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Fu X, Long J, Xiong Y, Li Z, Yang J, Tian D, Li Z, Yang S, Qi L. Epidemic patterns of the different influenza virus types and subtypes/lineages for 10 years in Chongqing, China, 2010-2019. Hum Vaccin Immunother 2024; 20:2363076. [PMID: 38847280 PMCID: PMC11164227 DOI: 10.1080/21645515.2024.2363076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 05/30/2024] [Indexed: 06/12/2024] Open
Abstract
To optimize seasonal influenza control and prevention programs in regions with potentially complicated seasonal patterns. Descriptive epidemiology was used to analyze the etiology of influenza, and chi-square tests were used to compare the epidemic patterns among different influenza virus types and subtypes/lineages. From January 2010 to December 2019, a total of 63,626 ILI cases were reported in Chongqing and 14,136 (22.22%) were laboratory-confirmed influenza cases. The proportions of specimens positive for influenza A and influenza B were 13.32% (8,478/63,626) and 8.86% (5,639/63,626), respectively. The proportion of positive specimens for influenza A reached the highest in winter (23.33%), while the proportion of positive specimens for influenza B reached the highest in spring (11.88%). Children aged 5-14 years old had the highest proportion of positive specimens for influenza. The influenza virus types/subtypes positive was significantly different by seasons and age groups (P<.001), but not by gender (p = .436). The vaccine strains were matched to the circulating influenza virus strains in all other years except for 2018 (vaccine strain was B/Colorado/06/2017; circulating strain was B/Yamagata). The study showed significant variations in epidemic patterns, including seasonal epidemic period and age distributions, among different influenza types, subtypes/lineages in Chongqing. Influenza vaccines matched to the circulating influenza virus strain in nine of the ten years. To prevent and mitigate the influenza outbreaks in this area, high risk population, especially children aged 5-14 years, are encouraged to get vaccinated against influenza before the epidemic seasons.
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Affiliation(s)
- Xiaoqing Fu
- Infectious Disease Prevention and Control, Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
- School of Public Health, Southwest Medical University, Luzhou, Sichuan, China
| | - Jiang Long
- Infectious Disease Prevention and Control, Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
- Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing, China
| | - Yu Xiong
- Infectious Disease Prevention and Control, Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
- Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing, China
| | - Zhifeng Li
- Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing, China
- Microbiological examination, Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
| | - Jule Yang
- Infectious Disease Prevention and Control, Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
- Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing, China
| | - Dechao Tian
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Zhourong Li
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Shuang Yang
- School of Public Health, Chongqing Medical University, Chongqing, China
| | - Li Qi
- Infectious Disease Prevention and Control, Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
- School of Public Health, Southwest Medical University, Luzhou, Sichuan, China
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Ye L, Chen J, Mei Q, Sun Y, Yang T. The impact of the COVID-19 pandemic and the free vaccination policy on seasonal influenza vaccination uptake among older adults in Ningbo, Eastern China. Hum Vaccin Immunother 2024; 20:2370999. [PMID: 38957901 PMCID: PMC11225915 DOI: 10.1080/21645515.2024.2370999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 06/18/2024] [Indexed: 07/04/2024] Open
Abstract
In 2020-21, during the COVID-19 pandemic, a free influenza vaccination program was initiated among the elderly residents in Ningbo, China. The impact of the COVID-19 pandemic and free vaccination policy on influenza vaccine uptake needs to be evaluated. The influenza vaccine uptake among individuals born before 31 December, 1962 from 2017-18 to 2022-23 season in Ningbo was analyzed. Multivariate logistic regressions were used to estimate the impact of the COVID-19 pandemic and free vaccination policy. Our analysis included an average of 1,856,565 individuals each year. Influenza vaccination coverage increased from 1.14% in 2017-18 to 33.41% in 2022-23. The vaccination coverage among the free policy target population was 50.03% in 2022-23. Multivariate analysis showed that free vaccination policy increased influenza vaccine uptake most (OR = 11.99, 95%CI: 11.87-12.11). The initial phase of the pandemic was associated with a positive effect on influenza vaccination (OR = 2.09, 95%CI: 2.07-2.12), but followed by a negative effect in the subsequent two seasons(2021-22: OR = 0.75, 95%CI: 0.73-0.76; 2022-23: OR = 0.40, 95%CI: 0.39-0.40). COVID-19 vaccination in the current season was a positive predictor of influenza vaccine uptake while not completing booster COVID-19 vaccination before was negative predictor in 2022-23. Having influenza vaccine history and having ILI medical history during the last season were also positive predictors of influenza vaccine uptake. Free vaccination policies have enhanced influenza vaccination coverage among elderly population. The COVID-19 pandemic plays different roles in different seasons. Our study highlights the need for how to implement free vaccination policies targeting vulnerable groups with low vaccination coverage.
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Affiliation(s)
- Lixia Ye
- Department of Immunization Program, Ningbo Municipal Center for Disease Prevention and Control, Ningbo, China
| | - Jieping Chen
- Department of Non-communicable Chronic Diseases Prevention and Control, Ningbo Municipal Center for Disease Prevention and Control, Ningbo, China
| | - Qiuhong Mei
- Department of Immunization Program, Ningbo Municipal Center for Disease Prevention and Control, Ningbo, China
| | - Yexiang Sun
- Institute of Big Data, Yinzhou District Center for Disease Prevention and Control, Ningbo, China
| | - Tianchi Yang
- Department of Immunization Program, Ningbo Municipal Center for Disease Prevention and Control, Ningbo, China
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Liu Y, Liu T, Yao M, Wang Q, Li R, Kou Z. Coverage of influenza vaccination and influencing factors among healthcare workers in Shandong Province, China, 2021-2022. Hum Vaccin Immunother 2024; 20:2402116. [PMID: 39279572 PMCID: PMC11407383 DOI: 10.1080/21645515.2024.2402116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/18/2024] Open
Abstract
Healthcare workers (HCWs) are at increased risk of exposure to the influenza virus in their daily clinical and disease prevention activities, making them a high-risk group for influenza infection. However, the vaccination rate among HCWs has always been low. This study investigated influenza vaccination uptake and willingness among HCWs in the context of the COVID-19 pandemic. The analysis revealed that the influenza vaccination uptake among HCWs was 67.5%, with 79.6% willing to receive the influenza vaccine in 2022/2023 A significant majority (92.7%) agreed that the COVID-19 pandemic increased their willingness to receive the influenza vaccine, and 94.8% agreed with the necessity of receiving the influenza vaccine even after COVID-19 vaccination. Binary logistic regression model identified key factors that influence vaccination intentions. HCWs who perceived a high risk of influenza and its threat to health, found vaccination convenient, and believed in the safety of the influenza vaccine were more likely to be vaccinated. Conversely, the high price of the influenza vaccine was a barrier, whereas those who considered the vaccine affordable were more likely to be vaccinated. Although Changchun Changsheng vaccine incident (The Changchun Changsheng Biotechnology Company was found to have violated good manufacturing practices in 2018, leading to widespread distribution of subpotent vaccines in China.) may not significantly impact the vaccination uptake among healthcare workers, some HCWs still harbor doubts about vaccine safety, which remains a key reason for vaccine hesitancy. This study emphasizes the importance of the strict monitoring and management of vaccines, conducting clinical studies to support vaccine safety, and implementing free influenza vaccine policies, workplace vaccination requirements, and organized mass vaccinations. Educational efforts to increase HCWs' understanding of influenza and influenza vaccines are crucial to increasing vaccination uptake. Furthermore, implementing comprehensive intervention measures is essential to effectively improve the influenza vaccination uptake.
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Affiliation(s)
- Yuwei Liu
- College of Public Health, Shandong Second Medical University, Shandong, China
| | - Ti Liu
- Shandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Prevention and Control, Jinan, China
| | - Mingxiao Yao
- Shandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Prevention and Control, Jinan, China
| | - Qiang Wang
- College of Public Health, Shandong Second Medical University, Shandong, China
| | - Renpeng Li
- Shandong Provincial Center for Health Science & Technology and Talents Development, Shandong, China
| | - Zengqiang Kou
- Shandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Prevention and Control, Jinan, China
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Yin Z, Dong Y, Wang Q, Ma Y, Gao Z, Ling Z, Aihaiti X, Abudusaimaiti X, Qiu R, Chen Z, Wushouer F. Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS. Sci Rep 2024; 14:21496. [PMID: 39277661 PMCID: PMC11401927 DOI: 10.1038/s41598-024-72618-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 09/09/2024] [Indexed: 09/17/2024] Open
Abstract
Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran's I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.
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Affiliation(s)
- Zhe Yin
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Yan Dong
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Qi Wang
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Yuanyuan Ma
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Zhenguo Gao
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Zhang Ling
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Xiapikatijiang Aihaiti
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Xiayidanmu Abudusaimaiti
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Ruiying Qiu
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Zihan Chen
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China
| | - Fuerhati Wushouer
- Xinjiang Uygur Autonomous Region Center for Disease Control and Prevention, No. 380, Jianquan First Street, Tianshan District, Ürümqi, 830002, Xinjiang Uygur Autonomous Region, China.
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Qiao M, Zhu F, Chen J, Li Y, Wang X. Effects of scheduled school breaks on the circulation of influenza in children, school-aged population, and adults in China: A spatio-temporal analysis. Int J Infect Dis 2024; 140:78-85. [PMID: 38218380 DOI: 10.1016/j.ijid.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/27/2023] [Accepted: 01/08/2024] [Indexed: 01/15/2024] Open
Abstract
OBJECTIVES To investigate the effect of scheduled school break on the circulation of influenza in young children, school-aged population, and adults. METHODS In a spatial-temporal analysis using influenza activity, school break dates, and meteorological covariates across mainland China during 2015-2018, we estimated age-specific, province-specific, and overall relative risk (RR) and effectiveness of school break on influenza. RESULTS We included data in 24, 25, and 17 provinces for individuals aged 0-4 years, 5-19 years and 20+ years. We estimated a RR meta-estimate of 0.34 (95% confidence interval 0.29-0.40) and an effectiveness of 66% for school break in those aged 5-19 years. School break showed a lagged and smaller mitigation effect in those aged 0-4 years (RR meta-estimate: 0.73, 0.68-0.79) and 20+ years (RR meta-estimate: 0.89, 0.78-1.01) versus those aged 5-19 years. CONCLUSION The results show heterogeneous effects of school break between population subgroups, a pattern likely to hold for other respiratory infectious diseases. Our study highlights the importance of anticipating age-specific effects of implementing school closure interventions and provides evidence for rational use of school closure interventions in future epidemics.
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Affiliation(s)
- Mengling Qiao
- Department of Biostatistics, National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Fuyu Zhu
- Department of Biostatistics, National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Junru Chen
- Department of Biostatistics, National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - You Li
- Department of Epidemiology, National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China; Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom
| | - Xin Wang
- Department of Biostatistics, National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China; Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom.
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Zhang L, Li Y, Ma N, Zhao Y, Zhao Y. Heterogeneity of influenza infection at precise scale in Yinchuan, Northwest China, 2012-2022: evidence from Joinpoint regression and spatiotemporal analysis. Sci Rep 2024; 14:3079. [PMID: 38321190 PMCID: PMC10847441 DOI: 10.1038/s41598-024-53767-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 02/05/2024] [Indexed: 02/08/2024] Open
Abstract
Identifying high-risk regions and turning points of influenza with a precise spatiotemporal scale may provide effective prevention strategies. In this study, epidemiological characteristics and spatiotemporal clustering analysis at the township level were performed. A descriptive study and a Joinpoint regression analysis were used to explore the epidemiological characteristics and the time trend of influenza. Spatiotemporal autocorrelation and clustering analyses were carried out to explore the spatiotemporal distribution characteristics and aggregation. Furthermore, the hotspot regions were analyzed by spatiotemporal scan analysis. A total of 4025 influenza cases were reported in Yinchuan showing an overall increasing trend. The tendency of influenza in Yinchuan consisted of three stages: increased from 2012 to the first peak in 2019 (32.62/100,000) with a slight decrease in 2016; during 2019 and 2020, the trend was downwards; then it increased sharply again and reached another peak in 2022. The Joinpoint regression analysis found that there were three turning points from January 2012 to December 2022, namely January 2020, April 2020, and February 2022. The children under ten displayed an upward trend and were statistically significant. The trend surface analysis indicated that there was a shifting trend from northern to central and southern. A significant positive spatial auto-correlation was observed at the township level and four high-incidence clusters of influenza were detected. These results suggested that children under 10 years old deserve more attention and the spatiotemporal distribution of high-risk regions of influenza in Yinchuan varies every year at the township level. Thus, more monitoring and resource allocation should be prone to the four high-incidence clusters, which may benefit the public health authorities to carry out the vaccination and health promotion timely.
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Affiliation(s)
- Lu Zhang
- School of Public Health, Ningxia Medical University, Yinchuan, 750004, Ningxia, China
- Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, 750004, Ningxia, China
| | - Yan Li
- Yinchuan Center for Diseases Prevention and Control, Yinchuan, 750004, Ningxia, China
| | - Ning Ma
- School of Public Health, Ningxia Medical University, Yinchuan, 750004, Ningxia, China
- Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, 750004, Ningxia, China
| | - Yi Zhao
- School of Public Health, Ningxia Medical University, Yinchuan, 750004, Ningxia, China
- Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, 750004, Ningxia, China
| | - Yu Zhao
- School of Public Health, Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
- Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, 750004, Ningxia, China.
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Si X, Wang L, Mengersen K, Hu W. Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland. Infect Dis Poverty 2024; 13:4. [PMID: 38200542 PMCID: PMC10777546 DOI: 10.1186/s40249-024-01173-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/02/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system. METHODS Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran's I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission. RESULTS All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran's I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran's I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0-14 years old: F = 26.80, P < 0.001; 15-64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0-14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition. CONCLUSIONS Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.
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Affiliation(s)
- Xiaohan Si
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, 4059, Australia
| | - Liping Wang
- Information Center, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
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Wu H, Xue M, Wu C, Ding Z, Wang X, Fu T, Yang K, Lin J, Lu Q. Estimation of influenza incidence and analysis of epidemic characteristics from 2009 to 2022 in Zhejiang Province, China. Front Public Health 2023; 11:1154944. [PMID: 37427270 PMCID: PMC10328336 DOI: 10.3389/fpubh.2023.1154944] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/28/2023] [Indexed: 07/11/2023] Open
Abstract
Background Influenza infection causes a huge burden every year, affecting approximately 8% of adults and approximately 25% of children and resulting in approximately 400,000 respiratory deaths worldwide. However, based on the number of reported influenza cases, the actual prevalence of influenza may be greatly underestimated. The purpose of this study was to estimate the incidence rate of influenza and determine the true epidemiological characteristics of this virus. Methods The number of influenza cases and the prevalence of ILIs among outpatients in Zhejiang Province were obtained from the China Disease Control and Prevention Information System. Specimens were sampled from some cases and sent to laboratories for influenza nucleic acid testing. Random forest was used to establish an influenza estimation model based on the influenza-positive rate and the percentage of ILIs among outpatients. Furthermore, the moving epidemic method (MEM) was applied to calculate the epidemic threshold for different intensity levels. Joinpoint regression analysis was used to identify the annual change in influenza incidence. The seasonal trends of influenza were detected by wavelet analysis. Results From 2009 to 2021, a total of 990,016 influenza cases and 8 deaths were reported in Zhejiang Province. The numbers of estimated influenza cases from 2009 to 2018 were 743,449, 47,635, 89,026, 132,647, 69,218, 190,099, 204,606, 190,763, 267,168 and 364,809, respectively. The total number of estimated influenza cases is 12.11 times the number of reported cases. The APC of the estimated annual incidence rate was 23.33 (95% CI: 13.2 to 34.4) from 2011 to 2019, indicating a constant increasing trend. The intensity levels of the estimated incidence from the epidemic threshold to the very high-intensity threshold were 18.94 cases per 100,000, 24.14 cases per 100,000, 141.55 cases per 100,000, and 309.34 cases per 100,000, respectively. From the first week of 2009 to the 39th week of 2022, there were a total of 81 weeks of epidemics: the epidemic period reached a high intensity in 2 weeks, the epidemic period was at a moderate intensity in 75 weeks, and the epidemic period was at a low intensity in 2 weeks. The average power was significant on the 1-year scale, semiannual scale, and 115-week scale, and the average power of the first two cycles was significantly higher than that of the other cycles. In the period from the 20th week to the 35th week, the Pearson correlation coefficients between the time series of influenza onset and the positive rate of pathogens, including A(H3N2), A (H1N1)pdm2009, B(Victoria) and B(Yamagata), were - 0.089 (p = 0.021), 0.497 (p < 0.001), -0.062 (p = 0.109) and - 0.084 (p = 0.029), respectively. In the period from the 36th week of the first year to the 19th week of the next year, the Pearson correlation coefficients between the time series of influenza onset and the positive rate of pathogens, including A(H3N2), A (H1N1)pdm2009, B(Victoria) and B(Yamagata), were 0.516 (p < 0.001), 0.148 (p < 0.001), 0.292 (p < 0.001) and 0.271 (p < 0.001), respectively. Conclusion The disease burden of influenza has been seriously underestimated in the past. An appropriate method for estimating the incidence rate of influenza may be to comprehensively consider the influenza-positive rate as well as the percentage of ILIs among outpatients. The intensity level of the estimated incidence from the epidemic threshold to the very high-intensity threshold was calculated, thus yielding a quantitative standard for judging the influenza prevalence level in the future. The incidence of influenza showed semi-annual peaks in Zhejiang Province, including a main peak from December to January of the next year followed by a peak in summer. Furthermore, the driving factors of the influenza peaks were preliminarily explored. While the peak in summer was mainly driven by pathogens of A(H3N2), the peak in winter was alternately driven by various pathogens. Our research suggests that the government urgently needs to address barriers to vaccination and actively promote vaccines through primary care providers.
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Affiliation(s)
- Haocheng Wu
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Ming Xue
- Hangzhou Center for Disease Control and Prevention (HZCDC), Hangzhou, China
| | - Chen Wu
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Zheyuan Ding
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Xinyi Wang
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Tianyin Fu
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Ke Yang
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Junfen Lin
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Qinbao Lu
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
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Lu W, Ren H. Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis. Front Public Health 2023; 10:1089418. [PMID: 36699887 PMCID: PMC9868952 DOI: 10.3389/fpubh.2022.1089418] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/21/2022] [Indexed: 01/12/2023] Open
Abstract
Numerous investigations of the spatiotemporal patterns of infectious disease epidemics, their potential influences, and their driving mechanisms have greatly contributed to effective interventions in the recent years of increasing pandemic situations. However, systematic reviews of the spatiotemporal patterns of communicable diseases are rare. Using bibliometric analysis, combined with content analysis, this study aimed to summarize the number of publications and trends, the spectrum of infectious diseases, major research directions and data-methodological-theoretical characteristics, and academic communities in this field. Based on 851 relevant publications from the Web of Science core database, from January 1991 to September 2021, the study found that the increasing number of publications and the changes in the disease spectrum have been accompanied by serious outbreaks and pandemics over the past 30 years. Owing to the current pandemic of new, infectious diseases (e.g., COVID-19) and the ravages of old infectious diseases (e.g., dengue and influenza), illustrated by the disease spectrum, the number of publications in this field would continue to rise. Three logically rigorous research directions-the detection of spatiotemporal patterns, identification of potential influencing factors, and risk prediction and simulation-support the research paradigm framework in this field. The role of human mobility in the transmission of insect-borne infectious diseases (e.g., dengue) and scale effects must be extensively studied in the future. Developed countries, such as the USA and England, have stronger leadership in the field. Therefore, much more effort must be made by developing countries, such as China, to improve their contribution and role in international academic collaborations.
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Affiliation(s)
- Weili Lu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China,*Correspondence: Hongyan Ren ✉
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Wang X, Cai J, Liu X, Wang B, Yan L, Liu R, Nie Y, Wang Y, Zhang X, Zhang X. Impact of PM 2.5 and ozone on incidence of influenza in Shijiazhuang, China: a time-series study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:10426-10443. [PMID: 36076137 PMCID: PMC9458314 DOI: 10.1007/s11356-022-22814-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/27/2022] [Indexed: 05/03/2023]
Abstract
Most of the studies are focused on influenza and meteorological factors for influenza. There are still few studies focused on the relationship between pollution factors and influenza, and the results are not consistent. This study conducted distributed lag nonlinear model and attributable risk on the relationship between influenza and pollution factors, aiming to quantify the association and provide a basis for the prevention of influenza and the formulation of relevant policies. Environmental data in Shijiazhuang from 2014 to 2019, as well as the data on hospital-confirmed influenza, were collected. When the concentration of PM2.5 was the highest (621 μg/m3), the relative risk was the highest (RR: 2.39, 95% CI: 1.10-5.17). For extremely high concentration PM2.5 (348 μg/m3), analysis of cumulative lag effect showed statistical significance from cumulative lag0-1 to lag0-6 day, and the minimum cumulative lag effect appeared in lag0-2 (RR: 0.760, 95% CI: 0.655-0.882). In terms of ozone, the RR value was 2.28(1.19,4.38), when O3 concentration was 310 μg/m3, and the RR was 1.65(1.26,2.15), when O3 concentration was 0 μg/m3. The RR of this lag effect increased with the increase of lag days, and reached the maximum at lag0-7 days, RR and 95% CI of slightly low concentration and extremely high concentration were 1.217(1.108,1.337) and 1.440(1.012,2.047), respectively. Stratified analysis showed that there was little difference in gender, but in different age groups, the cumulative lag effect of these two pollutants on influenza was significantly different. Our study found a non-linear relationship between two pollutants and influenza; slightly low concentrations were more associated with contaminant-related influenza. Health workers should encourage patients to get the influenza vaccine and wear masks when going out during flu seasons.
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Affiliation(s)
- Xue Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Jianning Cai
- The Department of Epidemic Treating and Preventing, Center for Disease Prevention and Control of Shijiazhuang City, Shijiazhuang, China
| | - Xuehui Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Binhao Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Lina Yan
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Ran Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Yaxiong Nie
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Yameng Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Xinzhu Zhang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China
| | - Xiaolin Zhang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, 361 Zhongshan East Road, Shijiazhuang, 050017, China.
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11
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Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016-2018. PLoS One 2022; 17:e0278900. [PMID: 36512615 PMCID: PMC9747007 DOI: 10.1371/journal.pone.0278900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Seasonal influenza is a significant public health challenge worldwide. This study aimed to investigate the epidemiological characteristics and spatial patterns of severe hospitalized influenza cases confirmed by polymerase chain reaction (PCR) in Iran. METHODS Data were obtained from Iran's Ministry of Health and Medical Education and included all hospitalized lab-confirmed influenza cases from January 1, 2016, to December 30, 2018 (n = 9146). The Getis-Ord Gi* and Local Moran's I statistics were used to explore the hotspot areas and spatial cluster/outlier patterns of influenza. We also built a multivariable logistic regression model to identify covariates associated with patients' mortality. RESULTS Cumulative incidence and mortality rate were estimated at 11.44 and 0.49 (per 100,000), respectively, and case fatality rate was estimated at 4.35%. The patients' median age was 40 (interquartile range: 22-63), and 55.5% (n = 5073) were female. The hotspot and cluster analyses revealed high-risk areas in northern parts of Iran, especially in cold, humid, and densely populated areas. Moreover, influenza hotspots were more common during the colder months of the year, especially in high-elevated regions. Mortality was significantly associated with older age (adjusted odds ratio [aOR]: 1.01, 95% confidence interval [CI]: 1.01-1.02), infection with virus type-A (aOR: 1.64, 95% CI: 1.27-2.15), male sex (aOR: 1.77, 95% CI: 1.44-2.18), cardiovascular disease (aOR: 1.71, 95% CI: 1.33-2.20), chronic obstructive pulmonary disease (aOR: 1.82, 95% CI: 1.40-2.34), malignancy (aOR: 4.77, 95% CI: 2.87-7.62), and grade-II obesity (aOR: 2.11, 95% CI: 1.09-3.74). CONCLUSIONS We characterized the spatial and epidemiological heterogeneities of severe hospitalized influenza cases confirmed by PCR in Iran. Detecting influenza hotspot clusters could inform prioritization and geographic specificity of influenza prevention, testing, and mitigation resource management, including vaccination planning in Iran.
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12
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Francis-Tan A, Wang X. Spatiotemporal patterns of lung disease in China before 2019: A brief analysis of two nationally representative surveys. PLoS One 2022; 17:e0278031. [PMID: 36409743 PMCID: PMC9678254 DOI: 10.1371/journal.pone.0278031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/17/2022] [Indexed: 11/22/2022] Open
Abstract
Little is publicly known about the conditions surrounding the emergence of COVID in China. Using two nationally representative datasets, the China Family Panel Studies (CFPS) and the China Health and Retirement Longitudinal Study (CHARLS), we engage in a descriptive analysis of spatiotemporal patterns of lung and other diseases before 2019. In both datasets, the incidence of lung disease in 2018 was elevated in Hubei province relative to other provinces. The incidence of psychiatric and nervous system disease was elevated as well. Overall, the evidence is consistent with many possible explanations. One conjecture is that there was an outbreak of influenza in central China, which implies the conditions that increased the susceptibility to influenza also facilitated the later spread of COVID. Another conjecture, though less likely, is that COVID was circulating at low levels in the population in central China during 2018. This study calls for more investigation to understand the conditions surrounding the emergence of COVID.
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Affiliation(s)
- Andrew Francis-Tan
- Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore
- * E-mail:
| | - Xueqing Wang
- Office of Population Research, School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
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13
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Zheng J, Shen G, Hu S, Han X, Zhu S, Liu J, He R, Zhang N, Hsieh CW, Xue H, Zhang B, Shen Y, Mao Y, Zhu B. Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review. BMC Infect Dis 2022; 22:723. [PMID: 36064333 PMCID: PMC9442567 DOI: 10.1186/s12879-022-07669-9] [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: 05/26/2022] [Accepted: 08/03/2022] [Indexed: 11/20/2022] Open
Abstract
Background The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases. Methods We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors. Conclusions Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07669-9.
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Affiliation(s)
- Junyao Zheng
- China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China.,School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China
| | - Guoquan Shen
- School of Public Administration and Policy, Renmin University of China, Beijing, China
| | - Siqi Hu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Xinxin Han
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Siyu Zhu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Jinlin Liu
- School of Public Policy and Administration, Northwestern Polytechnical University, Xi'an, China
| | - Rongxin He
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Ning Zhang
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.,MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College, London, UK
| | - Chih-Wei Hsieh
- Department of Public Policy, City University of Hong Kong, Hong Kong, China
| | - Hao Xue
- Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA
| | - Bo Zhang
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yue Shen
- Laboratory for Urban Future, School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Ying Mao
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Bin Zhu
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
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Lin CH, Wen TH. How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission. Trop Med Infect Dis 2022; 7:164. [PMID: 36006256 PMCID: PMC9413673 DOI: 10.3390/tropicalmed7080164] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/15/2022] [Accepted: 07/15/2022] [Indexed: 02/06/2023] Open
Abstract
Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.
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Affiliation(s)
- Chia-Hsien Lin
- Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei City 10610, Taiwan
- Department of Geography, National Taiwan University, Taipei City 10617, Taiwan;
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei City 10617, Taiwan;
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15
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Congdon P. A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates. JOURNAL OF GEOGRAPHICAL SYSTEMS 2022; 24:583-610. [PMID: 35496370 PMCID: PMC9039004 DOI: 10.1007/s10109-021-00366-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/07/2021] [Indexed: 06/14/2023]
Abstract
The COVID-19 epidemic has raised major issues with regard to modelling and forecasting outcomes such as cases, deaths and hospitalisations. In particular, the forecasting of area-specific counts of infectious disease poses problems when counts are changing rapidly and there are infection hotspots, as in epidemic situations. Such forecasts are of central importance for prioritizing interventions or making severity designations for different areas. In this paper, we consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations. In particular, we introduce parameters to allow temporal adaptivity in autoregressive dependence. A case study considers COVID-19 data for 144 English local authorities during the UK epidemic second wave in late 2020 and early 2021, which demonstrate geographical clustering in new cases-linked to the then emergent alpha variant. The model allows for both spatial and time variation in autoregressive effects. We assess sensitivity in short-term predictions and fit to specification (spatial vs space-time autoregression, linear vs log-linear, and form of space decay), and show improved one-step ahead and in-sample prediction using space-time autoregression including temporal adaptivity.
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Affiliation(s)
- Peter Congdon
- School of Geography, Queen Mary University of London, Mile End Rd, London, E1 4NS UK
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16
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Incidence trend and disease burden of seven vaccine-preventable diseases in Shandong province, China, 2013-2017: Findings from a population-based observational study. Vaccine X 2022; 10:100145. [PMID: 35243321 PMCID: PMC8867126 DOI: 10.1016/j.jvacx.2022.100145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/19/2021] [Accepted: 01/25/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Although vaccines provide a cost-effective solution to vaccine-preventable diseases (VPDs), the disease burden of VPDs is still very high in most parts of the world. Methods A population-based observational study was conducted in Shandong province, China, from 2013 to 2017, giving an insight into the epidemiological characteristics and disease burden of seven VPDs. The incidence trend was estimated using the Poisson regression model. The disease burden was calculated using the disability-adjusted life years (DALYs). Results Most VPDs included in the China’s National Immunization Program had higher incidence density (ID) in inland cities. The ID of mumps decreased significantly, while herpes zoster increased (both P < 0.05). The top three causes of the disease burden as assessed with DALYs included tuberculosis, herpes zoster, and hepatitis B, with the rates of 72.21, 59.99, and 52.10 DALYs/100 000, respectively. The disease burden of influenza and herpes zoster were relatively high in people aged > 50 years, while highest DALYs of hepatitis B were found in young adults. Conclusion Inequalities in the vaccine coverage by geography, socio-economic status, and targeted population contribute to the increasing incidence and high burden of VPDs and call for renewed and sustained immunization strategies in China.
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17
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Song S, Li Q, Shen L, Sun M, Yang Z, Wang N, Liu J, Liu K, Shao Z. From Outbreak to Near Disappearance: How Did Non-pharmaceutical Interventions Against COVID-19 Affect the Transmission of Influenza Virus? Front Public Health 2022; 10:863522. [PMID: 35425738 PMCID: PMC9001955 DOI: 10.3389/fpubh.2022.863522] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/02/2022] [Indexed: 11/13/2022] Open
Abstract
Influenza shares the same putative transmission pathway with coronavirus disease 2019 (COVID-19), and causes tremendous morbidity and mortality annually globally. Since the transmission of COVID-19 in China, a series of non-pharmaceutical interventions (NPIs) against to the disease have been implemented to contain its transmission. Based on the surveillance data of influenza, Search Engine Index, and meteorological factors from 2011 to 2021 in Xi'an, and the different level of emergence responses for COVID-19 from 2020 to 2021, Bayesian Structural Time Series model and interrupted time series analysis were applied to quantitatively assess the impact of NPIs in sequent phases with different intensities, and to estimate the reduction of influenza infections. From 2011 to 2021, a total of 197,528 confirmed cases of influenza were reported in Xi'an, and the incidence of influenza continuously increased from 2011 to 2019, especially, in 2019-2020, when the incidence was up to 975.90 per 100,000 persons; however, it showed a sharp reduction of 97.68% in 2020-2021, and of 87.22% in 2021, comparing with 2019-2020. The highest impact on reduction of influenza was observed in the phase of strict implementation of NPIs with an inclusion probability of 0.54. The weekly influenza incidence was reduced by 95.45%, and an approximate reduction of 210,100 (95% CI: 125,100-329,500) influenza infections was found during the post-COVID-19 period. The reduction exhibited significant variations in the geographical, population, and temporal distribution. Our findings demonstrated that NPIs against COVID-19 had a long-term impact on the reduction of influenza transmission.
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Affiliation(s)
- Shuxuan Song
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Qian Li
- Department of Infectious Disease Control and Prevention, Xi'an Center for Disease Prevention and Control, Xi'an, China
| | - Li Shen
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Minghao Sun
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Zurong Yang
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Nuoya Wang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Jifeng Liu
- Department of Infectious Disease Control and Prevention, Xi'an Center for Disease Prevention and Control, Xi'an, China
| | - Kun Liu
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Zhongjun Shao
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
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18
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English AS, Talhelm T, Tong R, Li X, Su Y. Historical rice farming explains faster mask use during early days of China's COVID-19 outbreak. CURRENT RESEARCH IN ECOLOGICAL AND SOCIAL PSYCHOLOGY 2022; 3:100034. [PMID: 35098192 PMCID: PMC8761258 DOI: 10.1016/j.cresp.2022.100034] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 05/11/2023]
Abstract
In the early days of the coronavirus outbreak, we observed mask use in public among 1,330 people across China. People in regions with a history of farming rice wore masks more often than people in wheat regions. Cultural differences persisted after taking into account objective risk factors such as local COVID cases. The differences fit with the emerging theory that rice farming's labor and irrigation demands made societies more interdependent, with tighter social norms. Cultural differences were strongest in the ambiguous, early days of the pandemic, then shrank as masks became nearly universal (94%). Separate survey and internet search data replicated this pattern. Although strong cultural differences lasted only a few days, research suggests that acting just a few days earlier can reduce deaths substantially.
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Affiliation(s)
| | - Thomas Talhelm
- Behavioral Science, Booth School of Business, University of Chicago; Chicago, USA
| | - Rongtian Tong
- Henry M. Jackson School of International Studies, University of Washington; Seattle, USA
| | - Xiaoyuan Li
- Intercultural Institute, Shanghai International Studies University; Shanghai, China
| | - Yan Su
- Intercultural Institute, Shanghai International Studies University; Shanghai, China
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Sun X, Xu Y, Zhu Y, Tang F. Impact of non-pharmaceutical interventions on the incidences of vaccine-preventable diseases during the COVID-19 pandemic in the eastern of China. Hum Vaccin Immunother 2021; 17:4083-4089. [PMID: 34374635 DOI: 10.1080/21645515.2021.1956227] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND In response to the coronavirus disease 2019 (COVID-19) pandemic, many countries have implemented mitigating non-pharmaceutical interventions. We investigated the impact of these interventions and changes in public behavior on the incidences of selected vaccine-preventable diseases (VPDs) during the COVID-19 pandemic in the eastern of China. METHODS From routine monitoring data collected in the capital of eastern China's Jiangsu Province, we extracted and analyzed the incidences of influenza; hand, foot and mouth disease (HFMD); varicella; mumps; pertussis; and hepatitis B. We also investigated the changes in public behavior during the COVID-19 pandemic through telephone interviews and questionnaire surveys. RESULTS Compared with the baseline (2017-2019), the incidences of all VPDs except influenza declined significantly in 2020 (HFMD decreased by 79.92%, varicella decreased by 7.71%, mumps decreased by 2.03%, pertussis decreased by 78.91%, and hepatitis B decreased by 0.31%). The reduction in reported cases in children (0-14 years) was greater than that in adults, and pertussis had the largest reduction (approximately 80%) in children. Influenza peaks in winter; in the three years before the COVID-19 pandemic, Influenza rates took an average of 10 weeks to recede to their lowest levels after the Spring Festival, while in 2020, this took only 1 week. A total of 366 outbreaks with 20,205 cases were reported during the COVID-19 pandemic. Among the participants in the study, 94.2% of the interviewees avoided going to high-risk areas, 82.4% avoided going to crowded places, 92.9% wore masks when going out,88.4% washed their hands frequently, and 67.9% maintained social distance. CONCLUSIONS Our study showed significant reductions in the incidences of VPDs after the implementation of a series of non-pharmaceutical interventions during the COVID-19 pandemic.
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Affiliation(s)
- Xiang Sun
- Department of Expanded Program on Immunization, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu Province, China
| | - Yan Xu
- Department of Expanded Program on Immunization, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu Province, China
| | - Yuanyuan Zhu
- Department of Expanded Program on Immunization, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu Province, China
| | - Fenyang Tang
- Department of Expanded Program on Immunization, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu Province, China
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Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18137120. [PMID: 34281057 PMCID: PMC8297262 DOI: 10.3390/ijerph18137120] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/27/2021] [Accepted: 06/29/2021] [Indexed: 01/04/2023]
Abstract
About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011-2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)52 model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.
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21
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Madden JM, More S, Teljeur C, Gleeson J, Walsh C, McGrath G. Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6285. [PMID: 34200681 PMCID: PMC8296107 DOI: 10.3390/ijerph18126285] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/02/2021] [Accepted: 06/04/2021] [Indexed: 12/16/2022]
Abstract
Like most countries worldwide, the coronavirus disease (COVID-19) has adversely affected Ireland. The aim of this study was to (i) investigate the spatio-temporal trend of COVID-19 incidence; (ii) describe mobility trends as measured by aggregated mobile phone records; and (iii) investigate the association between deprivation index, population density and COVID-19 cases while accounting for spatial and temporal correlation. Standardised incidence ratios of cases were calculated and mapped at a high spatial resolution (electoral division level) over time. Trends in the percentage change in mobility compared to a pre-COVID-19 period were plotted to investigate the impact of lockdown restrictions. We implemented a hierarchical Bayesian spatio-temporal model (Besag, York and Mollié (BYM)), commonly used for disease mapping, to investigate the association between covariates and the number of cases. There have been three distinct "waves" of COVID-19 cases in Ireland to date. Lockdown restrictions led to a substantial reduction in human movement, particularly during the 1st and 3rd wave. Despite adjustment for population density (incidence ratio (IR) = 1.985 (1.915-2.058)) and the average number of persons per room (IR = 10.411 (5.264-22.533)), we found an association between deprivation index and COVID-19 incidence (IR = 1.210 (CI: 1.077-1.357) for the most deprived quintile compared to the least deprived). There is a large range of spatial heterogeneity in COVID-19 cases in Ireland. The methods presented can be used to explore locally intensive surveillance with the possibility of localised lockdown measures to curb the transmission of infection, while keeping other, low-incidence areas open. Our results suggest that prioritising densely populated deprived areas (that are at increased risk of comorbidities) during vaccination rollout may capture people that are at risk of infection and, potentially, also those at increased risk of hospitalisation.
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Affiliation(s)
- Jamie M. Madden
- Centre for Veterinary Epidemiology and Risk Analysis (CVERA), School of Veterinary Medicine, University College Dublin, D04 W6F6 Dublin, Ireland; (S.M.); (G.M.)
| | - Simon More
- Centre for Veterinary Epidemiology and Risk Analysis (CVERA), School of Veterinary Medicine, University College Dublin, D04 W6F6 Dublin, Ireland; (S.M.); (G.M.)
| | - Conor Teljeur
- Health Technology Assessment Directorate, Health Information and Quality Authority, D07 E98Y Dublin, Ireland;
| | - Justin Gleeson
- National Institute for Regional and Spatial Analysis, National University of Ireland Maynooth, W23 F2H6 Kildare, Ireland;
| | - Cathal Walsh
- Health Research Institute and MACSI, University of Limerick, V94 T9PX Limerick, Ireland;
| | - Guy McGrath
- Centre for Veterinary Epidemiology and Risk Analysis (CVERA), School of Veterinary Medicine, University College Dublin, D04 W6F6 Dublin, Ireland; (S.M.); (G.M.)
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22
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Coura-Vital W, Cardoso DT, Ker FTDO, Magalhães FDC, Bezerra JMT, Viegas AM, Morais MHF, Bastos LS, Reis IA, Carneiro M, Barbosa DS. Spatiotemporal dynamics and risk estimates of COVID-19 epidemic in Minas Gerais State: analysis of an expanding process. Rev Inst Med Trop Sao Paulo 2021; 63:e21. [PMID: 33787741 PMCID: PMC7997666 DOI: 10.1590/s1678-9946202163021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 02/20/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 is an infectious disease caused by the recently discovered coronavirus
SARS-Cov-2. The disease became pandemic affecting many countries globally,
including Brazil. Considering the expansion process and particularities during
the initial stages of the epidemic, we aimed to analyze the spatial and
spatiotemporal patterns of COVID-19 occurrence and to identify priority risk
areas in Minas Gerais State, Southeast Brazil. An ecological study was performed
considering all data from human cases of COVID-19 confirmed from the
epidemiological week (EW) 11 (March 08, 2020) to EW 26 (June 27, 2020). Crude
and smoothed incidence rates were used to analyze the distribution of disease
patterns based on global and local indicators of spatial association and
space-time risk assessment. Positive spatial autocorrelation and spatial
dependence were found. Our results suggest that the metropolitan region of the
State capital Belo Horizonte (MRBH) and Vale do Rio Doce mesoregions, as major
epidemic foci in the beginning of the expansion process, have had important
influence on the dispersion of SARS-CoV-2 in Minas Gerais State. Triangulo
Mineiro/Alto Paranaiba region presented the highest risk of infection. In
addition, six statistically significant spatiotemporal clusters were identified
in the State, three at high risk and three at low risk. Our findings contribute
to a greater understanding of the space-time disease dynamic and discuss
strategies for identification of priority areas for COVID-19 surveillance and
control.
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Affiliation(s)
- Wendel Coura-Vital
- Universidade Federal de Ouro Preto, Escola de Farmácia, Departamento de Análises Clínicas, Ouro Preto, Minas Gerais, Brazil
| | - Diogo Tavares Cardoso
- Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Belo Horizonte, Minas Gerais, Brazil
| | | | - Fernanda do Carmo Magalhães
- Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Belo Horizonte, Minas Gerais, Brazil
| | - Juliana Maria Trindade Bezerra
- Universidade Estadual do Maranhão, Campus de Lago da Pedra, Curso de Ciências Biológicas, Lago da Pedra, Maranhão, Brazil
| | - Ana Maria Viegas
- Prefeitura Municipal de Belo Horizonte, Belo Horizonte, Minas Gerais, Brasil.,Prefeitura Municipal de Contagem, Contagem, Minas Gerais, Brazil
| | - Maria Helena Franco Morais
- Prefeitura Municipal de Belo Horizonte, Belo Horizonte, Minas Gerais, Brasil.,Prefeitura Municipal de Contagem, Contagem, Minas Gerais, Brazil
| | - Leonardo Soares Bastos
- Fundação Oswaldo Cruz, Programa de Computação Científica, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Ilka Afonso Reis
- Universidade Federal de Minas Gerais, Instituto de Ciências Exatas, Belo Horizonte, Minas Gerais, Brazil
| | - Mariângela Carneiro
- Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Belo Horizonte, Minas Gerais, Brazil
| | - David Soeiro Barbosa
- Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Belo Horizonte, Minas Gerais, Brazil
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23
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Jiang P, Fu X, Fan YV, Klemeš JJ, Chen P, Ma S, Zhang W. Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour. JOURNAL OF CLEANER PRODUCTION 2021; 279:123673. [PMID: 32836914 PMCID: PMC7435297 DOI: 10.1016/j.jclepro.2020.123673] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/10/2020] [Accepted: 08/08/2020] [Indexed: 05/18/2023]
Abstract
Coronavirus disease-2019 (COVID-19) poses a significant threat to the population and urban sustainability worldwide. The surge mitigation is complicated and associates many factors, including the pandemic status, policy, socioeconomics and resident behaviours. Modelling and analytics with spatial-temporal big urban data are required to assist the mitigation of the pandemic. This study proposes a novel perspective to analyse the spatial-temporal potential exposure risk of residents by capturing human behaviours based on spatial-temporal car park availability data. Near real-time data from 1,904 residential car parks in Singapore, a classical megacity, are collected to analyse car mobility and its spatial-temporal heat map. The implementation of the circuit breaker, a COVID-19 measure, in Singapore has reduced the mobility and heat (daily frequency of mobility) significantly at about 30.0%. It contributes to a 44.3%-55.4% reduction in the transportation-related air emissions under two scenarios of travelling distance reductions. Urban sustainability impacts in both environment and economy are discussed. The spatial-temporal potential exposure risk mapping with space-time interactions is further investigated via an extended Bayesian spatial-temporal regression model. The maximal reduction rate of the defined potential exposure risk lowers to 37.6% by comparison with its peak value. The big data analytics of changes in car mobility behaviour and the resultant potential exposure risks can provide insights to assist in (a) designing a flexible circuit breaker exit strategy, (b) precise management via identifying and tracing hotspots on the mobility heat map, and (c) making timely decisions by fitting curves dynamically in different phases of COVID-19 mitigation. The proposed method has the potential to be used by decision-makers worldwide with available data to make flexible regulations and planning.
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Affiliation(s)
- Peng Jiang
- Department of Systems Science, Institute of High Performance Computing, A∗STAR, Singapore, 138632, Singapore
| | - Xiuju Fu
- Department of Systems Science, Institute of High Performance Computing, A∗STAR, Singapore, 138632, Singapore
| | - Yee Van Fan
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69, Brno, Czech Republic
| | - Jiří Jaromír Klemeš
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69, Brno, Czech Republic
| | - Piao Chen
- Delft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands
| | - Stefan Ma
- Epidemiology & Disease Control Division, Ministry of Health, Singapore
| | - Wanbing Zhang
- Department of Systems Science, Institute of High Performance Computing, A∗STAR, Singapore, 138632, Singapore
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24
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Yang W, Deng M, Li C, Huang J. Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17072563. [PMID: 32276501 PMCID: PMC7177341 DOI: 10.3390/ijerph17072563] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/31/2020] [Accepted: 04/01/2020] [Indexed: 12/29/2022]
Abstract
Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.
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Affiliation(s)
- Wentao Yang
- National-local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China; (W.Y.); (C.L.)
- Hunan Provincial Key Laboratory of Geo-information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411100, China
| | - Min Deng
- School of Geosciences and Info-physics, Central South University, Changsha 410083, China;
- Correspondence: ; Tel.: +86-1350-746-7258
| | - Chaokui Li
- National-local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China; (W.Y.); (C.L.)
- Hunan Provincial Key Laboratory of Geo-information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411100, China
| | - Jincai Huang
- School of Geosciences and Info-physics, Central South University, Changsha 410083, China;
- Shenzhen Key Laboratory of Spatial Smart Sensing and Service, Shenzhen University, Shenzhen 518060, China
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