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Beggs PJ, Trueck S, Linnenluecke MK, Bambrick H, Capon AG, Hanigan IC, Arriagada NB, Cross TJ, Friel S, Green D, Heenan M, Jay O, Kennard H, Malik A, McMichael C, Stevenson M, Vardoulakis S, Dang TN, Garvey G, Lovett R, Matthews V, Phung D, Woodward AJ, Romanello MB, Zhang Y. The 2023 report of the MJA-Lancet Countdown on health and climate change: sustainability needed in Australia's health care sector. Med J Aust 2024; 220:282-303. [PMID: 38522009 DOI: 10.5694/mja2.52245] [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: 08/16/2023] [Accepted: 12/06/2023] [Indexed: 03/25/2024]
Abstract
The MJA-Lancet Countdown on health and climate change in Australia was established in 2017 and produced its first national assessment in 2018 and annual updates in 2019, 2020, 2021 and 2022. It examines five broad domains: health hazards, exposures and impacts; adaptation, planning and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. In this, the sixth report of the MJA-Lancet Countdown, we track progress on an extensive suite of indicators across these five domains, accessing and presenting the latest data and further refining and developing our analyses. Our results highlight the health and economic costs of inaction on health and climate change. A series of major flood events across the four eastern states of Australia in 2022 was the main contributor to insured losses from climate-related catastrophes of $7.168 billion - the highest amount on record. The floods also directly caused 23 deaths and resulted in the displacement of tens of thousands of people. High red meat and processed meat consumption and insufficient consumption of fruit and vegetables accounted for about half of the 87 166 diet-related deaths in Australia in 2021. Correction of this imbalance would both save lives and reduce the heavy carbon footprint associated with meat production. We find signs of progress on health and climate change. Importantly, the Australian Government released Australia's first National Health and Climate Strategy, and the Government of Western Australia is preparing a Health Sector Adaptation Plan. We also find increasing action on, and engagement with, health and climate change at a community level, with the number of electric vehicle sales almost doubling in 2022 compared with 2021, and with a 65% increase in coverage of health and climate change in the media in 2022 compared with 2021. Overall, the urgency of substantial enhancements in Australia's mitigation and adaptation responses to the enormous health and climate change challenge cannot be overstated. Australia's energy system, and its health care sector, currently emit an unreasonable and unjust proportion of greenhouse gases into the atmosphere. As the Lancet Countdown enters its second and most critical phase in the leadup to 2030, the depth and breadth of our assessment of health and climate change will be augmented to increasingly examine Australia in its regional context, and to better measure and track key issues in Australia such as mental health and Aboriginal and Torres Strait Islander health and wellbeing.
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Affiliation(s)
| | | | | | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT
| | - Anthony G Capon
- Monash Sustainable Development Institute, Monash University, Melbourne, VIC
| | | | | | | | | | - Donna Green
- Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, UNSW, Sydney, NSW
| | - Maddie Heenan
- Australian Prevention Partnership Centre, Sax Institute, Sydney, NSW
- The George Institute for Global Health, Sydney, NSW
| | - Ollie Jay
- Thermal Ergonomics Laboratory, University of Sydney, Sydney, NSW
| | - Harry Kennard
- Center on Global Energy Policy, Columbia University, New York, NY, USA
| | | | | | - Mark Stevenson
- Transport, Health and Urban Design (THUD) Research Lab, University of Melbourne, Melbourne, VIC
| | - Sotiris Vardoulakis
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT
| | - Tran N Dang
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | | | - Raymond Lovett
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT
- Australian Institute of Aboriginal and Torres Strait Islander Studies, Canberra, ACT
| | - Veronica Matthews
- University Centre for Rural Health, University of Sydney, Sydney, NSW
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Zhang L, Yang F, Zhu Z, Zeng W, Rong Z, Hu J, Li X, Zhao J, Zeng B, Li Y, Quan Y, Zhang Q, Huang Z, Li Y, Huang X, Zheng W, Xu J, Li Y, Chen Q, Xiao J, Zhang M. Assessing the impact of COVID-19 interventions on the hand, foot and mouth disease in Guangdong Province, China: a Bayesian modeling study. Front Public Health 2024; 11:1307321. [PMID: 38348379 PMCID: PMC10860754 DOI: 10.3389/fpubh.2023.1307321] [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: 10/04/2023] [Accepted: 12/26/2023] [Indexed: 02/15/2024] Open
Abstract
Background The non-pharmaceutical interventions (NPIs) against COVID-19 may have affected the transmission of hand, foot and mouth disease (HFMD). We aimed to assess the impact of the NPIs on HFMD in the high epidemic area of HFMD, Guangdong Province. Methods The data of HFMD cases, etiological information, and meteorological factors in Guangdong from January 1, 2012, to December 31, 2021, were collected. Using a Bayesian structural time series (BSTS) model integrated counterfactual framework, we assessed the effect of NPIs on HFMD by different intervention periods, populations (gender, age, occupation), and cities. We further explored the correlation between the reduction of HFMD and socioeconomic factors in 21 cities. Results A total of 351,217 HFMD cases were reported and 455,327 cases were averted in Guangdong Province during 2020-2021 with a reduction of 84.94% (95%CI: 81.63-87.22%) in 2020 and 29.49% (95%CI: 15.26-39.54%) in 2021. The impact of NPIs on HFMD differed by age and gender. The effects of NPIs were more remarkable for children aged 0-2 years and scattered children. We found that the relative reductions in 21 cities were related to the composition ratio of children and COVID-19 incidence. Conclusion The reduction of HFMD incidence was significantly associated with COVID-19 NPIs, and school closure was an effective intervention to prevent HFMD outbreaks. Our findings will contribute to the development of HFMD prevention and control measures.
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Affiliation(s)
- Li Zhang
- School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Fen Yang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, China
| | - Zhihua Zhu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Weilin Zeng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Zuhua Rong
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jianguo Zhao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Biao Zeng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, China
| | - Yihan Li
- School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yi Quan
- School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Qian Zhang
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Zitong Huang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuye Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xing Huang
- School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Wenyuan Zheng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Medicine, Jinan University, Guangzhou, China
| | - Jiaqing Xu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yan Li
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, China
| | - Qing Chen
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Jianpeng Xiao
- School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, China
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Linh Tran NQ, Cam Hong Le HT, Pham CT, Nguyen XH, Tran ND, Thi Tran TH, Nghiem S, Ly Luong TM, Bui V, Nguyen-Huy T, Doan VQ, Dang KA, Thuong Do TH, Thi Ngo HK, Nguyen TV, Nguyen NH, Do MC, Ton TN, Thu Dang TA, Nguyen K, Tran XB, Thai P, Phung D. Climate change and human health in Vietnam: a systematic review and additional analyses on current impacts, future risk, and adaptation. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 40:100943. [PMID: 38116497 PMCID: PMC10730327 DOI: 10.1016/j.lanwpc.2023.100943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/29/2023] [Accepted: 10/09/2023] [Indexed: 12/21/2023]
Abstract
This study aims to investigate climate change's impact on health and adaptation in Vietnam through a systematic review and additional analyses of heat exposure, heat vulnerability, awareness and engagement, and projected health costs. Out of 127 reviewed studies, findings indicated the wider spread of infectious diseases, and increased mortality and hospitalisation risks associated with extreme heat, droughts, and floods. However, there are few studies addressing health cost, awareness, engagement, adaptation, and policy. Additional analyses showed rising heatwave exposure across Vietnam and global above-average vulnerability to heat. By 2050, climate change is projected to cost up to USD1-3B in healthcare costs, USD3-20B in premature deaths, and USD6-23B in work loss. Despite increased media focus on climate and health, a gap between public and government publications highlighted the need for more governmental engagement. Vietnam's climate policies have faced implementation challenges, including top-down approaches, lack of cooperation, low adaptive capacity, and limited resources.
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Affiliation(s)
- Nu Quy Linh Tran
- Centre for Environment and Population Health, School of Medicine and Dentistry, Griffith University, Australia
| | - Huynh Thi Cam Hong Le
- Child Health Research Centre, Faculty of Medicine, University of Queensland, Australia
| | | | - Xuan Huong Nguyen
- Centre for Scientific Research and International Collaboration, Phan Chau Trinh University, Quang Nam, Vietnam
| | - Ngoc Dang Tran
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | | | - Son Nghiem
- Department of Health Economics, Wellbeing and Society, Australian National University, Australia
| | - Thi Mai Ly Luong
- Faculty of Environmental Sciences, Vietnam University of Science, Hanoi, Vietnam
| | - Vinh Bui
- Faculty of Science and Engineering, Southern Cross University, Australia
| | - Thong Nguyen-Huy
- Centre for Applied Climate Sciences, University of Southern Queensland, Australia
| | - Van Quang Doan
- Centre for Computational Sciences, University of Tsukuba, Japan
| | - Kim Anh Dang
- Queensland Alliance for Environmental Health Sciences, The University of Queensland, Australia
| | - Thi Hoai Thuong Do
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Hieu Kim Thi Ngo
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | | | - Ngoc Huy Nguyen
- Vietnam National University - Vietnam Japan University, Hanoi, Vietnam
| | - Manh Cuong Do
- Health Environment Management Agency, Ministry of Health, Vietnam
| | | | - Thi Anh Thu Dang
- Hue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam
| | - Kien Nguyen
- Hue University of Economics, Hue University, Hue City, Vietnam
| | | | - Phong Thai
- Queensland Alliance for Environmental Health Sciences, The University of Queensland, Australia
| | - Dung Phung
- School of Public Health, The University of Queensland, Australia
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Zhu H, Chen S, Liang R, Feng Y, Joldosh A, Xie Z, Chen G, Li L, Chen K, Fang Y, Ou J. Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China. BMC Infect Dis 2023; 23:299. [PMID: 37147566 PMCID: PMC10161995 DOI: 10.1186/s12879-023-08184-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/20/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. METHOD A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. RESULTS Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. CONCLUSION This study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.
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Affiliation(s)
- Hansong Zhu
- Fujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical University, Fuzhou, Fujian, 350012, China
| | - Si Chen
- Fujian Climate Center, Fuzhou, 350028, Fujian, China
| | - Rui Liang
- Department of Nutrition, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Yulin Feng
- School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Aynur Joldosh
- School of Public Health, Xiamen University, Xiamen, 361005, Fujian, China
| | - Zhonghang Xie
- Fujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical University, Fuzhou, Fujian, 350012, China
| | - Guangmin Chen
- Fujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical University, Fuzhou, Fujian, 350012, China
| | - Lingfang Li
- Fujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical University, Fuzhou, Fujian, 350012, China
| | - Kaizhi Chen
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, Fujian, China.
| | - Yuanyuan Fang
- Department of Pediatric Surgery, Fujian Children's Hospital, Fuzhou, 350001, Fujian, China.
| | - Jianming Ou
- Fujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical University, Fuzhou, Fujian, 350012, 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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Qiao X, Liu X, Wang Y, Li Y, Wang L, Yang Q, Wang H, Shen H. Analysis of the epidemiological trends of enterovirus A in Asia and Europe. J Infect Chemother 2023; 29:316-321. [PMID: 36528275 DOI: 10.1016/j.jiac.2022.12.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 11/15/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Enteroviruses have been in massive, cyclical epidemics worldwide. An in-depth understanding of the international epidemiological characteristics of Enterovirus A (EVA) is critical to determining its clinical significance and total disease burden. Although much research has been conducted on EVA epidemiology, there is still a lack of a comprehensive overview of EVA epidemiological characteristics and trends. OBJECTIVE EVA nucleic acid sequences from the NCBI virus database were used to summarize the epidemic time (based on the time of specimen collection), spatial and serotype distribution of EVA, and to analyze EVA isolated from cerebrospinal fluid specimens. METHODS EVA sequences were searched in NCBI Virus by keyword ("Enterovirus A″ or "EVA") to screen sequences released before December 2021 and sort them to analyze EVA by year, geographic region and serotype prevalence. RESULTS The results found 23,041 retrieved nucleic acid sequences with precise collection dates and geographical regions as of December 2021, with Asia accounting for 87%, Europe for 11% and Africa and the Americas for only 2%. Overall, EV-A71, CVA6 and CVA16 are a few of the main prevalent serotypes; and the prevalence characteristics of the different serotypes change over time from place to place. CONCLUSION The prevalence of different serotypes of EVA varies considerably over time and space, and we focused on analysing the epidemiological characteristics of EVAs in Asia and Europe and EVAs that invade the nervous system. This study will likely provide important clues for prevention, control and future research in virological surveillance, disease management and vaccine development.
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Affiliation(s)
- Xiaorong Qiao
- Key Laboratory of Jiangsu Province, Medical College, Jiangsu University, Zhenjiang, 212013, PR China
| | - Xiaolan Liu
- Key Laboratory of Jiangsu Province, Medical College, Jiangsu University, Zhenjiang, 212013, PR China
| | - Yan Wang
- Key Laboratory of Jiangsu Province, Medical College, Jiangsu University, Zhenjiang, 212013, PR China
| | - Yuhan Li
- Key Laboratory of Jiangsu Province, Medical College, Jiangsu University, Zhenjiang, 212013, PR China
| | - Lulu Wang
- Key Laboratory of Jiangsu Province, Medical College, Jiangsu University, Zhenjiang, 212013, PR China
| | - Qingru Yang
- Key Laboratory of Jiangsu Province, Medical College, Jiangsu University, Zhenjiang, 212013, PR China
| | - Hua Wang
- Key Laboratory of Jiangsu Province, Medical College, Jiangsu University, Zhenjiang, 212013, PR China
| | - Hongxing Shen
- Key Laboratory of Jiangsu Province, Medical College, Jiangsu University, Zhenjiang, 212013, PR 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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Simonart T, Lam Hoai XL, de Maertelaer V. Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis. JMIR DERMATOLOGY 2022; 5:e35034. [PMID: 37632891 PMCID: PMC10334945 DOI: 10.2196/35034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 08/24/2022] [Accepted: 09/11/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Most common viral skin infections are not reportable conditions. Studying the population dynamics of these viral epidemics using traditional field methods is costly and time-consuming, especially over wide geographical areas. OBJECTIVE This study aimed to explore the evolution, seasonality, and distribution of vaccinable and nonvaccinable viral skin infections through an analysis of Google Trends. METHODS Worldwide search trends from January 2004 through May 2021 for viral skin infections were extracted from Google Trends, quantified, and analyzed. RESULTS Time series decomposition showed that the total search term volume for warts; zoster; roseola; measles; hand, foot, and mouth disease (HFMD); varicella; and rubella increased worldwide over the study period, whereas the interest for Pityriasis rosea and herpes simplex decreased. Internet searches for HFMD, varicella, and measles exhibited the highest seasonal patterns. The interest for measles and rubella was more pronounced in African countries, whereas the interest for HFMD and roseola was more pronounced in East Asia. CONCLUSIONS Harnessing data generated by web searches may increase the efficacy of traditional surveillance systems and strengthens the suspicion that the incidence of some vaccinable viral skin infections such as varicella, measles, and rubella may be globally increasing, whereas the incidence of common nonvaccinable skin infections remains stable.
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Affiliation(s)
- Thierry Simonart
- Department of Dermatology, Delta Hospital, Centre Hospitalier Interrégional Edith Cavell, Université Libre de Bruxelles, Brussels, Belgium
| | - Xuân-Lan Lam Hoai
- Department of Dermatology, St Pierre - Brugmann - Hôpital Universitaire des Enfants Reine Fabiola University Hospitals, Université Libre de Bruxelles, Brussels, Belgium
| | - Viviane de Maertelaer
- Department of Biostatistics, Institut de Recherche Interdisciplinaire en Biologie Humaine et Moléculaire, Université Libre de Bruxelles, Brussels, Belgium
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