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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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2
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Lim AY, Jafari Y, Caldwell JM, Clapham HE, Gaythorpe KAM, Hussain-Alkhateeb L, Johansson MA, Kraemer MUG, Maude RJ, McCormack CP, Messina JP, Mordecai EA, Rabe IB, Reiner RC, Ryan SJ, Salje H, Semenza JC, Rojas DP, Brady OJ. A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk. BMC Infect Dis 2023; 23:708. [PMID: 37864153 PMCID: PMC10588093 DOI: 10.1186/s12879-023-08717-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 10/16/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
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Affiliation(s)
- Ah-Young Lim
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
| | - Yalda Jafari
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jamie M Caldwell
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Hannah E Clapham
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Laith Hussain-Alkhateeb
- School of Public Health and Community Medicine, Sahlgrenska Academy, Institute of Medicine, Global Health, University of Gothenburg, Gothenburg, Sweden
- Population Health Research Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Michael A Johansson
- Dengue Branch, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA
| | | | - Richard J Maude
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Clare P McCormack
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Jane P Messina
- School of Geography and the Environment, University of Oxford, Oxford, UK
- Oxford School of Global and Area Studies, University of Oxford, Oxford, UK
| | - Erin A Mordecai
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Ingrid B Rabe
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Sadie J Ryan
- Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Jan C Semenza
- Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, Umeå, Sweden
| | - Diana P Rojas
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
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3
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Topuz K, Davazdahemami B, Delen D. A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases. ANNALS OF OPERATIONS RESEARCH 2023:1-25. [PMID: 37361089 PMCID: PMC10189691 DOI: 10.1007/s10479-023-05377-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/01/2023] [Indexed: 06/28/2023]
Abstract
During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.
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Affiliation(s)
- Kazim Topuz
- Collins College of Business, School of Finance and Operations Management, The University of Tulsa, Tulsa, USA
| | - Behrooz Davazdahemami
- Department of IT and Supply Chain Management, University of Wisconsin-Whitewater, 809 W. Starin Rd., Hyland Hall 1222, Whitewater, USA
| | - Dursun Delen
- Center for Health Systems Innovation, Spears School of Business, Oklahoma State University, Stillwater, USA
- Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey
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4
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Zio S, Lamien B, Tiemounou S, Adaman Y, Tougri I, Beidari M, Boris OWYS. Multi-outputs Gaussian process for predicting Burkina Faso COVID-19 spread using correlations from the weather parameters. Infect Dis Model 2022; 7:448-462. [PMID: 35845472 PMCID: PMC9270784 DOI: 10.1016/j.idm.2022.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/14/2022] [Accepted: 06/24/2022] [Indexed: 12/23/2022] Open
Abstract
The novel coronavirus has affected all regions of the world, but each country has experienced different rates of infection. In West Africa, in particular, infection rates remain low as compared to other parts of the world. This heterogeneity in the spread of COVID-19 raises a lot of questions that are still unanswered. However, some studies point out that people's mobility, size of gatherings, rate of testing, and weather have a great impact on the COVID-19 spread. In this work, we first evaluate the correlation between meteorological parameters and COVID-19 cases using Spearman's rank correlation. Secondly, multi-output Gaussian processes (MOGP) are used to predict the daily confirmed COVID-19 cases by exploring its relationships with meteorological parameters. The number of daily reported COVID-19 cases, as well as, weather variables collected from March 9, 2020, to October 18, 2021, were used in the analysis. The weather variables considered in the analysis are the mean temperature, relative humidity, wind direction, insolation, precipitation, and wind speed. The predicting model was constructed exploiting the correlation between the data of the daily confirmed COVID-19 cases and data of the weather variables. The results show that a significant correlation between the daily confirmed COVID-19 cases was found with humidity, wind direction, wind speed, and insolation. These parameters are used to construct the predictive model using the Multi-Output Gaussian process (MOGP). Different combinations of the data of meteorological parameters together with the data of daily reported COVID-19 cases were used to derive different models. We found that the best predictor is obtained using the combination of Humidity and insolation. This model is then used to predict the daily confirmed COVID-19 cases knowing the humidity and Insolation.
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Affiliation(s)
- Souleymane Zio
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Bernard Lamien
- Institut du Génie Industriel et Textile, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Sibiri Tiemounou
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Yoda Adaman
- Agence Nationale de la Météorologie du Burkina Faso, ANAM, Burkina Faso
| | - Inoussa Tougri
- Institut du Génie Industriel et Textile, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Mohamed Beidari
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Ouedraogo W Y S Boris
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
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de Lima CL, da Silva ACG, Moreno GMM, Cordeiro da Silva C, Musah A, Aldosery A, Dutra L, Ambrizzi T, Borges IVG, Tunali M, Basibuyuk S, Yenigün O, Massoni TL, Browning E, Jones K, Campos L, Kostkova P, da Silva Filho AG, dos Santos WP. Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review. Front Public Health 2022; 10:900077. [PMID: 35719644 PMCID: PMC9204152 DOI: 10.3389/fpubh.2022.900077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.
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Affiliation(s)
- Clarisse Lins de Lima
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil
| | - Ana Clara Gomes da Silva
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil
| | | | | | - Anwar Musah
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Aisha Aldosery
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Livia Dutra
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Tercio Ambrizzi
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Iuri V. G. Borges
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Merve Tunali
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Selma Basibuyuk
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Orhan Yenigün
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Tiago Lima Massoni
- Department of Systems and Computing, Federal University of Campina Grande, Campina Grande, Brazil
| | - Ella Browning
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Kate Jones
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Luiza Campos
- Department of Civil Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Patty Kostkova
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
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Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2022; 38:527-538. [PMID: 35136337 DOI: 10.12788/fp.0174] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development. Observations With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology. Conclusions AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.
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Affiliation(s)
- L Brannon Thomas
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Stephen M Mastorides
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | | | - Colleen E Jakey
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Andrew A Borkowski
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
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Simsek M, Kantarci B, Boukerche A, Khan S. Machine Learning-Backed Planning of Rapid COVID-19 Tests With Autonomous Vehicles With Zero-Day Considerations. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022. [DOI: 10.1109/tetci.2021.3131352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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8
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Ejaz H, McGrath H, Wong BLH, Guise A, Vercauteren T, Shapey J. Artificial intelligence and medical education: A global mixed-methods study of medical students’ perspectives. Digit Health 2022; 8:20552076221089099. [PMID: 35521511 PMCID: PMC9067043 DOI: 10.1177/20552076221089099] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 03/06/2022] [Indexed: 12/02/2022] Open
Abstract
Objective Medical students, as clinicians and healthcare leaders of the future, are key stakeholders in the clinical roll-out of artificial intelligence-driven technologies. The authors aim to provide the first report on the state of artificial intelligence in medical education globally by exploring the perspectives of medical students. Methods The authors carried out a mixed-methods study of focus groups and surveys with 128 medical students from 48 countries. The study explored knowledge around artificial intelligence as well as what students wished to learn about artificial intelligence and how they wished to learn this. A combined qualitative and quantitative analysis was used. Results Support for incorporating teaching on artificial intelligence into core curricula was ubiquitous across the globe, but few students had received teaching on artificial intelligence. Students showed knowledge on the applications of artificial intelligence in clinical medicine as well as on artificial intelligence ethics. They were interested in learning about clinical applications, algorithm development, coding and algorithm appraisal. Hackathon-style projects and multidisciplinary education involving computer science students were suggested for incorporation into the curriculum. Conclusions Medical students from all countries should be provided teaching on artificial intelligence as part of their curriculum to develop skills and knowledge around artificial intelligence to ensure a patient-centred digital future in medicine. This teaching should focus on the applications of artificial intelligence in clinical medicine. Students should also be given the opportunity to be involved in algorithm development. Students in low- and middle-income countries require the foundational technology as well as robust teaching on artificial intelligence to ensure that they can drive innovation in their healthcare settings.
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Affiliation(s)
- Hamza Ejaz
- Norwich Medical School, University of East Anglia, UK
- Psychological and Behavioural Sciences, London School of Economics, UK
| | - Hari McGrath
- GKT School of Medical Education, King’s College London, UK
- Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Brian LH Wong
- Department of International Health, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
- Secretariat, the Lancet and Financial Times Commission on Governing Health Futures 2030, Global Health Centre, The Graduate Institute, 1211 Geneva, Switzerland
- Steering Committee, Digital Health Section, European Public Health Association (EUPHA), Utrecht, Netherlands
| | - Andrew Guise
- School of Population Health and Environmental Sciences, King’s College London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
| | - Jonathan Shapey
- School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
- Department of Neurosurgery, King’s College Hospital, London, UK
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Recurrence and Driving Factors of Visceral Leishmaniasis in Central China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18189535. [PMID: 34574459 PMCID: PMC8468393 DOI: 10.3390/ijerph18189535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/02/2021] [Accepted: 09/08/2021] [Indexed: 01/17/2023]
Abstract
Visceral leishmaniasis (VL) is an important vector-borne zoonosis caused by Leishmania spp. that has been spreading in China. It has been posing a significant risk to public health in central China due to its recurrence in recent decades. Yet, the spatiotemporal patterns and the driving factors of VL in central China remain unclear at present. The purpose of this study was to analyse spatiotemporal distribution, explore driving factors, and provide novel insight into prevention and control countermeasures of the VL spreading in central China. Based on data of human VL cases from 2006 to 2019 obtained from the Chinese Centres for Disease Control and Prevention (CDC), we depicted the map showing the spatiotemporal distribution of VL in central China. We further explored the driving factors contributing to the spread of VL through the general additive model (GAM) by combining maps of environmental, meteorological, and socioeconomic correlates. Most VL cases were reported in Shaanxi and Shanxi provinces, the number of which has been increasing every year in the last 14 years, from 3 new cases in 2006 to 101 new cases in 2019. The results of GAM revealed that environmental (i.e., changes in grasslands/forests), meteorological (i.e., temperature and relative humidity), and socioeconomic (i.e., population density) factors are significantly associated with the prevalence of VL in central China. Our results provide a better understanding regarding the current situation and the driving factors of VL in central China, assisting in developing the disease prevention and control strategies implemented by public health authorities.
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Jonkmans N, D'Acremont V, Flahault A. Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases. BMJ Glob Health 2021; 6:e006623. [PMID: 34531189 PMCID: PMC8449939 DOI: 10.1136/bmjgh-2021-006623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/01/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The WHO's Research and Development Blueprint priority list designates emerging diseases with the potential to generate public health emergencies for which insufficient preventive solutions exist. The list aims to reduce the time to the availability of resources that can avert public health crises. The current SARS-CoV-2 pandemic illustrates that an effective method of mitigating such crises is the pre-emptive prediction of outbreaks. This scoping review thus aimed to map and identify the evidence available to predict future outbreaks of the Blueprint diseases. METHODS We conducted a scoping review of PubMed, Embase and Web of Science related to the evidence predicting future outbreaks of Ebola and Marburg virus, Zika virus, Lassa fever, Nipah and Henipaviral disease, Rift Valley fever, Crimean-Congo haemorrhagic fever, Severe acute respiratory syndrome, Middle East respiratory syndrome and Disease X. Prediction methods, outbreak features predicted and implementation of predictions were evaluated. We conducted a narrative and quantitative evidence synthesis to highlight prediction methods that could be further investigated for the prevention of Blueprint diseases and COVID-19 outbreaks. RESULTS Out of 3959 articles identified, we included 58 articles based on inclusion criteria. 5 major prediction methods emerged; the most frequent being spatio-temporal risk maps predicting outbreak risk periods and locations through vector and climate data. Stochastic models were predominant. Rift Valley fever was the most predicted disease. Diseases with complex sociocultural factors such as Ebola were often predicted through multifactorial risk-based estimations. 10% of models were implemented by health authorities. No article predicted Disease X outbreaks. CONCLUSIONS Spatiotemporal models for diseases with strong climatic and vectorial components, as in River Valley fever prediction, may currently best reduce the time to the availability of resources. A wide literature gap exists in the prediction of zoonoses with complex sociocultural and ecological dynamics such as Ebola, COVID-19 and especially Disease X.
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Affiliation(s)
- Nils Jonkmans
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Valérie D'Acremont
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
- Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Antoine Flahault
- Institute of Global Health, Faculty of Medicine, Université de Genève, Geneva, Switzerland
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Alfred R, Obit JH. The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review. Heliyon 2021; 7:e07371. [PMID: 34179541 PMCID: PMC8219638 DOI: 10.1016/j.heliyon.2021.e07371] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 10/20/2020] [Accepted: 06/17/2021] [Indexed: 12/03/2022] Open
Abstract
Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning. The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.
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Affiliation(s)
- Rayner Alfred
- Corresponding author. http://www.machineintelligencespace.com
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12
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Kao IH, Perng JW. Early prediction of coronavirus disease epidemic severity in the contiguous United States based on deep learning. RESULTS IN PHYSICS 2021; 25:104287. [PMID: 33996401 PMCID: PMC8105308 DOI: 10.1016/j.rinp.2021.104287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 06/12/2023]
Abstract
In November 2019, the coronavirus disease outbreak began, caused by the novel severe acute respiratory syndrome coronavirus 2. In just over two months, the unprecedented rapid spread resulted in more than 10,000 confirmed cases worldwide. This study predicted the infectious spread of coronavirus disease in the contiguous United States using a convolutional autoencoder with long short-term memory and compared its predictive performance with that of the convolutional autoencoder without long short-term memory. The epidemic data were obtained from the World Health Organization and the US Centers for Disease Control and Prevention from January 1st to April 6th, 2020. We used data from the first 366,607 confirmed cases in the United States. In this study, the data from the Centers for Disease Control and Prevention were gridded by latitude and longitude and the grids were categorized into six epidemic levels based on the number of confirmed cases. The input of the convolutional autoencoder with long short-term memory was the distribution of confirmed cases 14 days before, whereas the output was the distribution of confirmed cases 7 days after the date of testing. The mean square error in this model was 1.664, the peak signal-to-noise ratio was 55.699, and the structural similarity index was 0.99, which were better than those of the corresponding results of the convolutional autoencoder. These results showed that the convolutional autoencoder with long short-term memory effectively and reliably predicted the spread of infectious disease in the contiguous United States.
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Affiliation(s)
- I-Hsi Kao
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| | - Jau-Woei Perng
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
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13
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Li SL, Messina JP, Pybus OG, Kraemer MUG, Gardner L. A review of models applied to the geographic spread of Zika virus. Trans R Soc Trop Med Hyg 2021; 115:956-964. [PMID: 33570155 PMCID: PMC8417088 DOI: 10.1093/trstmh/trab009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 12/13/2020] [Accepted: 01/08/2021] [Indexed: 11/13/2022] Open
Abstract
In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015–2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector–human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.
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Affiliation(s)
- Sabrina L Li
- School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
| | - Jane P Messina
- School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.,School of Global and Area Studies, University of Oxford, 12 Bevington Road, Oxford, OX2 6LH, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, 11a Mansfield Rd, Oxford, OX1 3SZ, UK
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, 11a Mansfield Rd, Oxford, OX1 3SZ, UK
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218-2682, USA.,Center for Systems Science and Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218-2682, USA
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14
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Ribeiro MHDM, Mariani VC, Coelho LDS. Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods. J Biomed Inform 2020; 111:103575. [PMID: 32976990 PMCID: PMC7507988 DOI: 10.1016/j.jbi.2020.103575] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 09/10/2020] [Accepted: 09/16/2020] [Indexed: 12/23/2022]
Abstract
Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two, and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm – version II is employed to find a set of candidates’ weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE), and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model’s performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, and sMAPE criteria as well as Diebold–Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modeling developed in this paper is promising and can be used by managers to support decision making. Ensemble empirical mode decomposition is applied into the raw time series. Heterogeneous ensemble learning models are used to forecasting meningitis cases. The NSGA-II algorithm and TOPSIS criterion are employed in the multi-objective procedure. Proposed model has errors statistically lower than 89.17% of the compared models. Promising results are achieved by the weighted integrated ensemble learning model.
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Affiliation(s)
- Matheus Henrique Dal Molin Ribeiro
- Graduate Program in Industrial & Systems Engineering (PPGEPS), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, Parana, 80215-901, Brazil; Department of Mathematics, Federal Technological University of Parana (UTFPR), Via do Conhecimento, KM 01 - Fraron, Pato Branco, Parana, 85503-390, Brazil.
| | - Viviana Cocco Mariani
- Department of Electrical Engineering, Federal University of Parana (UFPR), 100, Avenida Cel. Francisco dos Santos, Curitiba, Parana, 81530-000, Brazil; Mechanical Engineering Graduate Program (PPGEM), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, Parana, 80215-901, Brazil
| | - Leandro Dos Santos Coelho
- Graduate Program in Industrial & Systems Engineering (PPGEPS), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, Parana, 80215-901, Brazil; Department of Electrical Engineering, Federal University of Parana (UFPR), 100, Avenida Cel. Francisco dos Santos, Curitiba, Parana, 81530-000, Brazil
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15
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Bansal A, Padappayil RP, Garg C, Singal A, Gupta M, Klein A. Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review. J Med Syst 2020; 44:156. [PMID: 32740678 PMCID: PMC7395799 DOI: 10.1007/s10916-020-01617-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/15/2020] [Indexed: 01/07/2023]
Abstract
The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.
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Affiliation(s)
- Agam Bansal
- Internal Medicine, Cleveland Clinic, Cleveland, OH USA
| | | | - Chandan Garg
- Deptartment of Statistics, Columbia University, New York, NY USA
| | - Anjali Singal
- Deptartment of Anatomy, All India Institute of Medical Sciences, Bathinda, India
| | - Mohak Gupta
- All India Institute of Medical Sciences, New Delhi, India
| | - Allan Klein
- Deptartment of Cardiology, Cleveland Clinic, Cleveland, OH USA
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16
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Pourghasemi HR, Pouyan S, Farajzadeh Z, Sadhasivam N, Heidari B, Babaei S, Tiefenbacher JP. Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models. PLoS One 2020; 15:e0236238. [PMID: 32722716 PMCID: PMC7386644 DOI: 10.1371/journal.pone.0236238] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 07/01/2020] [Indexed: 12/17/2022] Open
Abstract
Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.
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Affiliation(s)
- Hamid Reza Pourghasemi
- Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Soheila Pouyan
- Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Zakariya Farajzadeh
- Department of Agricultural Economics, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Nitheshnirmal Sadhasivam
- Department of Geography, School of Earth Science, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India
| | - Bahram Heidari
- Department of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Sedigheh Babaei
- Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
| | - John P. Tiefenbacher
- Department of Geography, Texas State University, San Marcos, Texas, United States of America
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17
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Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet 2020; 395:1579-1586. [PMID: 32416782 PMCID: PMC7255280 DOI: 10.1016/s0140-6736(20)30226-9] [Citation(s) in RCA: 206] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 02/07/2023]
Abstract
Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.
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Affiliation(s)
- Nina Schwalbe
- Heilbrunn Department of Population and Family Health, Columbia Mailman School of Public Health, New York, NY, USA; Spark Street Advisors, New York, NY, USA.
| | - Brian Wahl
- Spark Street Advisors, New York, NY, USA; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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18
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Simsek M, Kantarci B. Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17103437. [PMID: 32423150 PMCID: PMC7277766 DOI: 10.3390/ijerph17103437] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 05/06/2020] [Accepted: 05/11/2020] [Indexed: 12/23/2022]
Abstract
The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9–30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.
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19
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Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16234845. [PMID: 31810239 PMCID: PMC6926588 DOI: 10.3390/ijerph16234845] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 11/26/2019] [Accepted: 11/28/2019] [Indexed: 11/17/2022]
Abstract
Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.
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20
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Liang R, Lu Y, Qu X, Su Q, Li C, Xia S, Liu Y, Zhang Q, Cao X, Chen Q, Niu B. Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data. Transbound Emerg Dis 2019; 67:935-946. [PMID: 31738013 DOI: 10.1111/tbed.13424] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/28/2022]
Abstract
African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator-Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%-84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision-makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.
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Affiliation(s)
- Ruirui Liang
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Yi Lu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Xiaosheng Qu
- National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, Nanning, China
| | - Qiang Su
- School of Life Sciences, Shanghai University, Shanghai, China.,Computing Center of Guangxi, Nanning, China.,Guangxi Institute for Food and Drug Control, Nanning, China
| | - Chunxia Li
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Sijing Xia
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Yongxin Liu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Qiang Zhang
- Technical Center For Animal, Plant and Food Inspection and Quarantine of Shanghai Customs, Shanghai, China
| | - Xin Cao
- Institute of Clinical Science, Shanghai Medical College, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qin Chen
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Bing Niu
- School of Life Sciences, Shanghai University, Shanghai, China
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21
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Hossain MG, Nazir KHMNH, Saha S, Rahman MT. Zika virus: A possible emerging threat for Bangladesh! J Adv Vet Anim Res 2019; 6:575-582. [PMID: 31819889 PMCID: PMC6882728 DOI: 10.5455/javar.2019.f385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 10/21/2019] [Accepted: 10/23/2019] [Indexed: 12/23/2022] Open
Abstract
Zika virus, a member of Flaviviridae is the etiology of Zika or Zika fever or Zika virus (ZIKV) disease characterized by mild symptoms similar to very mild form of Dengue or Chikungunya. The virus transmits through Aedes mosquitoes, particularly by Aedes aegypti. The most dangerous effect of ZIKV infection is the ability of the virus to cause microcephaly and congenital malformation to the newborn baby if the mother is infected. The neurological disorders including Guillain-Barré syndrome might be associated with adults and children due to ZIKV infections. Zika has emerged as a serious global public health problem as it has been found in 87 countries, particularly in Africa, America, and Asia and has no vaccine and treatment so far. Bangladesh is at a high risk of ZIKV infection and we consider ZIKV as a possible emerging threat for Bangladesh. This short review summarizes the insights of ZIKV infection, present status of the disease in Bangladesh and its neighboring countries, and recommendations for necessary preparations and strategies to be taken for effective controlling of the ZIKV infection in Bangladesh before getting any havoc.
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Affiliation(s)
- Md Golzar Hossain
- Department of Microbiology and Hygiene, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh.,Division of Virology, Department of Microbiology and Immunology, Graduate School of Medicine, Osaka University, Japan
| | - K H M Nazmul Hussain Nazir
- Department of Microbiology and Hygiene, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
| | - Sukumar Saha
- Department of Microbiology and Hygiene, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
| | - Md Tanvir Rahman
- Department of Microbiology and Hygiene, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
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22
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Ding F, Wang Q, Fu J, Chen S, Hao M, Ma T, Zheng C, Jiang D. Risk factors and predicted distribution of visceral leishmaniasis in the Xinjiang Uygur Autonomous Region, China, 2005-2015. Parasit Vectors 2019; 12:528. [PMID: 31703720 PMCID: PMC6839266 DOI: 10.1186/s13071-019-3778-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 10/30/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Visceral leishmaniasis (VL) is a neglected disease that is spread to humans by the bites of infected female phlebotomine sand flies. Although this vector-borne disease has been eliminated in most parts of China, it still poses a significant public health burden in the Xinjiang Uygur Autonomous Region. Understanding of the spatial epidemiology of the disease remains vague in the local community. In the present study, we investigated the spatiotemporal distribution of VL in the region in order to assess the potential threat of the disease. METHODS Based on comprehensive infection records, the spatiotemporal patterns of new cases of VL in the region between 2005 and 2015 were analysed. By combining maps of environmental and socioeconomic correlates, the boosted regression tree (BRT) model was adopted to identify the environmental niche of VL. RESULTS The fitted BRT models were used to map potential infection risk zones of VL in the Xinjiang Uygur Autonomous Region, revealing that the predicted high infection risk zones were mainly concentrated in central and northern Kashgar Prefecture, south of Atushi City bordering Kashgar Prefecture and regions of the northern Bayingolin Mongol Autonomous Prefecture. The final result revealed that approximately 16.64 million people inhabited the predicted potential infection risk areas in the region. CONCLUSIONS Our results provide a better understanding of the potential endemic foci of VL in the Xinjiang Uygur Autonomous Region with a 1 km spatial resolution, thereby enhancing our capacity to target the potential risk areas, to develop disease control strategies and to allocate medical supplies.
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Affiliation(s)
- Fangyu Ding
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qian Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingying Fu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shuai Chen
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mengmeng Hao
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tian Ma
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Canjun Zheng
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing, 102206, China.
| | - Dong Jiang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land & Resources, Beijing, 100101, China.
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23
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Chien LC, Sy F, Pérez A. Identifying high risk areas of Zika virus infection by meteorological factors in Colombia. BMC Infect Dis 2019; 19:888. [PMID: 31651247 PMCID: PMC6814059 DOI: 10.1186/s12879-019-4499-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 09/23/2019] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Several Zika virus (ZIKV) outbreaks have occurred since October 2015. Because there is no effective treatment for ZIKV infection, developing an effective surveillance and warning system is currently a high priority to prevent ZIKV infection. Despite Aedes mosquitos having been known to spread ZIKV, the calculation approach is diverse, and only applied to local areas. This study used meteorological measurements to monitor ZIKV infection due to the high correlation between climate change and Aedes mosquitos and the convenience to obtain meteorological data from weather monitoring stations. METHODS This study applied the Bayesian structured additive regression modeling approach to include spatial interactive terms with meteorological factors and a geospatial function in a zero-inflated Poisson model. The study area contained 32 administrative departments in Colombia from October 2015 to December 2017. Weekly ZIKV infection cases and daily meteorological measurements were collected. Mapping techniques were adopted to visualize spatial findings. A series of model selections determined the best combinations of meteorological factors in the same model. RESULTS When multiple meteorological factors are considered in the same model, both total rainfall and average temperature can best assess the geographic disparities of ZIKV infection. Meanwhile, a 1-in. increase in rainfall is associated with an increase in the logarithm of relative risk (logRR) of ZIKV infection of at most 1.66 (95% credible interval [CI] = 1.09, 2.15) as well as a 1 °F increase in average temperature is significantly associated with at most 0.79 (95% CI = 0.12, 1.22) increase in the logRR of ZIKV. Moreover, after controlling rainfall and average temperature, an independent geospatial function in the model results in two departments with an excessive ZIKV risk which may be explained by unobserved factors other than total rainfall and average temperature. CONCLUSION Our study found that meteorological factors are significantly associated with ZIKV infection across departments. The study determined both total rainfall and average temperature as the best meteorological factors to identify high risk departments of ZIKV infection. These findings can help governmental agencies monitor at risk areas according to meteorological measurements, and develop preventions in those at risk areas in priority.
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Affiliation(s)
- Lung-Chang Chien
- Department of Environmental and Occupational Health, University of Nevada, Las Vegas, School of Public Health, 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA.
| | - Francisco Sy
- Department of Environmental and Occupational Health, University of Nevada, Las Vegas, School of Public Health, 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA
| | - Adriana Pérez
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston-UTHealth, School of Public Health, Austin Campus, 1616 Guadalupe, Suite 6.300, Austin, TX, 78071, USA
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Akhtar M, Kraemer MUG, Gardner LM. A dynamic neural network model for predicting risk of Zika in real time. BMC Med 2019; 17:171. [PMID: 31474220 PMCID: PMC6717993 DOI: 10.1186/s12916-019-1389-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 07/12/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak's expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner. METHODS In this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future. RESULTS The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks. CONCLUSIONS Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.
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Affiliation(s)
- Mahmood Akhtar
- School of Civil and Environment Engineering, UNSW Sydney, Sydney, NSW, Australia
- School of Women's and Children's Health, UNSW Sydney, Sydney, NSW, Australia
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK
- Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Lauren M Gardner
- School of Civil and Environment Engineering, UNSW Sydney, Sydney, NSW, Australia.
- Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Moreira MW, Rodrigues JJ, Carvalho FH, Al-Muhtadi J, Kozlov S, Rabêlo RA. Classification of risk areas using a bootstrap-aggregated ensemble approach for reducing Zika virus infection in pregnant women. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.04.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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