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Guo Z, Wang Y, Li Y, Zhou L. Impact of meteorological factors on the incidence of hand-foot-mouth disease in Yangzhou from 2017 to 2022: a time series study. Front Public Health 2023; 11:1278516. [PMID: 37881347 PMCID: PMC10597706 DOI: 10.3389/fpubh.2023.1278516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/26/2023] [Indexed: 10/27/2023] Open
Abstract
Background Hand, foot, and mouth disease (HFMD) is a significant public health issue in China, and numerous studies have indicated a close association between HFMD incidence and meteorological factors. This study aims to investigate the relationship between meteorological factors and HFMD in Yangzhou City, Jiangsu Province, China. Methods HFMD case reports and meteorological data from Yangzhou City between 2017 and 2022 were extracted from the National Notifiable Infectious Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) was employed to assess the exposure-response relationship between meteorological factors and HFMD. Subsequently, a distributed lag nonlinear model (DLNM) was used to explore the exposure-lag-effect of meteorological factors on HFMD. Results HFMD in Yangzhou City exhibits obvious seasonality and periodicity. There is an inverted "U" shaped relationship between average temperature and the risk of HFMD, with the maximum lag effect observed at a temperature of 25°C with lag 0 day (RR = 2.07, 95% CI: 1.74-2.47). As the duration of sunshine and relative humidity increase, the risk of HFMD continuously rises, with the maximum lag effect observed at a sunshine duration of 12.4 h with a lag of 14 days (RR = 2.10, 95% CI: 1.17-3.77), and a relative humidity of 28% with a lag of 14 days (RR = 1.21, 95% CI: 1.01-1.64). There is a "U" shaped relationship between average atmospheric pressure and the risk of HFMD, with the maximum effect observed at an atmospheric pressure of 989 hPa with no lag (RR = 1.45, 95% CI: 1.25-1.69). As precipitation increases, the risk of HFMD decreases, with the maximum effect observed at a precipitation of 151 mm with a lag of 14 days (RR = 1.45, 95% CI: 1.19-2.53). Conclusion Meteorological factors including average temperature, average atmospheric pressure, relative humidity, precipitation, and sunshine duration significantly influenced the risk of HFMD in Yangzhou City. Effective prevention measures for HFMD should be implemented, taking into account the local climate conditions.
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Affiliation(s)
- Zaijin Guo
- Clinical Medical College, Yangzhou University, Yangzhou, China
- Northern Jiangsu People’s Hospital, Yangzhou, China
| | - Yin Wang
- Department of Acute Infectious Disease Control and Prevention, Yangzhou Centre for Disease Control and Prevention, Yangzhou, China
| | - Yunshui Li
- Clinical Medical College, Yangzhou University, Yangzhou, China
- Northern Jiangsu People’s Hospital, Yangzhou, China
| | - Luojing Zhou
- Clinical Medical College, Yangzhou University, Yangzhou, China
- Northern Jiangsu People’s Hospital, Yangzhou, China
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Zhao D, Zhang H, Zhang R, He S. Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China. BMC Public Health 2023; 23:619. [PMID: 37003988 PMCID: PMC10064964 DOI: 10.1186/s12889-023-15543-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease (HFMD) in mainland China from January 2008 to December 2019 and to provide a reference for the surveillance and early warning of HFMD. METHODS We collected data on the incidence of HFMD in mainland China between January 2008 and December 2019. The SARIMA, SARIMA-BPNN, and SARIMA-PSO-BPNN hybrid models were used to predict the incidence of HFMD. The prediction performance was compared using the mean absolute error(MAE), mean squared error(MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation analysis. RESULTS The incidence of HFMD in mainland China from January 2008 to December 2019 showed fluctuating downward trends with clear seasonality and periodicity. The optimal SARIMA model was SARIMA(1,0,1)(2,1,2)[12], with Akaike information criterion (AIC) and Bayesian Schwarz information criterion (BIC) values of this model were 638.72, 661.02, respectively. The optimal SARIMA-BPNN hybrid model was a 3-layer BPNN neural network with nodes of 1, 10, and 1 in the input, hidden, and output layers, and the R-squared, MAE, and RMSE values were 0.78, 3.30, and 4.15, respectively. For the optimal SARIMA-PSO-BPNN hybrid model, the number of particles is 10, the acceleration coefficients c1 and c2 are both 1, the inertia weight is 1, the probability of change is 0.95, and the values of R-squared, MAE, and RMSE are 0.86, 2.89, and 3.57, respectively. CONCLUSIONS Compared with the SARIMA and SARIMA-BPNN hybrid models, the SARIMA-PSO-BPNN model can effectively forecast the change in observed HFMD incidence, which can serve as a reference for the prevention and control of HFMD.
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Affiliation(s)
- Daren Zhao
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, People's Republic of China
| | - Huiwu Zhang
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, People's Republic of China.
| | - Ruihua Zhang
- School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, People's Republic of China.
- General Practitioners Training Center of Sichuan Province, Chengdu, Sichuan, People's Republic of China.
| | - Sizhang He
- Department of Information and Statistics, The Affiliated Hospital of Southwest Medical University, Luzhou, 64600, Sichuan, China
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Yang C, An S, Qiao B, Guan P, Huang D, Wu W. Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:20369-20385. [PMID: 36255582 PMCID: PMC9579594 DOI: 10.1007/s11356-022-23643-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.
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Affiliation(s)
- Chuan Yang
- Department of Mathematics, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning China
| | - Shuyi An
- Liaoning Provincial Center for Disease Control and Prevention, Shenyang, Liaoning China
| | - Baojun Qiao
- Liaoning Provincial Center for Disease Control and Prevention, Shenyang, Liaoning China
| | - Peng Guan
- Department of Mathematics, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning China
| | - Desheng Huang
- Department of Intelligent Computing, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning China
| | - Wei Wu
- Department of Mathematics, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning China
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Meng L, Zhou C, Xu Y, Liu F, Zhou C, Yao M, Li X. The lagged effect and attributable risk of apparent temperature on hand, foot, and mouth disease in Changsha, China: a distributed lag non-linear model. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:11504-11515. [PMID: 36094702 DOI: 10.1007/s11356-022-22875-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/31/2022] [Indexed: 06/15/2023]
Abstract
Hand, foot, and mouth disease (HFMD) is the leading Category C infectious disease affecting millions of children in China every year. In the context of global climate change, the understanding and quantification of the impact of weather factors on human health are particularly critical to the development and implementation of climate change adaptation and mitigation strategies. The aim of this study was to quantify the attributable burden of a combined bioclimatic indicator (apparent temperature) on HFMD and to identify temperature-specific sensitive populations. A total of 123,622 HFMD cases were included in the study. The non-linear relationship between apparent temperature and the incidence of HFMD was approximately M-shaped, with hot weather being more likely to be attributable than cold conditions, of which moderately hot accounting for the majority of cases (21,441, 17.34%). Taking the median apparent temperature (19.2 °C) as reference, the cold effect showed a short acute effect with the highest risk on the day of lag 0 (RR = 1.086, 95% CI: 1.024 ~ 1.152), whereas the hot effect lasted longer with the greatest risk at a lag of 7 days (RR = 1.081, 95% CI: 1.059 ~ 1.104). Subgroup analysis revealed that males, children under 3 years old, and scattered children tended to be more vulnerable to HFMD in hot weather, while females, those aged 3 ~ 5 years, and nursery children were sensitive to cold conditions. This study suggests that high temperatures have a greater impact on HFMD than low temperatures as well as lasting longer, of particular concern being moderately high temperatures rather than extreme temperatures. Early intervention takes on greater importance during cold days, while the duration of HFMD intervention must be longer during hot days.
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Affiliation(s)
- Lijun Meng
- Department of Epidemiology and Health Statistics, Xiang Ya School of Public Health, Central South University, Changsha, 410078, Hunan, China
| | - Chunliang Zhou
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, Hunan, China
| | - Yiqing Xu
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, Hunan, China
| | - Fuqiang Liu
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, Hunan, China
| | - Cui Zhou
- Department of Epidemiology and Health Statistics, Xiang Ya School of Public Health, Central South University, Changsha, 410078, Hunan, China
| | - Meng Yao
- Department of Epidemiology and Health Statistics, Xiang Ya School of Public Health, Central South University, Changsha, 410078, Hunan, China
| | - Xingli Li
- Department of Epidemiology and Health Statistics, Xiang Ya School of Public Health, Central South University, Changsha, 410078, Hunan, China.
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Yang Z, Rui J, Qi L, Ye W, Niu Y, Luo K, Deng B, Zhang S, Yu S, Liu C, Li P, Wang R, Wei H, Zhang H, Huang L, Zuo S, Zhang L, Zhang S, Yang S, Guo Y, Zhao Q, Wu S, Li Q, Chen Y, Chen T. Study on the interaction between different pathogens of Hand, foot and mouth disease in five regions of China. Front Public Health 2022; 10:970880. [PMID: 36238254 PMCID: PMC9552780 DOI: 10.3389/fpubh.2022.970880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/22/2022] [Indexed: 01/25/2023] Open
Abstract
Objectives This study aims to explore the interaction of different pathogens in Hand, foot and mouth disease (HFMD) by using a mathematical epidemiological model and the reported data in five regions of China. Methods A cross-regional dataset of reported HFMD cases was built from four provinces (Fujian Province, Jiangsu province, Hunan Province, and Jilin Province) and one municipality (Chongqing Municipality) in China. The subtypes of the pathogens of HFMD, including Coxsackievirus A16 (CV-A16), enteroviruses A71 (EV-A71), and other enteroviruses (Others), were included in the data. A mathematical model was developed to fit the data. The effective reproduction number (R eff ) was calculated to quantify the transmissibility of the pathogens. Results In total, 3,336,482 HFMD cases were collected in the five regions. In Fujian Province, the R eff between CV-A16 and EV-A71&CV-A16, and between CV-A16 and CV-A16&Others showed statistically significant differences (P < 0.05). In Jiangsu Province, there was a significant difference in R eff (P < 0.05) between the CV-A16 and Total. In Hunan Province, the R eff between CV-A16 and EV-A71&CV-A16, between CV-A16 and Total were significant (P < 0.05). In Chongqing Municipality, we found significant differences of the R eff (P < 0.05) between CV-A16 and CV-A16&Others, and between Others and CV-A16&Others. In Jilin Province, significant differences of the R eff (P < 0.05) were found between EV-A71 and Total, and between Others and Total. Conclusion The major pathogens of HFMD have changed annually, and the incidence of HFMD caused by others and CV-A16 has surpassed that of EV-A71 in recent years. Cross-regional differences were observed in the interactions between the pathogens.
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Affiliation(s)
- Zimei Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Li Qi
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
| | - Wenjing Ye
- Fujian Center for Disease Control and Prevention, Fuzhou, Fujian, China
| | - Yan Niu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Kaiwei Luo
- Hunan Center for Disease Control and Prevention, Changsha, Hunan, China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Shi Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Shanshan Yu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Chan Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Peihua Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Rui Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Hongjie Wei
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Hesong Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Lijin Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Simiao Zuo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Lexin Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Shurui Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Shiting Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Yichao Guo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Qinglong Zhao
- Jilin Center for Disease Control and Prevention, Changchun, Jilin, China
| | - Shenggen Wu
- Fujian Center for Disease Control and Prevention, Fuzhou, Fujian, China,Shenggen Wu
| | - Qin Li
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China,Qin Li
| | - Yong Chen
- Department of Stomatology, School of Medicine, Xiamen University, Xiamen, China,Yong Chen
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China,*Correspondence: Tianmu Chen
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6
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Lin S, Rui J, Xie F, Zhan M, Chen Q, Zhao B, Zhu Y, Li Z, Deng B, Yu S, Li A, Ke Y, Zeng W, Su Y, Chiang YC, Chen T. Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations. Front Public Health 2022; 10:920312. [PMID: 35844849 PMCID: PMC9284004 DOI: 10.3389/fpubh.2022.920312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Meteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases. Methods In this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (Reff ). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables. Results Precipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases. Conclusion Meteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.
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Affiliation(s)
- Shengnan Lin
- School of Public Health, Xiamen University, Xiamen, China
| | - Jia Rui
- School of Public Health, Xiamen University, Xiamen, China
- Cirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, France
| | - Fang Xie
- School of Public Health, Xiamen University, Xiamen, China
| | - Meirong Zhan
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Qiuping Chen
- School of Public Health, Xiamen University, Xiamen, China
- Cirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, France
| | - Bin Zhao
- Clinical Medical Laboratory, Xiang'an Hospital of Xiamen University, Xiamen, China
| | - Yuanzhao Zhu
- School of Public Health, Xiamen University, Xiamen, China
| | - Zhuoyang Li
- School of Public Health, Xiamen University, Xiamen, China
| | - Bin Deng
- School of Public Health, Xiamen University, Xiamen, China
| | - Shanshan Yu
- School of Public Health, Xiamen University, Xiamen, China
| | - An Li
- School of Public Health, Xiamen University, Xiamen, China
| | - Yanshu Ke
- School of Public Health, Xiamen University, Xiamen, China
| | - Wenwen Zeng
- School of Public Health, Xiamen University, Xiamen, China
| | - Yanhua Su
- School of Public Health, Xiamen University, Xiamen, China
| | - Yi-Chen Chiang
- School of Public Health, Xiamen University, Xiamen, China
| | - Tianmu Chen
- School of Public Health, Xiamen University, Xiamen, China
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Deng B, Rui J, Liang SY, Li ZF, Li K, Lin S, Luo L, Xu J, Liu W, Huang J, Wei H, Yang T, Liu C, Li Z, Li P, Zhao Z, Wang Y, Yang M, Zhu Y, Liu X, Zhang N, Cheng XQ, Wang XC, Hu JL, Chen T. Meteorological factors and tick density affect the dynamics of SFTS in jiangsu province, China. PLoS Negl Trop Dis 2022; 16:e0010432. [PMID: 35533208 PMCID: PMC9119627 DOI: 10.1371/journal.pntd.0010432] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 05/19/2022] [Accepted: 04/19/2022] [Indexed: 11/18/2022] Open
Abstract
Background This study aimed to explore whether the transmission routes of severe fever with thrombocytopenia syndrome (SFTS) will be affected by tick density and meteorological factors, and to explore the factors that affect the transmission of SFTS. We used the transmission dynamics model to calculate the transmission rate coefficients of different transmission routes of SFTS, and used the generalized additive model to uncover how meteorological factors and tick density affect the spread of SFTS. Methods In this study, the time-varying infection rate coefficients of different transmission routes of SFTS in Jiangsu Province from 2017 to 2020 were calculated based on the previous multi-population multi-route dynamic model (MMDM) of SFTS. The changes in transmission routes were summarized by collecting questionnaires from 537 SFTS cases in 2018–2020 in Jiangsu Province. The incidence rate of SFTS and the infection rate coefficients of different transmission routes were dependent variables, and month, meteorological factors and tick density were independent variables to establish a generalized additive model (GAM). The optimal GAM was selected using the generalized cross-validation score (GCV), and the model was validated by the 2016 data of Zhejiang Province and 2020 data of Jiangsu Province. The validated GAMs were used to predict the incidence and infection rate coefficients of SFTS in Jiangsu province in 2021, and also to predict the effect of extreme weather on SFTS. Results The number and proportion of infections by different transmission routes for each year and found that tick-to-human and human-to-human infections decreased yearly, but infections through animal and environmental transmission were gradually increasing. MMDM fitted well with the three-year SFTS incidence data (P<0.05). The best intervention to reduce the incidence of SFTS is to reduce the effective exposure of the population to the surroundings. Based on correlation tests, tick density was positively correlated with air temperature, wind speed, and sunshine duration. The best GAM was a model with tick transmissibility to humans as the dependent variable, without considering lagged effects (GCV = 5.9247E-22, R2 = 96%). Reported incidence increased when sunshine duration was higher than 11 h per day and decreased when temperatures were too high (>28°C). Sunshine duration and temperature had the greatest effect on transmission from host animals to humans. The effect of extreme weather conditions on SFTS was short-term, but there was no effect on SFTS after high temperature and sunshine hours. Conclusions Different factors affect the infection rate coefficients of different transmission routes. Sunshine duration, relative humidity, temperature and tick density are important factors affecting the occurrence of SFTS. Hurricanes reduce the incidence of SFTS in the short term, but have little effect in the long term. The most effective intervention to reduce the incidence of SFTS is to reduce population exposure to high-risk environments. Severe fever with thrombocytopenia syndrome (SFTS) is an emerging vector-borne disease caused by SFTS virus. After the first case was detected in China in 2009, SFTS endemic areas have gradually increased, with more than 23 provinces and cities reporting SFTS cases. In order to explore the transmission mechanism of SFTS and explain the impact of meteorological factors and tick density on the transmission routes of SFTS, this study collected SFTS cases data, meteorological data and tick surveillance data in Jiangsu Province from 2017 to 2019 to investigate the study question. The multi-population and multi-route dynamic model established in the previous study was used to calculate the infection rate coefficients of various transmission routes of SFTS in Jiangsu Province, and the generalized additive model was established to further elaborate the influence of SFTS transmission mechanism.
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Affiliation(s)
- Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Shu-yi Liang
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, People’s Republic of China
| | - Zhi-feng Li
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, People’s Republic of China
| | - Kangguo Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Jiefeng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Hongjie Wei
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Tianlong Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Chan Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Zhuoyang Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Peihua Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Yuanzhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Xingchun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
| | - Nan Zhang
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, People’s Republic of China
| | - Xiao-qing Cheng
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, People’s Republic of China
| | - Xiao-chen Wang
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, People’s Republic of China
| | - Jian-li Hu
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, People’s Republic of China
- * E-mail: (JlH); (TC)
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People’s Republic of China
- * E-mail: (JlH); (TC)
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Analysis of the Complete Genomes of Enterovirus 71 Subtypes in China. CANADIAN JOURNAL OF INFECTIOUS DISEASES & MEDICAL MICROBIOLOGY 2021; 2021:5564099. [PMID: 34484496 PMCID: PMC8416384 DOI: 10.1155/2021/5564099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 08/02/2021] [Accepted: 08/17/2021] [Indexed: 11/18/2022]
Abstract
Enterovirus 71 (EV-A71) is one of the most pathogens to hand, foot, and mouth disease (HFMD) as well as neurological complications in young children. Molecular characteristic of EV-A71 is important to prevent the virus outbreak. Here, the complete genomes of EV-A71 from China between 1998 and 2019 were downloaded from GenBank. The phylogenetic trees were developed by MEGA7.0 software, and the complete genetic epidemiological characteristics and amino acid mutations of EV-A71 from China were also analysed. The results showed that major epidemic EV-A71 subtype was C4b before 2004, while it turned to C4a after 2004 in mainland China, and C4 and B5 were major subtypes in Taiwan. VP1, VP4, 2C, 3C, 3D, and complete genome sequence can be used for virus genotyping, and VP1, VP4, and complete genomes have obvious advantages over other segments. There were many significant mutations in the viral complete genome sequence. This study indicated that the major C4 and B5 subtypes will contribute to the development of vaccines and drugs of EV-A71 for prevention and monitoring of EV-A71-associated HFMD in China.
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Control measures during the COVID-19 outbreak reduced the transmission of hand, foot, and mouth disease. JOURNAL OF SAFETY SCIENCE AND RESILIENCE 2021; 2. [PMCID: PMC8194009 DOI: 10.1016/j.jnlssr.2021.06.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Control measures during the coronavirus disease 2019 (COVID-19) outbreak may have limited the spread of infectious diseases. This study aimed to analyze the impact of COVID-19 on the spread of hand, foot, and mouth disease (HFMD) in China. A mathematical model was established to fit the reported data of HFMD in six selected cities in mainland China from 2015 to 2020. The absolute difference (AD) and relative difference (RD) between the reported incidence in 2020, and simulated maximum, minimum, or median incidence of HFMD in 2015–2019 were calculated. The incidence and Reff of HFMD have decreased in six selected cities since the outbreak of COVID-19, and in the second half of 2020, the incidence and Reff of HFMD have rebounded. The results show that the total attack rate (TAR) in 2020 was lower than the maximum, minimum, and median TAR fitted in previous years in six selected cities (except Changsha City). For the maximum, median, minimum fitted TAR, the range of RD (%) is 42·20–99·20%, 36·35–98·41% 48·35–96·23% (except Changsha City) respectively. The preventive and control measures of COVID-19 have significantly contributed to the containment of HFMD transmission.
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