2020
DOI: 10.1038/s41598-020-70761-0
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Spatiotemporal dynamics of hemorrhagic fever with renal syndrome in Jiangxi province, China

Abstract: Historically, Jiangxi province has had the largest HfRS burden in china. However, thus far, the comprehensive understanding of the spatiotemporal distributions of HfRS is limited in Jiangxi. in this study, seasonal decomposition analysis, spatial autocorrelation analysis, and space-time scan statistic analyses were performed to detect the spatiotemporal dynamics distribution of HfRS cases from 2005 to 2018 in Jiangxi at the county scale. The epidemic of HFRS showed the characteristic of bi-peak seasonality, th… Show more

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Cited by 10 publications
(14 citation statements)
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“…Monthly scrub typhus cases at the county-level in Fujian provide were aggregated for time series analysis. A seasonal-trend decomposition of time series was applied to explore the various characteristics of periodicity and seasonality in incidence over a 9-year period by R software (Version 4.1, Lucent Technologies Bell Laboratories, Auckland, New Zealand) [ 19 , 20 ]. Seasonal and Trend decomposition using Loess (STL, Loess stands for locally estimated scatterplot smoothing) is a versatile and robust filtering method to decompose time series into trend component, seasonal component and random component for the reason that occasional anomalous observations do not affect the estimation of trend and seasonal components.…”
Section: Methodsmentioning
confidence: 99%
“…Monthly scrub typhus cases at the county-level in Fujian provide were aggregated for time series analysis. A seasonal-trend decomposition of time series was applied to explore the various characteristics of periodicity and seasonality in incidence over a 9-year period by R software (Version 4.1, Lucent Technologies Bell Laboratories, Auckland, New Zealand) [ 19 , 20 ]. Seasonal and Trend decomposition using Loess (STL, Loess stands for locally estimated scatterplot smoothing) is a versatile and robust filtering method to decompose time series into trend component, seasonal component and random component for the reason that occasional anomalous observations do not affect the estimation of trend and seasonal components.…”
Section: Methodsmentioning
confidence: 99%
“…More than two-thirds of the province is covered by forests, exceeding the national average of 20% [27]. Through referring to previous studies on the geographic distribution of rodents [21,28], Jiangxi was divided into five zoogeographic regions, including the plain region bordering on rivers and lakes, Jiangxi's northern hilly region, Wuyi's mountainous hilly region, Wugong's mountainous hilly region, and Jiangxi's southern mountainous region (Figure 1C).…”
Section: Study Areamentioning
confidence: 99%
“…Given that the power of quantitative statistics and mapping visualization, techniques of spatial epidemiological have been widely applied in infectious disease control, prevention, and scientific investigations [20][21][22][23][24]. As a vector-borne infectious disease, the distribution of ST has significant spatial heterogeneity.…”
Section: Introductionmentioning
confidence: 99%
“…HFRS was first identified in 1961 in Jiangxi province, China. The areas affected by HFRS expanded from six counties in the 1960s to 88 counties in the 1990s; by 2021, a total of 97 counties reported cases of HFRS [4]. Although the incidence and case fatality rate of HFRS have dramatically declined in recent decades, owing to public health service efforts and markedly improved living environments, the population under threat of hantavirus infection is increasing.…”
Section: Introductionmentioning
confidence: 99%