Notifiable Respiratory Infectious Diseases in China: A Spatial–Temporal Epidemiology Analysis

Abstract

Nowadays, tuberculosis, scarlet fever, measles, influenza, and mumps are five major notifiable respiratory infectious diseases (RIDs) in China. The objective of this study was to describe, visualize, and compare the spatial-temporal distributions of these five RIDs from 2006 to 2016. In addition to descriptive epidemiology analysis, seasonality and spatial autocorrelation analysis were also applied to explore the epidemiologic trends and spatial changing patterns of the five RIDs, respectively. The results indicated that the incidence of tuberculosis, measles, and mumps presented a downtrend trend, while those of scarlet fever and influenza was in a strong uptrend across the research period. The incidences of the five diseases all peaked in spring. There were significant spatial disparities in the distribution of tuberculosis, scarlet fever, and measles cases, with the hotspots mainly located in the western plateau region, northern plain region, and southern mountainous region. To conclude, notable epidemiological differences were observed across regions, indicating that some provincial units should pay more attention to prevent and control respiratory infectious diseases.

Analysis of Spatial-Temporal Distribution of Respiratory Infectious Diseases in Shandong Province, China During 2005-2014

“…However, the incidence of scarlet fever, mumps and in uenza had been rising in recent years in Shandong Province, which suggested to improve the awareness of prevention and treatment of respiratory infectious diseases. The temporal distribution characteristics of respiratory infectious diseases were consistent with the ndings of Mao et al [18]. The reasons for the increased incidence of these respiratory infections may be as follows: some respiratory infectious pathogens mutate, which can increase susceptibility of humans, such as the emergence of new in uenza virus [30]; and some respiratory infections are asymptomatic or subclinical infections, which have the potential to cause outbreaks.…”

“…To our knowledge, only one study has systematically explored the epidemiologic trends and spatial changing patterns of respiratory infectious diseases, but which analyzed at the provincial level in China [18]. Therefore, cluster analysis of respiratory infectious diseases at a more precise level is urgently needed.…”

Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions, in the United States from 2011 to 2020

“…The local Moran's I detected the clusters based on the administrative divisions. [16] The high incidence of in uenza was mainly concentrated in the states of Louisiana, Virginia and Mississippi, and the Local spatial autocorrelation analysis revealed the HH cluster was mainly located in Louisiana and Mississippi. This means that if the in uenza incidence is high in Louisiana and Mississippi, the neighboring states will also have higher in uenza incidence rates.…”

“…The number of permutation was 999, and the signi cance level was 0.05. [16] Unlike spatial autocorrelation, scan statistics can evaluate the aggregation of observations and the location of aggregated observations. Spatio-temporal scan statistics are de ned by a speci c window with a circular geographic base and height corresponding to time.…”

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

“…Infectious diseases generally have distribution characteristics of spatial autocorrelation (Adham et al 2020 ; Masinaei et al 2020 ; Ding et al 2021 ). Spatial autocorrelation embodies the distribution law of the agglomeration or dispersion of infectious diseases on the spatial level, which is of great significance to the stage analysis and situation prediction of diseases (Zhang et al 2019 ; Mao et al 2020 ).…”