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A Bayesian classification model for discriminating common infectious diseases in Zhejiang province, China.

ABSTRACT: To develop a classification model for accurately discriminating common infectious diseases in Zhejiang province, China.Symptoms and signs, abnormal lab test results, epidemiological features, as well as the incidence rates were treated as predictors, and were collected from the published literature and a national surveillance system of infectious disease. A classification model was established using naïve Bayesian classifier. Dataset from historical outbreaks was applied for model validation, while sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and M-index were presented.A total of 146 predictors were included in the classification model, for discriminating 25 common infectious diseases. The sensitivity ranged from 44.44% for hepatitis E to 96.67% for measles. The specificity varied from 96.36% for dengue fever to 100% for 5 diseases. The median of total accuracy was 97.41% (range: 93.85%-99.04%). The AUCs exceeded 0.98 in 11 of 12 diseases, except in dengue fever (0.613). The M-index was 0.960 (95%CI 0.941-0.978).A novel classification model was constructed based on Bayesian approach to discriminate common infectious diseases in Zhejiang province, China. After entering symptoms and signs, abnormal lab test results, epidemiological features and city of disease origin, an output list of possible diseases ranked according to the calculated probabilities can be provided. The discrimination performance was reasonably good, making it useful in epidemiological applications.

SUBMITTER: Li F

PROVIDER: S-EPMC7034623 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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A Bayesian classification model for discriminating common infectious diseases in Zhejiang province, China.

Li Fudong F Shen Yi Y Lv Duo D Lin Junfen J Liu Biyao B He Fan F Wang Zhen Z

Medicine 20200201 8

To develop a classification model for accurately discriminating common infectious diseases in Zhejiang province, China.Symptoms and signs, abnormal lab test results, epidemiological features, as well as the incidence rates were treated as predictors, and were collected from the published literature and a national surveillance system of infectious disease. A classification model was established using naïve Bayesian classifier. Dataset from historical outbreaks was applied for model validation, wh ...[more]

PMID: 32080115

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Project description:Background Despite significant progress in sanitation status and public health awareness, intestinal infectious diseases (IID) have caused a serious disease burden in China. Little was known about the spatio-temporal pattern of IID at the county level in Zhejiang. Therefore, a spatio-temporal modelling study to identify high-risk regions of IID incidence and potential risk factors was conducted. Methods Reported cases of notifiable IID from 2008 to 2021 were obtained from the China Information System for Disease Control and Prevention. Moran’s I index and the local indicators of spatial association (LISA) were calculated using Geoda software to identify the spatial autocorrelation and high-risk areas of IID incidence. Bayesian hierarchical model was used to explore socioeconomic and climate factors affecting IID incidence inequities from spatial and temporal perspectives. Results From 2008 to 2021, a total of 101 cholera, 55,298 bacterial dysentery, 131 amoebic dysentery, 5297 typhoid, 2102 paratyphoid, 27,947 HEV, 1,695,925 hand, foot and mouth disease (HFMD), and 1,505,797 other infectious diarrhea (OID) cases were reported in Zhejiang Province. The hot spots for bacterial dysentery, OID, and HEV incidence were found mainly in Hangzhou, while high-high cluster regions for incidence of enteric fever and HFMD were mainly located in Ningbo. The Bayesian model showed that Areas with a high proportion of males had a lower risk of BD and enteric fever. People under the age of 18 may have a higher risk of IID. High urbanization rate was a protective factor against HFMD (RR = 0.91, 95% CI: 0.88, 0.94), but was a risk factor for HEV (RR = 1.06, 95% CI: 1.01–1.10). BD risk (RR = 1.14, 95% CI: 1.10–1.18) and enteric fever risk (RR = 1.18, 95% CI:1.10–1.27) seemed higher in areas with high GDP per capita. The greater the population density, the higher the risk of BD (RR = 1.29, 95% CI: 1.23–1.36), enteric fever (RR = 1.12, 95% CI: 1.00–1.25), and HEV (RR = 1.15, 95% CI: 1.09–1.21). Among climate variables, higher temperature was associated with a higher risk of BD (RR = 1.32, 95% CI: 1.23–1.41), enteric fever (RR = 1.41, 95% CI: 1.33–1.50), and HFMD (RR = 1.22, 95% CI: 1.08–1.38), and with lower risk of HEV (RR = 0.83, 95% CI: 0.78–0.89). Precipitation was positively correlated with enteric fever (RR = 1.04, 95% CI: 1.00–1.08), HFMD (RR = 1.03, 95% CI: 1.00–1.06), and HEV (RR = 1.05, 95% CI: 1.03–1.08). Higher HFMD risk was also associated with increasing relative humidity (RR = 1.20, 95% CI: 1.16–1.24) and lower wind velocity (RR = 0.88, 95% CI: 0.84–0.92). Conclusions There was significant spatial clustering of IID incidence in Zhejiang Province from 2008 to 2021. Spatio-temporal patterns of IID risk could be largely explained by socioeconomic and meteorological factors. Preventive measures and enhanced monitoring should be taken in some high-risk counties in Hangzhou city and Ningbo city. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-16552-4.

Project description:BackgroundDengue is one of the most important vector-bore infectious diseases in China because of its drastic increase in incidence, geographic extension and profound influence on China's economy. This study aims to retrospectively uncover the epidemiological profile of dengue in Zhejiang, one of the most developed provinces in China, and to find the problem existing in dengue control and prevention.MethodologyDescriptive analyses on the dengue incidence and associated factors were performed. We also identified potential space-time cluster and generated the risk map of dengue.Principal findingsA total of 529 cases were reported in Zhejiang Province from 2005 to 2016, and 44.4% were imported. 67.7% of cases were 25~60 years old and the overall male-to-female sex ratio was 1.09:1. Dengue was reported all year round and 70.7% of cases occurred between August and October. Indigenous cases were only reported in the period between July to November and more than half occurred in September. Geographically, dengue was most distributed in Jinghua (3.62 per million), Shaoxing (1.00 per million) and Taizhou (0.81 per million) prefecture level cities. Outbreaks were confirmed in Yiwu, Keqiao and Huangyan counties in 2009, 2015, and 2016, respectively. 73.9% cases would seek medical advice within two days after onset and be confirmed within 9 days after onset. 75.6% would be recognized as dengue within 8 days after their first visit. The time intervals between onset and confirmation (median 7 vs 6 days; Wilcoxon rank sum test Z = -2.40, P = 0.016), first visit and confirmation (median 7 vs 6 days; Wilcoxon rank sum test Z = -2.59, P = 0.009) of indigenous cases were significantly longer than those of imported ones. However, the time intervals between onset and first visit for indigenous cases was shorter (median 0 vs 1 days; Wilcoxon rank sum test Z = -2.10, P = 0.036). Fever (99.1%), fatigue (81.9), rash (63.7%), headache (67.2%) and myalgia (52.60%) were the most frequently mentioned symptoms.ConclusionsZhejiang has recently witnessed an increase in incidence and geographic extension of dengue. Timely diagnosis is important to stop local transmission and outbreak.

Project description:BACKGROUND:The eastern and western regions of China are different in many ways such as socioeconomic characteristics and health resource distribution. This study aimed to explore the outpatient health-seeking behavior and compare the influencing factors of residents in Zhejiang and Qinghai Province, which represent the situation in eastern and western China. Thus, this research will provide evidence for health resource allocation and health reform. METHODS:A cross-sectional study was conducted on a sample selected from 1600 households in Zhejiang and Qinghai province between 2016 to 2017 by the multi-stage stratified cluster random sampling method. Among the 4231 residents aged 15 years or older in the sample, 566 who reported ill-health were selected for data analysis. Two-week outpatient visits and choice of health institutions were used to measure residents' outpatient health-seeking behavior and assessed using Chi-square tests. The binary logistic regression was adopted to demonstrate the association between explanatory variables and outpatient visits. RESULTS:The study revealed that out of the people who reported ill-health, 58 individuals (50.97%) in Zhejiang and 106 (41.41%) in Qinghai went to health institutions to seek medical help (p < 0.05). The difference of residents' choice of health institution between Zhejiang and Qinghai was not statistically significant (p > 0.05). Among these respondents, Self-report severity was the common and significant factor related to their outpatient visits and it had a greater impact on outpatient visits in Zhejiang (4.18, CI 2.23-7.83, p < 0.05). Other factors such as chronic disease, knowledge of medicine and doctors and distance to the nearest health institution were significant influencing factors in Zhejiang, while in Qinghai it was occupation. CONCLUSIONS:The outpatient health-seeking behavior and its influencing factors among residents in Zhejiang and Qinghai province were different. The findings suggest the importance of having discrepant health policies in the two provinces. It's necessary to improve health literacy of residents in both provinces, strengthen the accessibility of health services in remote areas of Zhejiang and pay more attention to people with low socioeconomic status in Qinghai.

Dataset Information

A Bayesian classification model for discriminating common infectious diseases in Zhejiang province, China.

Publications

A Bayesian classification model for discriminating common infectious diseases in Zhejiang province, China.

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