive ability during the high season. Both models proved useful at predicting the locations of local cases identified in 2012.

Conclusions
The high-resolution mapping approaches described here can help elimination programmes understand the epidemiology of a disappearing disease. Generating case-based risk maps at high spatial and temporal resolution will allow control programmes to direct interventions proactively according to evidence-based measures of risk and ensure that the impact of limited resources is maximized to achieve and maintain malaria elimination.

Full text not available from this repository.

More information

Published date: 11 February 2013
Organisations: WorldPop, Geography & Environment, PHEW – P (Population Health)

Identifiers

Local EPrints ID: 348638
URI: https://eprints.soton.ac.uk/id/eprint/348638
ISSN: 1475-2875
PURE UUID: 057e7442-36ca-4317-b8bc-1fd4000d5109
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 18 Feb 2013 10:11
Last modified: 07 Aug 2019 00:34

Export record

Altmetrics

Contributors

Author: Justin M. Cohen
Author: Sabelo Dlamini
Author: Joseph M. Novotny
Author: Deepika Kandula
Author: Simon Kunene
Author: Andrew J. Tatem ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×