Adjusting mobile phone data to account for children's travel and the impact on measles dynamics in Zambia.
Mobility
bias
call data records
children
infectious disease modeling
measles
mobile phone data
Journal
American journal of epidemiology
ISSN: 1476-6256
Titre abrégé: Am J Epidemiol
Pays: United States
ID NLM: 7910653
Informations de publication
Date de publication:
27 Aug 2024
27 Aug 2024
Historique:
received:
12
07
2023
revised:
01
06
2024
accepted:
21
08
2024
medline:
28
8
2024
pubmed:
28
8
2024
entrez:
27
8
2024
Statut:
aheadofprint
Résumé
Models of measles transmission can be used to identify areas of high risk to tailor immunization strategies. Estimates of spatial connectivity can be derived from data such as mobile phone records, however it is not clear how this maps to the movement of children who are more likely to be infected. Using travel surveys across two districts in Zambia and national mobile phone data, we compared estimates of out-of-district travel for the population captured in the mobile phone data and child-specific travel from travel surveys. We then evaluated the impact of unadjusted and adjusted connectivity measures on simulated measles virus introduction events across Zambia. The number of trips made by children from the travel survey was three to five times lower than the general population estimates from mobile phone data. This decreased the percentage of districts with measles virus introduction events from 78% when using unadjusted data to 51% - 64% following adjustment. Failure to account for age-specific heterogeneities in travel estimated from mobile phone data resulted in overestimating subnational areas at high risk of introduction events, which could divert mitigation efforts to districts that are at lower risk.
Identifiants
pubmed: 39191642
pii: 7742768
doi: 10.1093/aje/kwae304
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.