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Muzembo BA, Kitahara K, Mitra D, Ntontolo NP, Ngatu NR, Ohno A, Khatiwada J, Dutta S, Miyoshi SI. The basic reproduction number (R 0) of ebola virus disease: A systematic review and meta-analysis. Travel Med Infect Dis 2024; 57:102685. [PMID: 38181864 DOI: 10.1016/j.tmaid.2023.102685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/07/2024]
Abstract
BACKGROUND Ebola virus disease (Ebola) is highly pathogenic, transmissible, and often deadly, with debilitating consequences. Superspreading within a cluster is also possible. In this study, we aim to document Ebola basic reproduction number (R0): the average number of new cases associated with an Ebola case in a completely susceptible population. METHODS We undertook a systematic review and meta-analysis. We searched PubMed, EMBASE, and Web of Science for studies published between 1976 and February 27, 2023. We also manually searched the reference lists of the reviewed studies to identify additional studies. We included studies that reported R0 during Ebola outbreaks in Africa. We excluded studies that reported only the effective reproduction number (Rt). Abstracting data from included studies was performed using a pilot-tested standard form. Two investigators reviewed the studies, extracted the data, and assessed quality. The pooled R0 was determined by a random-effects meta-analysis. R0 was stratified by country. We also estimated the theoretically required immunization coverage to reach herd-immunity using the formula of (1-1/R0) × 100 %. RESULTS The search yielded 2042 studies. We included 53 studies from six African countries in the systematic review providing 97 Ebola mean R0 estimates. 27 (with 46 data points) studies were included in the meta-analysis. The overall pooled mean Ebola R0 was 1.95 (95 % CI 1.74-2.15), with high heterogeneity (I2 = 99.99 %; τ2 = 0.38; and p < 0.001) and evidence of small-study effects (Egger's statistics: Z = 4.67; p < 0.001). Mean Ebola R0 values ranged from 1.2 to 10.0 in Nigeria, 1.1 to 7 in Guinea, 1.14 to 8.33 in Sierra Leone, 1.13 to 5 in Liberia, 1.2 to 5.2 in DR Congo, 1.34 to 2.7 in Uganda, and from 1.40 to 2.55 for all West African countries combined. Pooled mean Ebola R0 was 9.38 (95 % CI 4.16-14.59) in Nigeria, 3.31 (95 % CI 2.30-4.32) in DR Congo, 2.0 (95 % CI 1.25-2.76) in Uganda, 1.83 (95 % CI 1.61-2.05) in Liberia, 1.73 (95 % CI 1.47-2.0) in Sierra Leonne, and 1.44 (95 % CI 1.29-1.60) in Guinea. In theory, 50 % of the population needs to be vaccinated to achieve herd immunity, assuming that Ebola vaccine would be 100 % effective. CONCLUSIONS Ebola R0 varies widely across countries. Ebola has a much wider R0 range than is often claimed (1.3-2.0). It is possible for an Ebola index case to infect more than two susceptible individuals.
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Affiliation(s)
- Basilua Andre Muzembo
- Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
| | - Kei Kitahara
- Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan; Collaborative Research Centre of Okayama University for Infectious Diseases in India at ICMR-NICED, Kolkata, India
| | - Debmalya Mitra
- Collaborative Research Centre of Okayama University for Infectious Diseases in India at ICMR-NICED, Kolkata, India
| | - Ngangu Patrick Ntontolo
- Institut Médical Evangélique (IME), Kimpese, Congo; Department of Family Medicine and PHC, Protestant University of Congo, Congo
| | - Nlandu Roger Ngatu
- Department of Public Health, Kagawa University Faculty of Medicine, Miki, Japan
| | - Ayumu Ohno
- Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan; Collaborative Research Centre of Okayama University for Infectious Diseases in India at ICMR-NICED, Kolkata, India
| | | | - Shanta Dutta
- Division of Bacteriology, ICMR-National Institute of Cholera and Enteric Diseases, Kolkata, India
| | - Shin-Ichi Miyoshi
- Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
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Sun H, Zhang Y, Gao G, Wu D. Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives. Front Public Health 2022; 10:958835. [PMID: 36544794 PMCID: PMC9760721 DOI: 10.3389/fpubh.2022.958835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 11/09/2022] [Indexed: 12/12/2022] Open
Abstract
With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.
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Affiliation(s)
- Hua Sun
- Popsmart Technology (Zhejiang) Co., Ltd, Ningbo, China
| | - Yuzhou Zhang
- Popsmart Technology (Zhejiang) Co., Ltd, Ningbo, China,College of Computer Science and Technology, Zhejiang University, Hangzhou, China,*Correspondence: Yuzhou Zhang
| | - Guang Gao
- Popsmart Technology (Zhejiang) Co., Ltd, Ningbo, China
| | - Dun Wu
- Popsmart Technology (Zhejiang) Co., Ltd, Ningbo, China
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3
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Wu M, Li C, Shen Z, He S, Tang L, Zheng J, Fang Y, Li K, Cheng Y, Shi Z, Sheng G, Liu Y, Zhu J, Ye X, Chen J, Chen W, Li L, Sun Y, Chen J. Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data. COMMUNICATIONS PHYSICS 2022; 5:270. [PMID: 36373056 PMCID: PMC9638278 DOI: 10.1038/s42005-022-01045-4] [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: 10/06/2021] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location-related data contributed by 10,527,737 smartphone users in Wuhan, China. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does, indicating a sub-sampled dataset would be as good at prediction. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.
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Affiliation(s)
- Mincheng Wu
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Chao Li
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Zhangchong Shen
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Shibo He
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Lingling Tang
- Shulan (Hangzhou) Hospital Affiliated to Shulan International Medical College, Zhejiang Shuren University, Hangzhou, 310015 China
| | - Jie Zheng
- Zhejiang Institute of Medical-care Information Technology, Hangzhou, 311100 China
| | - Yi Fang
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Kehan Li
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Yanggang Cheng
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Zhiguo Shi
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Guoping Sheng
- Shulan (Hangzhou) Hospital Affiliated to Shulan International Medical College, Zhejiang Shuren University, Hangzhou, 310015 China
| | - Yu Liu
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Jinxing Zhu
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Xinjiang Ye
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Jinlai Chen
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Wenrong Chen
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, 310027 China
| | - Youxian Sun
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Jiming Chen
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
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Mir SA, Bhat MS, Rather G, Mattoo D. Role of big geospatial data in the COVID-19 crisis. DATA SCIENCE FOR COVID-19 2022. [PMCID: PMC8988928 DOI: 10.1016/b978-0-323-90769-9.00031-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The outbreak of the 2019 novel coronavirus disease (COVID-19) has infected 4 million people worldwide and has caused more than 300,000 deaths worldwide. With infection and death rates on rise, COVID-19 poses a serious threat to social functioning, human health, economies, and geopolitics. Geographic information systems and big geospatial technologies have come to the forefront in this fight against COVID-19 by playing an important role by integrating multisourced data, enhanced and rapid analytics of mapping services, location analytics, and spatial tracking of confirmed, forecasting transmission trajectories, spatial clustering of risk on epidemiologic levels, public awareness on the elimination of panic spread and decision-making support for the government and research institutions for effective prevention and control of COVID-19 cases. Big geospatial data has turned itself as the major support system for governments in dealing with this global healthcare crisis because of its advanced and innovative technological capabilities from preparation of data to modeling the results with quick and large accessibility to every spatial scale. This robust data-driven system using the accurate and prediction geoanalysis is being widely used by governments and public health institutions interfaced with both health and nonhealth digital data repositories for mining the individual and regional datasets for breaking the transmission chain. Profiling of confirmed cases on the basis of location and temporality and then visualizing them effectively coupled with behavioral and critical geographic variables such as mobility patterns, demographic data, and population density enhance the predictive analytics of big geospatial data. With the intersection of artificial intelligence, geospatial data enables real-time visualization and syndromic surveillance of epidemic data based on spatiotemporal dynamics and the data are then accurately geopositioned. This chapter aims to reflect on the relevance of big geospatial data and health geoinformatics in containing and preventing the further spread of COVID-19 and how countries and research organizations around the world have used it as accurate, fast, and comprehensive dataset in their containing strategy and management of this public health crisis. China and Taiwan are used as case studies as in how these countries have applied the computational architecture of big geospatial data and location analytics surveillance techniques for prediction and monitoring of COVID-19-positive cases.
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Valdivia-Granda WA. Known and Unknown Transboundary Infectious Diseases as Hybrid Threats. Front Public Health 2021; 9:668062. [PMID: 34336765 PMCID: PMC8316594 DOI: 10.3389/fpubh.2021.668062] [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: 03/01/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Abstract
The pathogenicity, transmissibility, environmental stability, and potential for genetic manipulation make microbes hybrid threats that could blur the distinction between peace and war. These agents can fall below the detection, attribution, and response capabilities of a nation and seriously affect their health, trade, and security. A framework that could enhance horizon scanning regarding the potential risk of microbes used as hybrid threats requires not only accurately discriminating known and unknown pathogens but building novel scenarios to deploy mitigation strategies. This demands the transition of analyst-based biosurveillance tracking a narrow set of pathogens toward an autonomous biosurveillance enterprise capable of processing vast data streams beyond human cognitive capabilities. Autonomous surveillance systems must gather, integrate, analyze, and visualize billions of data points from different and unrelated sources. Machine learning and artificial intelligence algorithms can contextualize capability information for different stakeholders at different levels of resolution: strategic and tactical. This document provides a discussion of the use of microorganisms as hybrid threats and considerations to quantitatively estimate their risk to ensure societal awareness, preparedness, mitigation, and resilience.
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Bhatia S, Lassmann B, Cohn E, Desai AN, Carrion M, Kraemer MUG, Herringer M, Brownstein J, Madoff L, Cori A, Nouvellet P. Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread. NPJ Digit Med 2021; 4:73. [PMID: 33864009 PMCID: PMC8052406 DOI: 10.1038/s41746-021-00442-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 03/16/2021] [Indexed: 02/02/2023] Open
Abstract
Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1-4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, London, UK.
| | - Britta Lassmann
- ProMED, International Society for Infectious Diseases, Brookline, MA, USA
| | - Emily Cohn
- Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Angel N Desai
- ProMED, International Society for Infectious Diseases, Brookline, MA, USA
| | - Malwina Carrion
- ProMED, International Society for Infectious Diseases, Brookline, MA, USA
- Department of Health Science, Sargent College, Boston University, Boston, MA, USA
| | - Moritz U G Kraemer
- Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA
- Department of Zoology, Tinbergen Building, Oxford University, Oxford, UK
- Department of Pediatrics, Harvard Medical School, Boston, USA
| | | | - John Brownstein
- Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Larry Madoff
- ProMED, International Society for Infectious Diseases, Brookline, MA, USA
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
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7
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Shakiba N, Edholm CJ, Emerenini BO, Murillo AL, Peace A, Saucedo O, Wang X, Allen LJ. Effects of environmental variability on superspreading transmission events in stochastic epidemic models. Infect Dis Model 2021; 6:560-583. [PMID: 33754134 PMCID: PMC7969833 DOI: 10.1016/j.idm.2021.03.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 03/02/2021] [Accepted: 03/02/2021] [Indexed: 11/02/2022] Open
Abstract
Superspreaders (individuals with a high propensity for disease spread) have played a pivotal role in recent emerging and re-emerging diseases. In disease outbreak studies, host heterogeneity based on demographic (e.g. age, sex, vaccination status) and environmental (e.g. climate, urban/rural residence, clinics) factors are critical for the spread of infectious diseases, such as Ebola and Middle East Respiratory Syndrome (MERS). Transmission rates can vary as demographic and environmental factors are altered naturally or due to modified behaviors in response to the implementation of public health strategies. In this work, we develop stochastic models to explore the effects of demographic and environmental variability on human-to-human disease transmission rates among superspreaders in the case of Ebola and MERS. We show that the addition of environmental variability results in reduced probability of outbreak occurrence, however the severity of outbreaks that do occur increases. These observations have implications for public health strategies that aim to control environmental variables.
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Affiliation(s)
- Nika Shakiba
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | | | - Blessing O. Emerenini
- Department of Mathematics, Oregon State University, Corvallis, OR, USA
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - Anarina L. Murillo
- Department of Pediatrics and Center for Statistical Sciences, Brown University, Providence, RI, USA
| | - Angela Peace
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, USA
| | - Omar Saucedo
- Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Xueying Wang
- Department of Mathematics and Statistics, Washington State University, Pullman, WA, USA
| | - Linda J.S. Allen
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, USA
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Wu J, Wang J, Nicholas S, Maitland E, Fan Q. Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations. J Med Internet Res 2020; 22:e21980. [PMID: 33001836 PMCID: PMC7561444 DOI: 10.2196/21980] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 07/28/2020] [Accepted: 09/14/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease's rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19. OBJECTIVE The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations. METHODS We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China's new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China's response to the epidemic and to provide lessons for other countries' prevention and control of COVID-19. RESULTS In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus's sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system. CONCLUSIONS China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.
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Affiliation(s)
- Jun Wu
- Dong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China
| | - Jian Wang
- Dong Fureng Institute of Economic and Social Development, Wuhan University, Beijing, China
| | - Stephen Nicholas
- Australian National Institute of Management and Commerce, Sydney, Australia
- Newcastle Business School, University of Newcastle, Newcastle, Australia
| | - Elizabeth Maitland
- School of Management, University of Liverpool, Liverpool, United Kingdom
| | - Qiuyan Fan
- Dong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China
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9
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Degeling C, Carter SM, van Oijen AM, McAnulty J, Sintchenko V, Braunack-Mayer A, Yarwood T, Johnson J, Gilbert GL. Community perspectives on the benefits and risks of technologically enhanced communicable disease surveillance systems: a report on four community juries. BMC Med Ethics 2020; 21:31. [PMID: 32334597 PMCID: PMC7183724 DOI: 10.1186/s12910-020-00474-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 04/17/2020] [Indexed: 12/18/2022] Open
Abstract
Background Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologies – pathogen whole genome sequencing (WGS) and Big Data analytics – promise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information could be perceived as intrusive and a threat to privacy. Method Four community juries were convened in two demographically different Sydney municipalities and two regional cities in New South Wales, Australia (western Sydney, Wollongong, Tamworth, eastern Sydney) to elicit the views of well-informed community members on the acceptability and legitimacy of:
making pathogen WGS and linked administrative data available for public health research using this information in concert with data linkage and machine learning to enhance communicable disease surveillance systems
Fifty participants of diverse backgrounds, mixed genders and ages were recruited by random-digit-dialling and topic-blinded social-media advertising. Each jury was presented with balanced factual evidence supporting different expert perspectives on the potential benefits and costs of technologically enhanced public health research and communicable disease surveillance and given the opportunity to question experts. Results Almost all jurors supported data linkage and WGS on routinely collected patient isolates for the purposes of public health research, provided standard de-identification practices were applied. However, allowing this information to be operationalised as a syndromic surveillance system was highly contentious with three juries voting in favour, and one against by narrow margins. For those in favour, support depended on several conditions related to system oversight and security being met. Those against were concerned about loss of privacy and did not trust Australian governments to run secure and effective systems. Conclusions Participants across all four events strongly supported the introduction of data linkage and pathogenomics to public health research under current research governance structures. Combining pathogen WGS with event-based data surveillance systems, however, is likely to be controversial because of a lack of public trust, even when the potential public health benefits are clear. Any suggestion of private sector involvement or commercialisation of WGS or surveillance data was unanimously rejected.
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Affiliation(s)
- Chris Degeling
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, Australia. .,School of Health and Society, University of Wollongong, Wollongong, Australia.
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, Australia.,School of Health and Society, University of Wollongong, Wollongong, Australia
| | - Antoine M van Oijen
- Molecular Horizons and the Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia
| | | | - Vitali Sintchenko
- The Centre for Infectious Diseases and Microbiology - Public Health, Westmead, Sydney, Australia.,Marie Bashir Institute for Infectious Disease and Biosecurity, The University of Sydney, Sydney, Australia
| | - Annette Braunack-Mayer
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, Australia.,School of Health and Society, University of Wollongong, Wollongong, Australia
| | - Trent Yarwood
- Cairns and Hinterland Hospital and Health Service, Cairns, Australia.,Cairns Clinical School, James Cook University, Cairns, Australia.,Rural Clinical School, University of Queensland, Brisbane, Australia
| | - Jane Johnson
- The Centre for Infectious Diseases and Microbiology - Public Health, Westmead, Sydney, Australia.,Sydney Health Ethics, School of Public Health, The University of Sydney, Sydney, Australia
| | - Gwendolyn L Gilbert
- The Centre for Infectious Diseases and Microbiology - Public Health, Westmead, Sydney, Australia.,Marie Bashir Institute for Infectious Disease and Biosecurity, The University of Sydney, Sydney, Australia.,Sydney Health Ethics, School of Public Health, The University of Sydney, Sydney, Australia
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10
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Degeling C, Johnson J, Gilbert GL. Perspectives of Australian policy-makers on the potential benefits and risks of technologically enhanced communicable disease surveillance - a modified Delphi survey. Health Res Policy Syst 2019; 17:35. [PMID: 30947721 PMCID: PMC6449976 DOI: 10.1186/s12961-019-0440-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 03/14/2019] [Indexed: 11/22/2022] Open
Abstract
Background Event-based social media monitoring and pathogen whole genome sequencing (WGS) will enhance communicable disease surveillance research and systems. If linked electronically and scanned systematically, the information provided by these technologies could be mined to uncover new epidemiological patterns and associations much faster than traditional public health approaches. The benefits of earlier outbreak detection are significant, but implementation could be opposed in the absence of a social licence or if ethical and legal concerns are not addressed. Methods A three-phase mixed-method Delphi survey with Australian policy-makers, health practitioners and lawyers (n = 44) was conducted to explore areas of consensus and disagreement over (1) key policy and practical issues raised by the introduction of novel communicable disease surveillance programmes; and (2) the most significant and likely risks from using social media content and WGS technologies in epidemiological research and outbreak investigations. Results Panellists agreed that the integration of social media monitoring and WGS technologies into communicable disease surveillance systems raised significant issues, including impacts on personal privacy, medicolegal risks and the potential for unintended consequences. Notably, their concerns focused on how these technologies should be used, rather than how the data was collected. Panellists held that social media users should expect their posts to be monitored in the interests of public health, but using those platforms to contact identified individuals was controversial. The conditions of appropriate use of pathogen WGS in epidemiological research and investigations was also contentious. Key differences amongst participants included the necessity for consent before testing and data-linkage, thresholds for action, and the legal and ethical importance of harms to individuals and commercial entities. The erosion of public trust was seen as the most significant risk from the systematic use of these technologies. Conclusions Enhancing communicable disease surveillance with social-media monitoring and pathogen WGS may cause controversy. The challenge is to determine and then codify how these technologies should be used such that the balance between individual risk and community benefit is widely accepted. Participants agreed that clear guidelines for appropriate use that address legal and ethical concerns need to be developed in consultation with relevant experts and the broader Australian public. Electronic supplementary material The online version of this article (10.1186/s12961-019-0440-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chris Degeling
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of Social Science, University of Wollongong, Building 233.G05D, Wollongong, NSW, 2500, Australia. .,Sydney Health Ethics, Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia.
| | - Jane Johnson
- Sydney Health Ethics, Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia.,Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Westmead, NSW, Australia
| | - Gwendolyn L Gilbert
- Sydney Health Ethics, Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia.,Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Westmead, NSW, Australia.,Marie Bashir Institute for Infectious Disease and Biosecurity, University of Sydney, Sydney, NSW, Australia
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11
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Abstract
This chapter will provide an overview of how “big data” and “big data” analytics can be brought to bear on the pressing biodefense challenges of: (1) threat awareness; and (2) surveillance and detection. The chapter will also discuss potential problems that can arise by relying exclusively on “big data” approaches, which have properties and limitations inherent in their composition that may not be initially recognized but which may lead to erroneous results. The chapter will conclude by discussing how multi-disciplinary teams of researchers using hybrid systems, involving “big data” and “small data,” could more effectively and accurately contribute to understanding biodefense problems.
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Affiliation(s)
- Sunit K. Singh
- Molecular Biology Unit, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
| | - Jens H. Kuhn
- NIH/NIAID, Division of Clinical Research, Integrated Research Facility at Fort Detrick, Frederick, MD USA
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12
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Lau MSY, Gibson GJ, Adrakey H, McClelland A, Riley S, Zelner J, Streftaris G, Funk S, Metcalf J, Dalziel BD, Grenfell BT. A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak. PLoS Comput Biol 2017; 13:e1005798. [PMID: 29084216 PMCID: PMC5679647 DOI: 10.1371/journal.pcbi.1005798] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 11/09/2017] [Accepted: 09/28/2017] [Indexed: 11/18/2022] Open
Abstract
In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging. Availability of individual-level, spatio-temporal disease data (e.g. GPS locations of infected individuals) has been increasing in recent years, primarily due to the increased use of modern communication devices such as mobile phones. Such data create invaluable opportunities for researchers to study disease transmission on a more refined individual-to-individual level, facilitating the designs of potentially more effective control measures. However, the growing availability of such precise data has not been accompanied by development of statistically sound mechanistic frameworks. Developing such frameworks is an essential step for systematically extracting maximal information from data, in particular, evaluating the efficacy of individually-targeted control strategies and enabling forward epidemic prediction at the individual level. In this paper we develop a novel statistical framework that overcomes a few key limitations of existing approaches, enabling a machinery that can be used to infer the history of partially observed outbreaks and, more importantly, to produce a more comprehensive epidemic prediction. Our framework may also be a good surrogate for more computationally challenging individual-based models.
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Affiliation(s)
- Max S. Y. Lau
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
| | - Gavin J. Gibson
- Maxwell Institute for Mathematical Sciences, Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Hola Adrakey
- Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom
| | - Amanda McClelland
- International Federation of Red Cross and Red Crescent Societies, Geneva, Switzerland
| | - Steven Riley
- MRC Centre for Outbreak Analysis and Modelling, Department Infectious Disease Epidemiology, Imperial College, London, United Kingdom
| | - Jon Zelner
- School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - George Streftaris
- Maxwell Institute for Mathematical Sciences, Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Sebastian Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Jessica Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Benjamin D. Dalziel
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, United States of America
- Department of Mathematics, Oregon State University, Corvallis, Oregon, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
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13
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Bansal S, Chowell G, Simonsen L, Vespignani A, Viboud C. Big Data for Infectious Disease Surveillance and Modeling. J Infect Dis 2017; 214:S375-S379. [PMID: 28830113 DOI: 10.1093/infdis/jiw400] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts.
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Affiliation(s)
- Shweta Bansal
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland.,Department of Biology, Georgetown University, Washington D.C
| | - Gerardo Chowell
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland.,School of Public Health, Georgia State University, Atlanta
| | - Lone Simonsen
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland.,Department of Public Health, University of Copenhagen, Denmark
| | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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14
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Constructing Ebola transmission chains from West Africa and estimating model parameters using internet sources. Epidemiol Infect 2017; 145:1993-2002. [PMID: 28462731 DOI: 10.1017/s0950268817000760] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
During the recent Ebola crisis in West Africa, individual person-level details of disease onset, transmissions, and outcomes such as survival or death were reported in online news media. We set out to document disease transmission chains for Ebola, with the goal of generating a timely account that could be used for surveillance, mathematical modeling, and public health decision-making. By accessing public web pages only, such as locally produced newspapers and blogs, we created a transmission chain involving two Ebola clusters in West Africa that compared favorably with other published transmission chains, and derived parameters for a mathematical model of Ebola disease transmission that were not statistically different from those derived from published sources. We present a protocol for responsibly gleaning epidemiological facts, transmission model parameters, and useful details from affected communities using mostly indigenously produced sources. After comparing our transmission parameters to published parameters, we discuss additional benefits of our method, such as gaining practical information about the affected community, its infrastructure, politics, and culture. We also briefly compare our method to similar efforts that used mostly non-indigenous online sources to generate epidemiological information.
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