Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016

Abstract

BackgroundThe resurgence of mumps around the world occurs frequently in recent years. As the country with the largest number of cases in the world, the status of mumps epidemics in China is not yet clear. This study, taking the relatively serious epidemic province of Guangxi as the example, aimed to examine the spatiotemporal pattern and epidemiological characteristics of mumps, and provide a scientific basis for the effective control of this disease and formulation of related health policies.MethodsGeographic information system (GIS)-based spatiotemporal analyses, including spatial autocorrelation analysis, Kulldorff’s purely spatial and space-time scan statistics, were applied to detect the location and extent of mumps high-risk areas. Spatial empirical Bayesian (SEB) was performed to smoothen the rate for eliminating the instability of small-area data.ResultsA total of 208,470 cases were reported during 2005 and 2016 in Guangxi. Despite the fluctuations in 2006 and 2011, the overall mumps epidemic continued to decline. Bimodal seasonal distribution (mainly from April to July) were found and students aged 5–9 years were high-incidence groups. Though results of the global spatial autocorrelation based on the annual incidence largely varied, the spatial distribution of the average annual incidence of mumps was nonrandom with the significant Moran’s I. Spatial cluster analysis detected high-value clusters, mainly located in the western, northern and central parts of Guangxi. Spatiotemporal scan statistics identified almost the same high-risk areas, and the aggregation time was mainly concentrated in 2009–2012.ConclusionThe incidence of mumps in Guangxi exhibited spatial heterogeneity in 2005–2016. Several spatial and spatiotemporal clusters were identified in this study, which might assist the local government to develop targeted health strategies, allocate health resources reasonably and increase the efficiency of disease prevention.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3240-4) contains supplementary material, which is available to authorized users.

Effects of extreme meteorological factors on daily mumps cases in Hefei, China, during 2011–2016

“…A time series study conducted in Fujian (Hu et al 2018) showed that various meteorological factors, including temperature, relative humidity, air pressure, rainfall, wind speed, and sunshine duration, affect the incidence of mumps, for example, low temperatures and wind velocity were found to increase the risk of mumps. This view is consistent with a study in Guangxi (Yu et al 2018a, b). However, a finding obtained in Guangzhou (Yang et al 2014b) showed that low temperature and wind velocity are protective factors for mumps.…”

“…Recent studies have shown that meteorological factors may influence mumps incidence (Hu et al 2018;Yang et al 2014b;Yu et al 2018a, b). In the USA (Patel et al 2017), South Korea (Choe et al 2017) and Netherlands (Sane et al 2014), mumps showed a seasonal variation and peaked during spring.…”

“…Evidences accumulated over the past 20 years suggests that extreme weather conditions may be associated with adverse health outcomes, such as increasing prevalence, morbidity, and mortality due to respiratory diseases (De et al 2013;George et al 2017). Extreme temperature may affect the onset of infectious diseases, such as mumps, measles, and hand, foot, and mouth disease (Yu et al 2019;Yang et al 2014a;Yu et al 2018a, b). Hitherto, no study has been conducted to clearly define the extreme values of meteorological factors and how it relates to acquiring mumps.…”

Spatiotemporal Distribution of Varicella in South Korea

“…A spatiotemporal analysis of varicella in Valencia, Spain from 2008 to 2012 identi ed spatiotemporal clusters where the population was economically disadvantaged or perhaps less educated and less aware of vaccination schedules [5]. In spatio-temporal analysis of measles [6] and mumps [7] in China, high-risk clusters were mainly distributed in the urban-rural transition zones or semi-urban areas because, with parents migrating to urban areas for employment opportunities, children were left in impoverished and remote area from vaccination clinics and become susceptible to disease.…”

Spatiotemporal Data Analysis and Forecasting Model for Forestland Rehabilitation

“…With the said database, spatial distribution of the species where identified per region, which led them identify loss of biodiversity and imbalances in the environment (König et al, 2019). Moreover, Yu et al (2018) established that visualizations is important means of communicating and representing massive data sets. Spatiotemporal visualization sometimes in a form of shapefile can be widely used as an instrument to depict results for decision-making processes.…”