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McClymont H, Lambert SB, Barr I, Vardoulakis S, Bambrick H, Hu W. Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic. J Epidemiol Glob Health 2024:10.1007/s44197-024-00272-y. [PMID: 39141074 DOI: 10.1007/s44197-024-00272-y] [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: 04/04/2024] [Accepted: 06/26/2024] [Indexed: 08/15/2024] Open
Abstract
The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.
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Affiliation(s)
- Hannah McClymont
- Ecosystem Change and Population Health (ECAPH) Research Group, School of Public Health and Social Work, Queensland University of Technology (QUT), Brisbane, Australia
| | - Stephen B Lambert
- Communicable Diseases Branch, Queensland Health, Brisbane, Australia
- National Centre for Immunisation Research and Surveillance, Sydney Children's Hospitals Network, Westmead, Australia
| | - Ian Barr
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Australia
| | - Sotiris Vardoulakis
- Health Research Institute, University of Canberra, Canberra, Australia
- Healthy Environments and Lives (HEAL) National Research Network, Canberra, Australia
| | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health (ECAPH) Research Group, School of Public Health and Social Work, Queensland University of Technology (QUT), Brisbane, Australia.
- Healthy Environments and Lives (HEAL) National Research Network, Canberra, Australia.
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Haque S, Mengersen K, Barr I, Wang L, Yang W, Vardoulakis S, Bambrick H, Hu W. Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations. ENVIRONMENTAL RESEARCH 2024; 249:118568. [PMID: 38417659 DOI: 10.1016/j.envres.2024.118568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.
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Affiliation(s)
- Shovanur Haque
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; Centre for Data Science (CDS), Queensland University of Technology (QUT), Brisbane, Australia
| | - Ian Barr
- World Health Organization Collaborating Centre for Reference and Research on Influenza, VIDRL, Doherty Institute, Melbourne, Australia; Department of Microbiology and Immunology, University of Melbourne, Victoria, Australia
| | - Liping Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious disease, Chinese Centre for Disease Control and Prevention, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Sotiris Vardoulakis
- HEAL Global Research Centre, Health Research Institute, University of Canberra, ACT Canberra, 2601, Australia
| | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, The Australian National University, ACT 2601 Canberra, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
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McClymont H, Si X, Hu W. Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model. Heliyon 2023; 9:e13782. [PMID: 36845036 PMCID: PMC9941072 DOI: 10.1016/j.heliyon.2023.e13782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/23/2023] Open
Abstract
Background Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy. Methods COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R eff over the Melbourne Delta outbreak. Results Case-only ARIMA model resulted in an R squared (R2) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R2 0.948, RMSE 137.57, and MAPE 21.26. Conclusion Multivariable ARIMA modelling for COVID-19 cases and R eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.
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Mavragani A, Li G, Yang J, Zhang T, Du J, Liu T, Zhang X, Han X, Li W, Ma L, Feng L, Yang W. Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation. J Med Internet Res 2023; 25:e44238. [PMID: 36780207 PMCID: PMC9972203 DOI: 10.2196/44238] [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: 11/11/2022] [Revised: 12/21/2022] [Accepted: 01/10/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. OBJECTIVE The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. METHODS We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. RESULTS The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. CONCLUSIONS The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.
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Affiliation(s)
| | - Gang Li
- Beijing Centre for Disease Prevention and Control, Beijing, China
| | - Jin Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jing Du
- Beijing Centre for Disease Prevention and Control, Beijing, China
| | - Tian Liu
- Jingzhou Center for Disease Control and Prevention, Jingzhou, China
| | - Xingxing Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xuan Han
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Wei Li
- The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Libing Ma
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Wang Z, Zhang W, Lu N, Lv R, Wang J, Zhu C, Ai L, Mao Y, Tan W, Qi Y. A potential tool for predicting epidemic trends and outbreaks of scrub typhus based on Internet search big data analysis in Yunnan Province, China. Front Public Health 2022; 10:1004462. [PMID: 36530696 PMCID: PMC9751444 DOI: 10.3389/fpubh.2022.1004462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 11/11/2022] [Indexed: 12/04/2022] Open
Abstract
Introduction Scrub typhus, caused by Orientia tsutsugamushi, is a neglected tropical disease. The southern part of China is considered an important epidemic and conserved area of scrub typhus. Although a surveillance system has been established, the surveillance of scrub typhus is typically delayed or incomplete and cannot predict trends in morbidity. Internet search data intuitively expose the public's attention to certain diseases when used in the public health area, thus reflecting the prevalence of the diseases. Methods In this study, based on the Internet search big data and historical scrub typhus incidence data in Yunnan Province of China, the autoregressive integrated moving average (ARIMA) model and ARIMA with external variables (ARIMAX) model were constructed and compared to predict the scrub typhus incidence. Results The results showed that the ARIMAX model produced a better outcome than the ARIMA model evaluated by various indexes and comparisons with the actual data. Conclusions The study demonstrates that Internet search big data can enhance the traditional surveillance system in monitoring and predicting the prevalence of scrub typhus and provides a potential tool for monitoring epidemic trends of scrub typhus and early warning of its outbreaks.
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Affiliation(s)
- Zixu Wang
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Bengbu Medical College, Bengbu, China
| | - Wenyi Zhang
- Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Nianhong Lu
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Nanjing Bioengineering (Gene) Technology Center for Medicines, Nanjing, China
| | - Ruichen Lv
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Nanjing Bioengineering (Gene) Technology Center for Medicines, Nanjing, China
| | - Junhu Wang
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Nanjing Bioengineering (Gene) Technology Center for Medicines, Nanjing, China
| | - Changqiang Zhu
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Nanjing Bioengineering (Gene) Technology Center for Medicines, Nanjing, China
| | - Lele Ai
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Nanjing Bioengineering (Gene) Technology Center for Medicines, Nanjing, China
| | - Yingqing Mao
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Nanjing Bioengineering (Gene) Technology Center for Medicines, Nanjing, China
| | - Weilong Tan
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Nanjing Bioengineering (Gene) Technology Center for Medicines, Nanjing, China,*Correspondence: Weilong Tan
| | - Yong Qi
- Huadong Research Institute for Medicine and Biotechniques, Nanjing, China,Nanjing Bioengineering (Gene) Technology Center for Medicines, Nanjing, China,Yong Qi
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Gao C, Zhang R, Chen X, Yao T, Song Q, Ye W, Li P, Wang Z, Yi D, Wu Y. Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission. NPJ Digit Med 2022; 5:161. [PMID: 36307547 DOI: 10.1038/s41746-022-00704-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 10/07/2022] [Indexed: 11/09/2022] Open
Abstract
With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data. We use statistical classification and regression to construct an early warning model for occurrence and development. Under the guidance of the improved coronavirus herd immunity optimizer (ICHIO), we construct a "sampling-feature-hyperparameter-weight" synchronous optimization strategy. In occurrence warning, we propose an undersampling synchronous evolutionary ensemble (USEE); in development warning, we propose a bootstrap-sampling synchronous evolutionary ensemble (BSEE). Regarding the internal training data (Heilongjiang Province), the ROC-AUC of USEE3 incorporating multisource data is 0.9553, the PR-AUC is 0.8327, and the R2 of BSEE2 fused by the "nonlinear + linear" method is 0.8698. Regarding the external validation data (Shaanxi Province), the ROC-AUC and PR-AUC values of USEE3 were 0.9680 and 0.9548, respectively, and the R2 of BSEE2 was 0.8255. Our method has good accuracy and generalization and can be flexibly used in the prediction of cryptic transmission in various regions. We propose strategy research that integrates multiple early warning tasks based on multisource Internet big data and combines multiple ensemble models. It is an extension of the research in the field of traditional infectious disease monitoring and has important practical significance and innovative theoretical value.
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Affiliation(s)
- Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China
| | - Rui Zhang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China
| | - Tianhua Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China
| | - Qiuyue Song
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China
| | - Wei Ye
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China
| | - PengPeng Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China
| | - Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
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Xu L, Zhou C, Luo S, Chan DK, McLaws ML, Liang W. Modernising infectious disease surveillance and an early-warning system: The need for China's action. THE LANCET REGIONAL HEALTH - WESTERN PACIFIC 2022; 23:100485. [PMID: 35685717 PMCID: PMC9168420 DOI: 10.1016/j.lanwpc.2022.100485] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Masinaei M. Estimating the seasonally varying effect of meteorological factors on the district-level incidence of acute watery diarrhea among under-five children of Iran, 2014-2018: a Bayesian hierarchical spatiotemporal model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2022; 66:1125-1144. [PMID: 35288786 DOI: 10.1007/s00484-022-02263-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 05/16/2023]
Abstract
Under-five years old acute watery diarrhea (U5AWD) accounts for most diarrheal diseases' burden, but little is known about the adjusted effect of meteorological and socioeconomic determinants. A dataset containing the seasonal numbers of U5AWD cases at the district level of Iran is collected through MOHME. Accordingly, the district-level standardized incidence ratio and Moran's I values are calculated to detect the significant clusters of U5AWD over sixteen seasons from 2014 to 2018. Additionally, the author tested twelve Bayesian hierarchical models in order to determine which one was the most accurate at forecasting seasonal number of incidents. Iran features a number of U5AWD hotspots, particularly in the southeast. An extended spatiotemporal model with seasonally varying coefficients and space-time interaction outperformed other models, and so became the paper's proposal in modeling U5AWD. Temperature demonstrated a global positive connection with seasonal U5AWD in districts (IRR: 1.0497; 95% CrI: 1.0254-1.0748), owing to its varying effects during the winter ((IRR: 1.0877; 95% CrI: 1.0408-1.1375) and fall (IRR: 1.0866; 95% CrI: 1.0405-1.1357) seasons. Also, elevation (IRR: 0.9997; 95% CrI: 0.9996-0.9998), piped drinking water (IRR: 0.9948; 95% CrI: 0.9933-0.9964), public sewerage network (IRR: 0.9965; 95% CrI: 0.9938-0.9992), years of schooling (IRR: 0.9649; 95% CrI: 0.944-0.9862), infrastructure-to-household size ratio (IRR: 0.9903; 95% CrI: 0.986-0.9946), wealth index (IRR: 0.9502; 95% CrI: 0.9231-0.9781), and urbanization (IRR: 0.9919; 95% CrI: 0.9893-0.9944) of districts were negatively associated with seasonal U5AWD incidence. Strategically, developing geoinformation alarm systems based on meteorological data might help predict U5AWD high-risk areas. The study also anticipates increased rates of U5AWD in districts with poor sanitation and socioeconomic level. Therefore, governments should take appropriate preventative actions in these sectors.
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Affiliation(s)
- Masoud Masinaei
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
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Future perspectives of emerging infectious diseases control: A One Health approach. One Health 2022; 14:100371. [PMID: 35075433 PMCID: PMC8770246 DOI: 10.1016/j.onehlt.2022.100371] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/16/2022] [Accepted: 01/17/2022] [Indexed: 01/04/2023] Open
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