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Le TT, Nguyen HT, Vu PT, Le DC, Nguyen TK, Hoang VT, Duong KL, Dao TL. Space-time scanning statistics in the prediction and evaluation of dengue epidemic clusters. IJID REGIONS 2024; 13:100441. [PMID: 39351397 PMCID: PMC11440294 DOI: 10.1016/j.ijregi.2024.100441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/29/2024] [Accepted: 08/30/2024] [Indexed: 10/04/2024]
Abstract
Objectives To detect clusters of dengue hemorrhagic fever in an urbanized district of Hai Phong City, Vietnam using Poisson space-time retrospective and prospective analysis. Methods A cross-sectional and retrospective study analyzed dengue surveillance data in the period from January 01, 2018, to December 31, 2022. Spatial-temporal scanning statistics were performed using the free software SatScan v10.1.2. Results A total of 519 cases were recorded. The cumulative incidence per 100,000 inhabitants was 3.37, 127.36, 10.96, 0, and 296.04 in 2018, 2019, 2020, 2021, and 2022, respectively. By retrospective Poisson model-based analysis, seven clusters were detected. Six of these seven detected outbreaks occurred in November and December 2022. The largest cluster had a relative risk (RR) of 1539.5 (P <0.00001). The smallest cluster has a RR of 316.1 (P = 0.006). Prospective analysis using the Poisson model significantly detected four active case clusters at the time of the study. The largest cluster of cases with RR was 47.7 (P <0.00001) and the smallest cluster with RR was 18.2 (P <0.00001). Conclusions This study provides a basis for improving the effectiveness of interventions and conducting further investigations into risk factors in the study area, as well as in other urban and suburban areas nationwide.
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Affiliation(s)
- Thi Thanh Le
- Thai Binh University of Medicine and Pharmacy, Thai Binh, Vietnam
- Hai An District Medical Center, Hai Phong, Vietnam
| | - Hai Tuan Nguyen
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Phong Tuc Vu
- Thai Binh University of Medicine and Pharmacy, Thai Binh, Vietnam
| | - Duc Cuong Le
- Thai Binh University of Medicine and Pharmacy, Thai Binh, Vietnam
| | | | - Van Thuan Hoang
- Thai Binh University of Medicine and Pharmacy, Thai Binh, Vietnam
| | - Khanh Linh Duong
- Thai Binh University of Medicine and Pharmacy, Thai Binh, Vietnam
| | - Thi Loi Dao
- Thai Binh University of Medicine and Pharmacy, Thai Binh, Vietnam
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2
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Strika Z, Petkovic K, Likic R, Batenburg R. Bridging healthcare gaps: a scoping review on the role of artificial intelligence, deep learning, and large language models in alleviating problems in medical deserts. Postgrad Med J 2024:qgae122. [PMID: 39323384 DOI: 10.1093/postmj/qgae122] [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: 04/14/2024] [Revised: 08/08/2024] [Accepted: 09/04/2024] [Indexed: 09/27/2024]
Abstract
"Medical deserts" are areas with low healthcare service levels, challenging the access, quality, and sustainability of care. This qualitative narrative review examines how artificial intelligence (AI), particularly large language models (LLMs), can address these challenges by integrating with e-Health and the Internet of Medical Things to enhance services in under-resourced areas. It explores AI-driven telehealth platforms that overcome language and cultural barriers, increasing accessibility. The utility of LLMs in providing diagnostic assistance where specialist deficits exist is highlighted, demonstrating AI's role in supplementing medical expertise and improving outcomes. Additionally, the development of AI chatbots offers preliminary medical advice, serving as initial contact points in remote areas. The review also discusses AI's role in enhancing medical education and training, supporting the professional development of healthcare workers in these regions. It assesses AI's strategic use in data analysis for effective resource allocation, identifying healthcare provision gaps. AI, especially LLMs, is seen as a promising solution for bridging healthcare gaps in "medical deserts," improving service accessibility, quality, and distribution. However, continued research and development are essential to fully realize AI's potential in addressing the challenges of medical deserts.
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Affiliation(s)
- Zdeslav Strika
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
| | - Karlo Petkovic
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
| | - Robert Likic
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
- Department of Internal Medicine, Division of Clinical Pharmacology, Clinical Hospital Centre Zagreb, Kispaticeva 12, Zagreb 10000, Croatia
| | - Ronald Batenburg
- Netherlands Institute for Health Services Research (NIVEL), Otterstraat 118, Utrecht 3553, The Netherlands
- Department of Sociology, Radboud University, Thomas Van Aquinostraat 4, Nijmegen 6524, The Netherlands
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Nguyen PHN. Data-driven nexus between malaria incidence and World Bank indicators in the Mekong River during 2000-2022. PLOS GLOBAL PUBLIC HEALTH 2024; 4:e0003764. [PMID: 39312524 PMCID: PMC11419363 DOI: 10.1371/journal.pgph.0003764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 08/29/2024] [Indexed: 09/25/2024]
Abstract
The increase in hydro dams in the Mekong River amidst the prevalence of multidrug-resistant malaria in Cambodia has raised concerns about global public health. Political conflicts during Covid-19 pandemic led cross-border movements of malaria cases from Myanmar and caused health care burden in Thailand. While previous publications used climatic indicators for predicting mosquito-borne diseases, this research used globally recognizable World Bank indicators to find the most impactful indicators related with malaria and shed light on the predictability of mosquito-borne diseases. The World Bank datasets of the World Development Indicators and Climate Change Knowledge Portal contain 1494 time series indicators. They were stepwise screened by Pearson and Distance correlation. The sets of five and four contain respectively 19 and 149 indicators highly correlated with malaria incidence which were found similarly among five and four GMS countries. Living areas, ages, career, income, technology accessibility, infrastructural facilities, unclean fuel use, tobacco smoking, and health care deficiency have affected malaria incidence. Tonle Sap Lake, the largest freshwater lake in Southeast Asia, could contribute to the larval habitat. Seven groups of indicator topics containing 92 indicators with not-null datapoints were analyzed by regression models, including Multiple Linear, Ridge, Lasso, and Elastic Net models to choose 7 crucial features for malaria prediction via Long Short Time Memory network. The indicator of people using at least basic sanitation services and people practicing open defecation were health factors had most impacts on regression models. Malaria incidence could be predicted by one indicator to reach the optimal mean absolute error which was lower than 10 malaria cases (per 1,000 population at risk) in the Long Short Time Memory model. However, public health crises caused by political problems should be analyzed by political indexes for more precise predictions.
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Affiliation(s)
- Phuong Hoang Ngoc Nguyen
- Department of Plant Biotechnology and Biotransformation, Faculty of Biology and Biotechnology, University of Science, Vietnam National University of Ho Chi Minh City (VNUHCM-US), Ho Chi Minh City, Vietnam
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4
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El Morr C, Ozdemir D, Asdaah Y, Saab A, El-Lahib Y, Sokhn ES. AI-based epidemic and pandemic early warning systems: A systematic scoping review. Health Informatics J 2024; 30:14604582241275844. [PMID: 39172555 DOI: 10.1177/14604582241275844] [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] [Indexed: 08/24/2024]
Abstract
Background: Timely detection of disease outbreaks is critical in public health. Artificial Intelligence (AI) can identify patterns in data that signal the onset of epidemics and pandemics. This scoping review examines the effectiveness of AI in epidemic and pandemic early warning systems (EWS). Objective: To assess the capability of AI-based systems in predicting epidemics and pandemics and to identify challenges and strategies for improvement. Methods: A systematic scoping review was conducted. The review included studies from the last 5 years, focusing on AI and machine learning applications in EWS. After screening 1087 articles, 33 were selected for thematic analysis. Results: The review found that AI-based EWS have been effectively implemented in various contexts, using a range of algorithms. Key challenges identified include data quality, model explainability, bias, data volume, velocity, variety, availability, and granularity. Strategies for mitigating AI bias and improving system adaptability were also discussed. Conclusion: AI has shown promise in enhancing the speed and accuracy of epidemic detection. However, challenges related to data quality, bias, and model transparency need to be addressed to improve the reliability and generalizability of AI-based EWS. Continuous monitoring and improvement, as well as incorporating social and environmental data, are essential for future development.
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Affiliation(s)
- Christo El Morr
- School of Health Policy and Management, York University, Toronto, ON, Canada
| | - Deniz Ozdemir
- Department of Psychology, York University, Toronto, ON, Canada
| | - Yasmeen Asdaah
- School of Health Policy and Management, York University, Toronto, ON, Canada
| | - Antoine Saab
- Quality and Safety Department, Lebanese Hospital Geitaoui-UMC, Beirut, Lebanon
| | - Yahya El-Lahib
- Faculty of Social Work, University of Calgary, Calgary, AB, Canada
| | - Elie Salem Sokhn
- Laboratory Department, Lebanese Hospital-Geitaoui University Medical Center, Beirut, Lebanon
- Molecular testing Laboratory, Medical Laboratory Department, Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
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5
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Mumtaz Z, Rashid Z, Saif R, Yousaf MZ. Deep learning guided prediction modeling of dengue virus evolving serotype. Heliyon 2024; 10:e32061. [PMID: 38882365 PMCID: PMC11177124 DOI: 10.1016/j.heliyon.2024.e32061] [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: 03/01/2024] [Revised: 05/28/2024] [Accepted: 05/28/2024] [Indexed: 06/18/2024] Open
Abstract
Evolution remains an incessant process in viruses, allowing them to elude the host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Emerging and re-emerging diseases are among the significant public health concerns globally. The revival of dengue is mainly due to the potential for naturally arising mutations to induce genotypic alterations in serotypes. These transformations could lead to future outbreaks, underscoring the significance of studying DENV evolution in endemic regions. Predicting the emerging Dengue Virus (DENV) genome is crucial as the virus disrupts host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging DENV serotypes. While Recurrent Neural Networks (RNN) were initially designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing the contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generated sequences were analyzed along with available DENV serotype sequences to find conserved motifs in the genome through MEME Suite (version 5.5.5). The generative model showed an accuracy of 93 %, and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score, all to be around 99.00 %, demonstrating the classification model's reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99 % similarity to DENV-4 and around 63-65 % similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum of 90 % similarity underscored their uniqueness.
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Affiliation(s)
- Zilwa Mumtaz
- KAM School of Life Sciences, Forman Christian College University, Ferozpur Road, Lahore, Pakistan
| | - Zubia Rashid
- Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Rashid Saif
- Department of Biotechnology, Qarshi University, Lahore, Pakistan
| | - Muhammad Zubair Yousaf
- KAM School of Life Sciences, Forman Christian College University, Ferozpur Road, Lahore, Pakistan
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Abbas S, Abbas M, Alam A, Hussain N, Irshad M, Khaliq M, Han X, Hafeez F, Romano D, Chen RZ. Mitigating dengue incidence through advanced Aedes larval surveillance and control: A successful experience from Pakistan. BULLETIN OF ENTOMOLOGICAL RESEARCH 2024; 114:444-453. [PMID: 38769861 DOI: 10.1017/s0007485324000269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Dengue fever is a viral disease caused by one of four dengue stereotypes (Flavivirus: Flaviviridae) that are primarily transmitted by Aedes albopictus (Skuse) and Aedes aegypti (L.). To safeguard public health, it is crucial to conduct surveys that examine the factors favouring the presence of these species. Our study surveyed 42 councils across four towns within the Bhakkar district of Punjab Province, by inspecting man-made or natural habitats containing standing water. First, door-to-door surveillance teams from the district health department were assigned to each council to surveillance Aedes species and dengue cases. Second, data collection through surveillance efforts, and validation procedures were implemented, and the verified data was uploaded onto the Dengue Tracking System by Third Party Validation teams. Third, data were analysed to identify factors influencing dengue fever cases. The findings demonstrated the following: (1) Predominantly, instances were discerned among individuals who had a documented history of having travelled beyond the confines of the province. (2) Containers associated with evaporative air coolers and tyre shops were responsible for approximately 30% of the Aedes developmental sites. (4) Variability in temperature was responsible for approximately 45% of the observed differences in the quantity of recorded Aedes mosquito developmental sites. (5) Implementation of dengue prevention initiatives precipitated a 50% reduction in Aedes-positive containers, alongside a notable 70% decline in reported cases of dengue fever during the period spanning 2019 to 2020, while the majority of reported cases were of external origin. Aedes control measures substantially curtailed mosquito populations and lowered vector-virus interactions. Notably, local dengue transmission was eliminated through advanced and effective Aedes control efforts, emphasising the need for persistent surveillance and eradication of larval habitats in affected regions.
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Affiliation(s)
- Sohail Abbas
- College of Plant Protection, Jilin Agricultural University, Changchun, Jilin, 130118, PR China
| | - Muneer Abbas
- Arid Zone Research Institute, Bhakkar, Punjab 30004, Pakistan
| | - Aleena Alam
- College of Plant Protection, Jilin Agricultural University, Changchun, Jilin, 130118, PR China
| | - Niaz Hussain
- Arid Zone Research Institute, Bhakkar, Punjab 30004, Pakistan
| | - Muhammad Irshad
- Arid Zone Research Institute, Bhakkar, Punjab 30004, Pakistan
| | - Mudassar Khaliq
- Arid Zone Research Institute, Bhakkar, Punjab 30004, Pakistan
| | - Xiao Han
- College of Plant Protection, Jilin Agricultural University, Changchun, Jilin, 130118, PR China
| | - Faisal Hafeez
- Entomological Research Institute, Ayub Agricultural Research Institute, Faisalabad, Punjab 38000, Pakistan
| | - Donato Romano
- The BioRobotics Institute & Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, 56127, Pisa, Italy
| | - Ri Zhao Chen
- College of Plant Protection, Jilin Agricultural University, Changchun, Jilin, 130118, PR China
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7
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Fang L, Hu W, Pan G. Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:401-410. [PMID: 38150020 DOI: 10.1007/s00484-023-02605-1] [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: 08/30/2022] [Revised: 09/18/2023] [Accepted: 12/13/2023] [Indexed: 12/28/2023]
Abstract
In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted a systematic review to summarize machine learning methods and predictors in previous studies. We searched PubMed, ScienceDirect, and Web of Science databases for articles published up to July 2023. The selected papers included not only the forecast of dengue incidence but also machine learning methods. A total of 23 papers were included in this study. Predictive factors included meteorological factors (22, 95.7%), historical dengue data (14, 60.9%), environmental factors (4, 17.4%), socioeconomic factors (4, 17.4%), vector surveillance data (2, 8.7%), and internet search data (3, 13.0%). Among meteorological factors, temperature (20, 87.0%), rainfall (20, 87.0%), and relative humidity (14, 60.9%) were the most commonly used. We found that Support Vector Machine (SVM) (6, 26.1%), Long Short-Term Memory (LSTM) (5, 21.7%), Random Forest (RF) (4, 17.4%), Least Absolute Shrinkage and Selection Operator (LASSO) (2, 8.7%), ensemble model (2, 8.7%), and other models (4, 17.4%) were identified as the best models based on evaluation metrics used in each article. These results indicate that meteorological factors are important predictors that cannot be ignored and SVM and LSTM algorithms are the most commonly used models in dengue fever prediction with good predictive performance. This review will contribute to the development of more robust early dengue warning systems and promote the application of machine learning methods in predicting climate-related infectious diseases.
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Affiliation(s)
- Lanlan Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Wan Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Guixia Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
- The Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, Hefei, China.
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8
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Snyder J, Maglasang G. Dengue in Cebu City, Philippines: A Pilot Study of Predictive Models and Visualizations for Public Health. Am J Trop Med Hyg 2024; 110:179-187. [PMID: 38081048 PMCID: PMC10793016 DOI: 10.4269/ajtmh.23-0250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 09/25/2023] [Indexed: 01/05/2024] Open
Abstract
Dengue is a global health issue, particularly in the tropical and subtropical regions of the world. Prevention is the most appropriate method to fight the spread of the virus. The objective of this research is to present a model, along with visualizations, that will enable health officials and community leaders to identify when and where potential dengue outbreaks are likely to occur. Armed with this information, local resources can be adequately deployed in an effort to use limited supplies effectively. A mathematical model that uses easily obtainable data, along with visualizations for the 80 barangays of Cebu City, Philippines, is presented. Visualizations are constructed appropriate for a generalist audience to comprehend and use for dengue mitigation. Results of this study include a model that uses readily available data to predict dengue outbreaks one month in advance and visualizations appropriate for decision-makers in public health. Additional items are identified that could enhance the explanatory power of the model, and future directions are discussed.
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Affiliation(s)
- Johnny Snyder
- Davis School of Business, Colorado Mesa University, Grand Junction, Colorado
| | - Gibson Maglasang
- Research Institute for Computational Mathematics and Physics, Cebu Normal University, Cebu City, Philippines
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Pillay MT, Minakawa N, Kim Y, Kgalane N, Ratnam JV, Behera SK, Hashizume M, Sweijd N. Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model. Sci Rep 2023; 13:23091. [PMID: 38155182 PMCID: PMC10754862 DOI: 10.1038/s41598-023-50176-3] [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: 07/25/2023] [Accepted: 12/15/2023] [Indexed: 12/30/2023] Open
Abstract
Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer's performance of 80% according to AUROC was 20-40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.
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Affiliation(s)
- Micheal T Pillay
- Department of Vector Ecology and Environment, Institute of Tropical Medicine (NEKKEN), Nagasaki University, 1-12-4, Sakamoto, Nagasaki City, 852-8523, Japan.
- Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki City, Japan.
| | - Noboru Minakawa
- Department of Vector Ecology and Environment, Institute of Tropical Medicine (NEKKEN), Nagasaki University, 1-12-4, Sakamoto, Nagasaki City, 852-8523, Japan
| | - Yoonhee Kim
- Department of Global Environmental Health, Graduate School of Medicine, The University of Tokyo: The University of Tokyo, 7-3-1 Hongo, Bunkyo Ward, Tokyo, 113-8654, Japan
| | - Nyakallo Kgalane
- Limpopo Department of Health, Malaria Control: 18 College Street, Polokwane, 0700, South Africa
| | - Jayanthi V Ratnam
- Application Laboratory, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25, Showa-Machi, Kanazawa-Ku, Yokohama-City, Kanagawa, 236-0001, Japan
| | - Swadhin K Behera
- Application Laboratory, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25, Showa-Machi, Kanazawa-Ku, Yokohama-City, Kanagawa, 236-0001, Japan
| | - Masahiro Hashizume
- Graduate School of Medicine Department of Global Health Policy, The University of Tokyo: The University of Tokyo, 7-3-1 Hongo, Bunkyo Ward, Tokyo, 113-8654, Japan
| | - Neville Sweijd
- Alliance for Collaboration on Climate & Earth Systems Science (ACCESS), CSIR, Lower Hope Road, Rosebank, 770, Cape Town, South Africa
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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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11
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Geng X, Ma Y, Cai W, Zha Y, Zhang T, Zhang H, Yang C, Yin F, Shui T. Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China. PLoS Negl Trop Dis 2023; 17:e0011587. [PMID: 37683009 PMCID: PMC10511093 DOI: 10.1371/journal.pntd.0011587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/20/2023] [Accepted: 08/11/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control. METHODS We collected the daily number of HFMD cases among children aged 0-14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks. RESULTS From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM10. The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay. CONCLUSIONS The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors.
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Affiliation(s)
- Xiaoran Geng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yue Ma
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wennian Cai
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuanyi Zha
- Kunming Medical University, Kunming, China
| | - Tao Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huadong Zhang
- Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Changhong Yang
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Fei Yin
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Tiejun Shui
- Yunnan Center for Disease Control and Prevention, Kunming, China
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12
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Yang H, Nguyen TN, Chuang TW. An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases. Trop Med Infect Dis 2023; 8:tropicalmed8040238. [PMID: 37104363 PMCID: PMC10142856 DOI: 10.3390/tropicalmed8040238] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/06/2023] [Accepted: 04/18/2023] [Indexed: 04/28/2023] Open
Abstract
Dengue fever is a prevalent mosquito-borne disease that burdens communities in subtropical and tropical regions. Dengue transmission is ecologically complex; several environmental conditions are critical for the spatial and temporal distribution of dengue. Interannual variability and spatial distribution of dengue transmission are well-studied; however, the effects of land cover and use are yet to be investigated. Therefore, we applied an explainable artificial intelligence (AI) approach to integrate the EXtreme Gradient Boosting and Shapley Additive Explanation (SHAP) methods to evaluate spatial patterns of the residences of reported dengue cases based on various fine-scale land-cover land-use types, Shannon's diversity index, and household density in Kaohsiung City, Taiwan, between 2014 and 2015. We found that the proportions of general roads and residential areas play essential roles in dengue case residences with nonlinear patterns. Agriculture-related features were negatively associated with dengue incidence. Additionally, Shannon's diversity index showed a U-shaped relationship with dengue infection, and SHAP dependence plots showed different relationships between various land-use types and dengue incidence. Finally, landscape-based prediction maps were generated from the best-fit model and highlighted high-risk zones within the metropolitan region. The explainable AI approach delineated precise associations between spatial patterns of the residences of dengue cases and diverse land-use characteristics. This information is beneficial for resource allocation and control strategy modification.
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Affiliation(s)
- Hsiu Yang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Thi-Nhung Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Ting-Wu Chuang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
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13
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Majeed MA, Shafri HZM, Zulkafli Z, Wayayok A. A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4130. [PMID: 36901139 PMCID: PMC10002017 DOI: 10.3390/ijerph20054130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia.
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Affiliation(s)
- Mokhalad A. Majeed
- Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
| | - Helmi Zulhaidi Mohd Shafri
- Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
- Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
| | - Zed Zulkafli
- Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
| | - Aimrun Wayayok
- Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
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14
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Leung XY, Islam RM, Adhami M, Ilic D, McDonald L, Palawaththa S, Diug B, Munshi SU, Karim MN. A systematic review of dengue outbreak prediction models: Current scenario and future directions. PLoS Negl Trop Dis 2023; 17:e0010631. [PMID: 36780568 PMCID: PMC9956653 DOI: 10.1371/journal.pntd.0010631] [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: 07/05/2022] [Revised: 02/24/2023] [Accepted: 01/29/2023] [Indexed: 02/15/2023] Open
Abstract
Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.
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Affiliation(s)
- Xing Yu Leung
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rakibul M. Islam
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Mohammadmehdi Adhami
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Dragan Ilic
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Lara McDonald
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Shanika Palawaththa
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Basia Diug
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Saif U. Munshi
- Department of Virology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| | - Md Nazmul Karim
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- * E-mail:
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15
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Feng Y, Cui X, Lv J, Yan B, Meng X, Zhang L, Guo Y. Deep learning models for hepatitis E incidence prediction leveraging meteorological factors. PLoS One 2023; 18:e0282928. [PMID: 36913401 PMCID: PMC10010535 DOI: 10.1371/journal.pone.0282928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/27/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction. METHODS We extracted the monthly meteorological data, incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We employ GRA method to analyze the correlation between the incidence and meteorological factors. With these meteorological factors, we achieve a variety of methods for incidence of hepatitis E by LSTM and attention-based LSTM. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). RESULTS Duration of sunshine and rainfall-related factors(total rainfall, maximum daily rainfall) are more relevant to the incidence of hepatitis E than other factors. Without meteorological factors, we obtained 20.74%, 19.50% for incidence in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we obtained 14.74%, 12.91%, 13.21%, 16.83% for incidence, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.83%. Without meteorological factors, we achieved 20.41%, 19.39% for cases in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we achieved 14.20%, 12.49%, 12.72%, 15.73% for cases, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.92%. More detailed results are shown in results section of this paper. CONCLUSIONS The experiments show that attention-based LSTM is superior to other comparative models. Multivariate attention and temporal attention can greatly improve the prediction performance of the models. Among them, when all meteorological factors are used, multivariate attention performance is better. This study can provide reference for the prediction of other infectious diseases.
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Affiliation(s)
- Yi Feng
- Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Xiya Cui
- School of Data and Computer Science, Shandong Women’s Unversity, Jinan, Shandong, China
| | - Jingjing Lv
- Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Bingyu Yan
- Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Xin Meng
- Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Li Zhang
- Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China
- School of Public Health, Shandong University, Jinan, Shandong, China
- * E-mail: (LZ); (YG)
| | - Yanhui Guo
- School of Data and Computer Science, Shandong Women’s Unversity, Jinan, Shandong, China
- * E-mail: (LZ); (YG)
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16
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Li Z. Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13555. [PMID: 36294134 PMCID: PMC9603269 DOI: 10.3390/ijerph192013555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/15/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (Rsum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.
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Affiliation(s)
- Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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