Use of earth observation-derived hydrometeorological variables to model and predict rotavirus infection (MAL-ED): a multisite cohort study

Josh M. Colston, Benjamin Zaitchik, Gagandeep Kang, Pablo Peñataro Yori, Tahmeed Ahmed, Aldo Lima, Ali Turab, Esto Mduma, Prakash Sunder Shrestha, Pascal Bessong, Roger D. Peng, Robert E. Black, Lawrence H. Moulton, Margaret N. Kosek

Research output: Contribution to journalArticle

Abstract

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/m2), specific humidity (kg/kg), and average daily temperatures (°C). Lag relationships, independent effects, and interactions were characterised by use of modified Poisson models and compared with and without adjustment for seasonality and between-site variation. Final models were created with stepwise selection of main effects and interactions and their validity assessed by excluding each site in turn and calculating Tjur's Coefficients of Determination. 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.

Original languageEnglish (US)
Pages (from-to)e248-e258
JournalThe Lancet Planetary Health
Volume3
Issue number6
DOIs
StatePublished - Jun 1 2019

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Rotavirus Infections
Cohort Studies
Observation
National Institutes of Health (U.S.)
surveillance
Humidity
Climate
Soil
United States National Aeronautics and Space Administration
interaction
Infectious Disease Transmission
climate
Rotavirus
Climate Change
Information Systems
Communicable Diseases
health
assimilation
Medicine
community

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health(social science)
  • Health Policy
  • Public Health, Environmental and Occupational Health

Cite this

Use of earth observation-derived hydrometeorological variables to model and predict rotavirus infection (MAL-ED) : a multisite cohort study. / Colston, Josh M.; Zaitchik, Benjamin; Kang, Gagandeep; Peñataro Yori, Pablo; Ahmed, Tahmeed; Lima, Aldo; Turab, Ali; Mduma, Esto; Sunder Shrestha, Prakash; Bessong, Pascal; Peng, Roger D.; Black, Robert E.; Moulton, Lawrence H.; Kosek, Margaret N.

In: The Lancet Planetary Health, Vol. 3, No. 6, 01.06.2019, p. e248-e258.

Research output: Contribution to journalArticle

Colston, Josh M. ; Zaitchik, Benjamin ; Kang, Gagandeep ; Peñataro Yori, Pablo ; Ahmed, Tahmeed ; Lima, Aldo ; Turab, Ali ; Mduma, Esto ; Sunder Shrestha, Prakash ; Bessong, Pascal ; Peng, Roger D. ; Black, Robert E. ; Moulton, Lawrence H. ; Kosek, Margaret N. / Use of earth observation-derived hydrometeorological variables to model and predict rotavirus infection (MAL-ED) : a multisite cohort study. In: The Lancet Planetary Health. 2019 ; Vol. 3, No. 6. pp. e248-e258.
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T2 - a multisite cohort study

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AU - Zaitchik, Benjamin

AU - Kang, Gagandeep

AU - Peñataro Yori, Pablo

AU - Ahmed, Tahmeed

AU - Lima, Aldo

AU - Turab, Ali

AU - Mduma, Esto

AU - Sunder Shrestha, Prakash

AU - Bessong, Pascal

AU - Peng, Roger D.

AU - Black, Robert E.

AU - Moulton, Lawrence H.

AU - Kosek, Margaret N.

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N2 - 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/m2), specific humidity (kg/kg), and average daily temperatures (°C). Lag relationships, independent effects, and interactions were characterised by use of modified Poisson models and compared with and without adjustment for seasonality and between-site variation. Final models were created with stepwise selection of main effects and interactions and their validity assessed by excluding each site in turn and calculating Tjur's Coefficients of Determination. 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.

AB - 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/m2), specific humidity (kg/kg), and average daily temperatures (°C). Lag relationships, independent effects, and interactions were characterised by use of modified Poisson models and compared with and without adjustment for seasonality and between-site variation. Final models were created with stepwise selection of main effects and interactions and their validity assessed by excluding each site in turn and calculating Tjur's Coefficients of Determination. 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.

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