Use of earth observation-derived hydrometeorological variables to model and predict rotavirus infection (MAL-ED): a multisite cohort study.
Bangladesh
/ epidemiology
Brazil
/ epidemiology
Climate
Cohort Studies
Humans
Incidence
India
/ epidemiology
Infant
Infant, Newborn
Models, Theoretical
Nepal
/ epidemiology
Pakistan
/ epidemiology
Peru
/ epidemiology
Rotavirus Infections
/ epidemiology
South Africa
/ epidemiology
Tanzania
/ epidemiology
Weather
Journal
The Lancet. Planetary health
ISSN: 2542-5196
Titre abrégé: Lancet Planet Health
Pays: Netherlands
ID NLM: 101704339
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
03
07
2018
revised:
08
04
2019
accepted:
09
04
2019
entrez:
24
6
2019
pubmed:
24
6
2019
medline:
7
7
2020
Statut:
ppublish
Résumé
Climate change threatens to undermine recent progress in reducing global deaths from diarrhoeal disease in children. However, the scarcity of evidence about how individual environmental factors affect transmission of specific pathogens makes prediction of trends under different climate scenarios challenging. We aimed to model associations between daily estimates of a suite of hydrometeorological variables and rotavirus infection status ascertained through community-based surveillance. For this analysis of multisite cohort data, rotavirus infection status was ascertained through community-based surveillance of infants in the eight-site MAL-ED cohort study, and matched by date with earth observation estimates of nine hydrometeorological variables from the Global Land Data Assimilation System: daily total precipitation volume (mm), daily total surface runoff (mm), surface pressure (mbar), wind speed (m/s), relative humidity (%), soil moisture (%), solar radiation (W/m All nine hydrometeorological variables were significantly associated with rotavirus infection after adjusting for seasonality and between-site variation over multiple consecutive or non-consecutive lags, showing complex, often non-linear associations that differed by symptom status and showed considerable mutual interaction. The final models explained 5·9% to 6·2% of the variability in rotavirus infection in the pooled data and their predictions explained between 0·0% and 14·1% of the variability at individual study sites. These results suggest that the effect of climate on rotavirus transmission was mediated by four independent mechanisms: waterborne dispersal, airborne dispersal, virus survival on soil and surfaces, and host factors. Earth observation data products available at a global scale and at subdaily resolution can be combined with longitudinal surveillance data to test hypotheses about routes and drivers of transmission but showed little potential for making predictions in this setting. Bill & Melinda Gates Foundation; Foundation for the National Institutes of Health, National Institutes of Health, Fogarty International Center; Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases, Johns Hopkins School of Medicine; and NASA's Group on Earth Observations Work Programme.
Sections du résumé
BACKGROUND
Climate change threatens to undermine recent progress in reducing global deaths from diarrhoeal disease in children. However, the scarcity of evidence about how individual environmental factors affect transmission of specific pathogens makes prediction of trends under different climate scenarios challenging. We aimed to model associations between daily estimates of a suite of hydrometeorological variables and rotavirus infection status ascertained through community-based surveillance.
METHODS
For this analysis of multisite cohort data, rotavirus infection status was ascertained through community-based surveillance of infants in the eight-site MAL-ED cohort study, and matched by date with earth observation estimates of nine hydrometeorological variables from the Global Land Data Assimilation System: daily total precipitation volume (mm), daily total surface runoff (mm), surface pressure (mbar), wind speed (m/s), relative humidity (%), soil moisture (%), solar radiation (W/m
FINDINGS
All nine hydrometeorological variables were significantly associated with rotavirus infection after adjusting for seasonality and between-site variation over multiple consecutive or non-consecutive lags, showing complex, often non-linear associations that differed by symptom status and showed considerable mutual interaction. The final models explained 5·9% to 6·2% of the variability in rotavirus infection in the pooled data and their predictions explained between 0·0% and 14·1% of the variability at individual study sites.
INTERPRETATION
These results suggest that the effect of climate on rotavirus transmission was mediated by four independent mechanisms: waterborne dispersal, airborne dispersal, virus survival on soil and surfaces, and host factors. Earth observation data products available at a global scale and at subdaily resolution can be combined with longitudinal surveillance data to test hypotheses about routes and drivers of transmission but showed little potential for making predictions in this setting.
FUNDING
Bill & Melinda Gates Foundation; Foundation for the National Institutes of Health, National Institutes of Health, Fogarty International Center; Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases, Johns Hopkins School of Medicine; and NASA's Group on Earth Observations Work Programme.
Identifiants
pubmed: 31229000
pii: S2542-5196(19)30084-1
doi: 10.1016/S2542-5196(19)30084-1
pmc: PMC6650544
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e248-e258Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.
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