Publications
1.
Tessema, Sofonias; Wesolowski, Amy; Chen, Anna; Murphy, Maxwell; Wilheim, Jordan; Mupiri, Anna-Rosa; Ruktanonchai, Nick W; Alegana, Victor A; Tatem, Andrew J; Tambo, Munyaradzi; Didier, Bradley; Cohen, Justin M; Bennett, Adam; Sturrock, Hugh JW; Gosling, Roland; Hsiang, Michelle S; Smith, David L; Mumbengegwi, Davis R; Smith, Jennifer L; Greenhouse, Bryan
Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa Journal Article
In: eLife, vol. 8, pp. e43510, 2019, ISSN: 2050-084X.
Abstract | Links | BibTeX | Tags: genetic epidemiology, malaria, Mobility, parasite genomics
@article{10.7554/eLife.43510,
title = {Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa},
author = {Sofonias Tessema and Amy Wesolowski and Anna Chen and Maxwell Murphy and Jordan Wilheim and Anna-Rosa Mupiri and Nick W Ruktanonchai and Victor A Alegana and Andrew J Tatem and Munyaradzi Tambo and Bradley Didier and Justin M Cohen and Adam Bennett and Hugh JW Sturrock and Roland Gosling and Michelle S Hsiang and David L Smith and Davis R Mumbengegwi and Jennifer L Smith and Bryan Greenhouse},
editor = {Ben Cooper and Neil M Ferguson and Samir Bhatt and Thomas S Churcher},
url = {https://doi.org/10.7554/eLife.43510},
doi = {10.7554/eLife.43510},
issn = {2050-084X},
year = {2019},
date = {2019-04-01},
journal = {eLife},
volume = {8},
pages = {e43510},
publisher = {eLife Sciences Publications, Ltd},
abstract = {Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.},
keywords = {genetic epidemiology, malaria, Mobility, parasite genomics},
pubstate = {published},
tppubtype = {article}
}
Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.
2.
Bhatt, Samir; Weiss, Daniel J; Mappin, Bonnie; Dalrymple, Ursula; Cameron, Ewan; Bisanzio, Donal; Smith, David L; Moyes, Catherine L; Tatem, Andrew J; Lynch, Michael; Fergus, Cristin A; Yukich, Joshua; Bennett, Adam; Eisele, Thomas P; Kolaczinski, Jan; Cibulskis, Richard E; Hay, Simon I; Gething, Peter W
Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017 Journal Article
In: eLife, vol. 4, pp. e09672, 2015, ISSN: 2050-084X.
Abstract | Links | BibTeX | Tags: Bayesian compartment model, ITN, LLIN, malaria
@article{10.7554/eLife.09672,
title = {Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017},
author = {Samir Bhatt and Daniel J Weiss and Bonnie Mappin and Ursula Dalrymple and Ewan Cameron and Donal Bisanzio and David L Smith and Catherine L Moyes and Andrew J Tatem and Michael Lynch and Cristin A Fergus and Joshua Yukich and Adam Bennett and Thomas P Eisele and Jan Kolaczinski and Richard E Cibulskis and Simon I Hay and Peter W Gething},
editor = {Catherine Kyobutungi},
url = {https://doi.org/10.7554/eLife.09672},
doi = {10.7554/eLife.09672},
issn = {2050-084X},
year = {2015},
date = {2015-12-01},
urldate = {2015-12-01},
journal = {eLife},
volume = {4},
pages = {e09672},
publisher = {eLife Sciences Publications, Ltd},
abstract = {Insecticide-treated nets (ITNs) for malaria control are widespread but coverage remains inadequate. We developed a Bayesian model using data from 102 national surveys, triangulated against delivery data and distribution reports, to generate year-by-year estimates of four ITN coverage indicators. We explored the impact of two potential ínefficiencies': uneven net distribution among households and rapid rates of net loss from households. We estimated that, in 2013, 21% (17%–26%) of ITNs were over-allocated and this has worsened over time as overall net provision has increased. We estimated that rates of ITN loss from households are more rapid than previously thought, with 50% lost after 23 (20–28) months. We predict that the current estimate of 920 million additional ITNs required to achieve universal coverage would in reality yield a lower level of coverage (77% population access). By improving efficiency, however, the 920 million ITNs could yield population access as high as 95%.},
keywords = {Bayesian compartment model, ITN, LLIN, malaria},
pubstate = {published},
tppubtype = {article}
}
Insecticide-treated nets (ITNs) for malaria control are widespread but coverage remains inadequate. We developed a Bayesian model using data from 102 national surveys, triangulated against delivery data and distribution reports, to generate year-by-year estimates of four ITN coverage indicators. We explored the impact of two potential ínefficiencies': uneven net distribution among households and rapid rates of net loss from households. We estimated that, in 2013, 21% (17%–26%) of ITNs were over-allocated and this has worsened over time as overall net provision has increased. We estimated that rates of ITN loss from households are more rapid than previously thought, with 50% lost after 23 (20–28) months. We predict that the current estimate of 920 million additional ITNs required to achieve universal coverage would in reality yield a lower level of coverage (77% population access). By improving efficiency, however, the 920 million ITNs could yield population access as high as 95%.
