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The geo-location and city/county distribution of Jiangsu Province in China. (The map was created with ArcGIS software version 10.8).
Source publication
The mumps resurgence has frequently been reported around the world in recent years, especially in many counties mumps vaccines have been widely used. This study aimed to describe the spatial epidemiological characteristics of mumps in Jiangsu, and provide a scientific basis for the implementation and adjustment of strategies to prevent and control...
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Citations
... First, the influencing factors of influenza seasonal prevalence. Transmission of influenza has obvious seasonal characteristics, especially a higher incidence rate in winter and spring, which is consistent with many respiratory diseases [49,50]. Hubei Province is located in a subtropical monsoon climate region, and most parts experience cold winters, with an average temperature between 2˚C and 4˚C, hot summers, variable spring temperatures, and rapid fall in autumn temperatures. ...
... All suggest that areas with better economic conditions have higher levels of available health care, higher rates of active medical visits by residents, and higher rates of influenza diagnosis, which may lead to better economic areas having higher influenza incidence rates. This finding was confirmed by other studies [40][41][42][43][44][45][46][47][48][49][50][51][52][53]. Compared to previous influenza studies or known hypotheses, there are also inconsistencies in this study. ...
Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei’s influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza’s influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.
Background
The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).
Methodology
PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.
Findings
In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.
Interpretation
The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
The resurgence and outbreaks of mumps occur frequently in many countries worldwide in recent years, even in countries with high vaccination coverage. In this study, a descriptive and spatiotemporal clustering analysis at the township level was conducted to explore the dynamic spatiotemporal aggregation and epidemiological characteristics of mumps in Wuhan. During 2005 and 2019, there were 40 685 cases reported in Wuhan, with an average annual morbidity of 28.11 per 100 000 populations. The morbidity showed a fluctuating tendency, and peaked in 2010 and 2018. Bimodal seasonality was found, with a large peak between May and July, and a mild peak from November to January in the following year. Male students aged 5-9-year-old were the main risk group of mumps infection. Significant global spatial auto-correlation was detected except in 2007, 2009 and 2015. The spatial and temporal scan statistics indicated that the hot-spots mainly located at the western and southern areas of Wuhan with variations almost every year. Our findings could assist the public health authorities to develop and improve targeted health strategies, and allocate health resources rationally.
Background
Jiangsu Province is located in the Yangtze River Delta region, with a total area of 107,200 square kilometers. Since 1949, over 55,000 cases have been registered, with Taixing accounting for the highest number of patients. The proportion of new cases with MB and G2D was higher compared to other regions. As a result, Jiangsu has been considered a priority area for public health interventions in China.
Methods
This paper mainly described the population, time, and spatial distribution of the newly detected leprosy cases in Jiangsu Province between 2005 and 2020. In this study, all the data were entered into Microsoft Excel and SPSS for the descriptive analysis. ArcGIS was applied to create statistical maps, and Geoda was used to conduct spatial autocorrelation analysis with local Moran's I statistics (LISA). The epidemiological data were obtained from LEPMIS. In addition, population data were obtained from the Statistical Yearbook of Jiangsu Province.
Results
During the study period, 363 new cases were reported. Of these, 232 were men and 131 were women (1.77:1). The mean age at diagnosis was 60.56 years, and no adolescent cases were identified. Three hundred and twenty-seven (90.08%) were diagnosed with MB and 36 (9.92%) with PB. 31.68% (115/363) of the patients presented with G2D. Farmers accounted for 74.9%, and most cases were identified in skin clinics (248, 68.32%). We observed a decreasing trend in detection rate, with a higher concentration of new cases diagnosed between July and October. Spatial analysis showed that the new cases were primarily distributed in the northwest of Jiangsu province, and Suqian has the highest incidence of leprosy. Special attention should be paid to Wuzhong, a county with a potential risk of inter-provincial transmission. Furthermore, 55 new cases came from other Chinese provinces but lived in Jiangsu.
Conclusion
The NCDR of leprosy decreased, but the new cases showed disabilities, a sign of the late diagnosis. The results indicated that some regions were still suffering from the burden of leprosy. Thus, we recommend that the government should adopt effective strategies to promote leprosy control. The main priorities for eliminating new cases were to provide sustainable financial support, improve the quality of clinical services, strengthen preventive intervention and rehabilitation services for disabilities, provide health education among high-risk populations, and explore new approaches.
