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Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal
BACKGROUND: Incidence of respiratory syncytial virus (RSV) and rhinovirus (RHV) varies throughout the year. We aim to quantify the relationship between weather variables (temperature, humidity, precipitation, and aerosol concentration) and disease incidence in order to quantify how outbreaks of RSV...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631296/ http://dx.doi.org/10.1093/ofid/ofx163.873 |
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author | Scott, Anna Englund, Janet Chu, Helen Tielsch, James Tielsch, James Khatry, Subarna Leclerq, Steven C Shrestha, Laxman Kuypers, Jane Steinhoff, Mark C Katz, Joanne |
author_facet | Scott, Anna Englund, Janet Chu, Helen Tielsch, James Tielsch, James Khatry, Subarna Leclerq, Steven C Shrestha, Laxman Kuypers, Jane Steinhoff, Mark C Katz, Joanne |
author_sort | Scott, Anna |
collection | PubMed |
description | BACKGROUND: Incidence of respiratory syncytial virus (RSV) and rhinovirus (RHV) varies throughout the year. We aim to quantify the relationship between weather variables (temperature, humidity, precipitation, and aerosol concentration) and disease incidence in order to quantify how outbreaks of RSV and RHV are related to seasonal or sub-seasonal meteorology, and if these relationships can predict viral outbreaks of RSV and RHV. METHODS: Health data were collected in a community-based, prospective randomized trial of maternal influenza immunization of pregnant women and their infants conducted in rural Nepal from 2011–2014. Adult illness episodes were defined as fever plus cough, sore throat, runny nose, and/or myalgia, with infant illness defined similarly but without fever requirement. Cases were identified through longitudinal household-based weekly surveillance. Temperature, humidity, precipitation, and fine particulate matter (PM 2.5) data come from reanalysis data products NCEP, Era-Interim, and Merra-2, which are produced by assimilating historical in-situ and satellite-based observations into a weather model. RESULTS: RSV exhibits a relationship with temperature after removing the seasonal cycle (r = -0.16, N = 208, P = 0.02), and RHV exhibits a strong relationship to daily temperature (r =-0.14, N =208, P = 0.05). When lagging meteorology by up to 15 weeks, correlations with disease count and weather improve (RSV: r_max = 0.45, P < 0.05; RHV: r_max = 0.15, P = 0.05). We use an SIR model forced by lagged meteorological variables to predict RSV and RHV, suggesting that disease burden can be predicted at lead times of weeks to months. CONCLUSION: Meteorological variables are associated with RSV and RHV incidence in rural Nepal and can be used to drive predictive models with a lead time of several months. DISCLOSURES: J. Englund, Gilead: Consultant and Investigator, Research support Chimerix: Investigator, Research support Alios: Investigator, Research support Novavax: Investigator, Research support MedImmune: Investigator, Research support GlaxoSmithKline: Investigator, Research support |
format | Online Article Text |
id | pubmed-5631296 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-56312962017-11-07 Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal Scott, Anna Englund, Janet Chu, Helen Tielsch, James Tielsch, James Khatry, Subarna Leclerq, Steven C Shrestha, Laxman Kuypers, Jane Steinhoff, Mark C Katz, Joanne Open Forum Infect Dis Abstracts BACKGROUND: Incidence of respiratory syncytial virus (RSV) and rhinovirus (RHV) varies throughout the year. We aim to quantify the relationship between weather variables (temperature, humidity, precipitation, and aerosol concentration) and disease incidence in order to quantify how outbreaks of RSV and RHV are related to seasonal or sub-seasonal meteorology, and if these relationships can predict viral outbreaks of RSV and RHV. METHODS: Health data were collected in a community-based, prospective randomized trial of maternal influenza immunization of pregnant women and their infants conducted in rural Nepal from 2011–2014. Adult illness episodes were defined as fever plus cough, sore throat, runny nose, and/or myalgia, with infant illness defined similarly but without fever requirement. Cases were identified through longitudinal household-based weekly surveillance. Temperature, humidity, precipitation, and fine particulate matter (PM 2.5) data come from reanalysis data products NCEP, Era-Interim, and Merra-2, which are produced by assimilating historical in-situ and satellite-based observations into a weather model. RESULTS: RSV exhibits a relationship with temperature after removing the seasonal cycle (r = -0.16, N = 208, P = 0.02), and RHV exhibits a strong relationship to daily temperature (r =-0.14, N =208, P = 0.05). When lagging meteorology by up to 15 weeks, correlations with disease count and weather improve (RSV: r_max = 0.45, P < 0.05; RHV: r_max = 0.15, P = 0.05). We use an SIR model forced by lagged meteorological variables to predict RSV and RHV, suggesting that disease burden can be predicted at lead times of weeks to months. CONCLUSION: Meteorological variables are associated with RSV and RHV incidence in rural Nepal and can be used to drive predictive models with a lead time of several months. DISCLOSURES: J. Englund, Gilead: Consultant and Investigator, Research support Chimerix: Investigator, Research support Alios: Investigator, Research support Novavax: Investigator, Research support MedImmune: Investigator, Research support GlaxoSmithKline: Investigator, Research support Oxford University Press 2017-10-04 /pmc/articles/PMC5631296/ http://dx.doi.org/10.1093/ofid/ofx163.873 Text en © The Author 2017. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Scott, Anna Englund, Janet Chu, Helen Tielsch, James Tielsch, James Khatry, Subarna Leclerq, Steven C Shrestha, Laxman Kuypers, Jane Steinhoff, Mark C Katz, Joanne Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal |
title | Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal |
title_full | Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal |
title_fullStr | Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal |
title_full_unstemmed | Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal |
title_short | Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal |
title_sort | meterology-driven prediction of rsv/rhv incidence in rural nepal |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631296/ http://dx.doi.org/10.1093/ofid/ofx163.873 |
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