ArticleBMC public health2023
Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China.
Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Comparing frequentist and Bayesian methods to identify drivers of pathogen strain invasion: A temporal case study of pertussis in the United States.Infectious Disease Modelling · 2026Article
- Bayesian spatial modeling of measles outbreak in Texas, USA.Environmental and ecological statistics · 2026Article
- Spatio-temporal patterns and determinants of measles incidence in Ethiopia between 2018 and 2024.Frontiers in public health · 2026Article
- Modeling the influences of climate conditions on measles transmission in China.Epidemiology and infection · 2025Article
- Individual and contextual factors associated with measles infection in Malaysia: a multilevel analysis.Osong public health and research perspectives · 2024Article
- Spatio-temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model.Health science reports · 2024Article
- Time Series Forecasting of Measles Incidence in Pakistan, 2014-2024.Asia-Pacific journal of public healthArticle
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMeasles-containing vaccine (MCV) has been effective in controlling the spread of measles. Some countries have declared measles elimination. But recently years, the number of cases worldwide has increased, posing a challenge to the global goal of measles eradication. This study estimated the relationship between meteorological factors and measles using spatiotemporal Bayesian model, aiming to provide scientific evidence for public health policy to eliminate measles.
methodsDescriptive statistical analysis was performed on monthly data of measles and meteorological variables in 136 counties of Shandong Province from 2009 to 2017. Spatiotemporal Bayesian model was used to estimate the effects of meteorological factors on measles, and to evaluate measles risk areas at county level. Case population was divided into multiple subgroups according to gender, age and occupation. The effects of meteorological factors on measles in subgroups were compared.
resultsSpecific meteorological conditions increased the risk of measles, including lower relative humidity, temperature, and atmospheric pressure; higher wind velocity, sunshine duration, and diurnal temperature variation. Taking lowest value (Q1) as reference, RR (95%CI) for higher temperatures (Q2-Q4) were 0.79 (0.69-0.91), 0.54 (0.44-0.65), and 0.48 (0.38-0.61), respectively; RR (95%CI) for higher relative humidity (Q2-Q4) were 0.76 (0.66-0.88), 0.56 (0.47-0.67), and 0.49 (0.38-0.63), respectively; RR (95%CI) for higher wind velocity (Q2-Q4) were 1.43 (1.25-1.64), 1.85 (1.57-2.18), 2.00 (1.59-2.52), respectively. 22 medium-to-high risk counties were identified, mainly in northwestern, southwestern and central Shandong Province. The trend was basically same in the effects of meteorological factors on measles in subgroups, but the magnitude of the effects was different.
conclusionsMeteorological factors have an important impact on measles. It is crucial to integrate these factors into public health policies for measles prevention and control in China.
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