1
|
Clarke J, Lim A, Gupte P, Pigott DM, van Panhuis WG, Brady OJ. A global dataset of publicly available dengue case count data. Sci Data 2024; 11:296. [PMID: 38485954 PMCID: PMC10940302 DOI: 10.1038/s41597-024-03120-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
OpenDengue is a global database of dengue case data collated from public sources and standardised and formatted to facilitate easy reanalysis. Dataset version 1.2 of this database contains information on over 56 million dengue cases from 102 countries between 1924 and 2023, making it the largest and most comprehensive dengue case database currently available. Over 95% of records are at the weekly or monthly temporal resolution and subnational data is available for 40 countries. To build OpenDengue we systematically searched databases, ministry of health websites, peer reviewed literature and Pro-MED mail reports and extracted denominator-based case count data. We undertake standardisation and error checking protocols to ensure consistency and resolve discrepancies. We meticulously documented the extraction process to ensure records are attributable and reproducible. The OpenDengue database remains under development with plans for further disaggregation and user contributions are encouraged. This new dataset can be used to better understand the long-term drivers of dengue transmission, improve estimates of disease burden, targeting and evaluation of interventions and improving future projections.
Collapse
Affiliation(s)
- J Clarke
- Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - A Lim
- Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - P Gupte
- Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - D M Pigott
- 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
| | - W G van Panhuis
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - O J Brady
- Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
| |
Collapse
|
2
|
Olivo S, Venier D, Zannier M, Pittini C, Achil I, Danielis M. A two-year retrospective study of the neonatal emergency transport service in Northeast Italy. J Matern Fetal Neonatal Med 2023; 36:2199907. [PMID: 37037655 DOI: 10.1080/14767058.2023.2199907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
BACKGROUND Some newborns require acute transport to a Neonatal Intensive Care Unit (NICU) due to unpredicted or unpredictable reasons. OBJECTIVE To describe the activity of the Neonatal Emergency Transport Service (NETS) in Northeast Italy. METHODS An observational retrospective study was performed between 1 January 2018, and 31 December , 2019. RESULTS A total of 133 transports were collected, with a neonatal transport index of 1.4%. Infants ≤2500 grams were more frequently transferred by NETS than those in the normal group (n = 34/563, 6.0% vs. n = 99/8,437, 1.2%; p < .001). The incidence of preterm birth among transferred newborns was 42/133 (31.6%). For the newborns with >2500 grams, there was a low incidence of a cesarean birth compared to vaginal delivery (23.2% versus 63.5%; p = .001), while the percentages were reversed in the group of infants ≤2500 grams (67.7% versus 20.6%) (p = .001). Infant stabilization time was higher in the underweight group compared to those weighed >2500 grams (31.5 versus 23.0 min; p < .001), as well as the median length of stay in NICU (18.0 versus 8.0 days, respectively, p < .001). The group of infants ≤2500 grams received more intravenous therapy (47.1% vs. 26.2%) and invasive ventilation (26.5% vs. 8.1%), compared to the group of infants who weighed >2500 grams. CONCLUSIONS This study described a local reality by showing the characteristics of the neonatal transports that took place in a metropolitan area in Northeast Italy. Wider database is necessary to achieve a better knowledge in the field of perinatal outcomes.
Collapse
Affiliation(s)
- Stella Olivo
- Neonatal Intensive Care Unit, Department of Maternal Care, Academic Hospital of Udine, Italy
| | - Debora Venier
- Neonatal Intensive Care Unit, Department of Maternal Care, Academic Hospital of Udine, Italy
| | - Mirco Zannier
- Neonatal Intensive Care Unit, Department of Maternal Care, Academic Hospital of Udine, Italy
| | - Carla Pittini
- Neonatal Intensive Care Unit, Department of Maternal Care, Academic Hospital of Udine, Italy
| | - Illarj Achil
- Laboratory of Studies and Evidence Based Nursing, Department of Medicine, Padova University, Italy
| | - Matteo Danielis
- Laboratory of Studies and Evidence Based Nursing, Department of Medicine, Padova University, Italy
| |
Collapse
|
3
|
Proma AY, Das PR, Akter S, Dewan SMR, Islam MS. The urgent need for a policy on epidemiological data on cardiovascular diseases in Bangladesh. Health Sci Rep 2023; 6:e1410. [PMID: 37425230 PMCID: PMC10326673 DOI: 10.1002/hsr2.1410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/11/2023] Open
Abstract
Background Disease prevention and healthcare policy choices cannot be made without epidemiology data. Since it is a growing country with rapidly increasing illness rates, this information is in great demand in Bangladesh. This is because there is a shortage of reliable and sufficient data, leading to inadequate preventive and treatment methods. Discussion Poor health concerns and economic conditions mean that not all families can afford to provide the nutrition their members need, leading to an increase in the prevalence of many diseases. The outcome is an ever-increasing threat of cardiovascular disease (CVD) issues, the leading cause of death in Bangladesh, even though the underlying causes remain unknown. There is a strong demand for accurate information on CVD patients in Bangladesh, however, there is no effective framework for managing epidemiological data. This prevents an in-depth analysis of the nation's socioeconomic status, dietary practices, and way of life, as well as the implementation of sound healthcare policy. Conclusion In this article, we present arguments on this important issue using the healthcare systems of the developed world and Bangladesh as examples.
Collapse
Affiliation(s)
- Amrin Yeasin Proma
- Department of Pharmacy, School of MedicineUniversity of Asia PacificDhakaBangladesh
| | - Proma Rani Das
- Department of Pharmacy, School of MedicineUniversity of Asia PacificDhakaBangladesh
| | - Sayma Akter
- Department of Pharmacy, School of MedicineUniversity of Asia PacificDhakaBangladesh
| | - Syed Masudur Rahman Dewan
- Department of Pharmacy, School of MedicineUniversity of Asia PacificDhakaBangladesh
- Division of PharmacologyCenter for Life Sciences ResearchDhakaBangladesh
| | - Mohammad Safiqul Islam
- Department of Pharmacy, Faculty of ScienceNoakhali Science and Technology UniversityNoakhaliBangladesh
| |
Collapse
|
4
|
Schneider KA, Tsoungui Obama HCJ, Kamanga G, Kayanula L, Adil Mahmoud Yousif N. The many definitions of multiplicity of infection. FRONTIERS IN EPIDEMIOLOGY 2022; 2:961593. [PMID: 38455332 PMCID: PMC10910904 DOI: 10.3389/fepid.2022.961593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 09/06/2022] [Indexed: 03/09/2024]
Abstract
The presence of multiple genetically different pathogenic variants within the same individual host is common in infectious diseases. Although this is neglected in some diseases, it is well recognized in others like malaria, where it is typically referred to as multiplicity of infection (MOI) or complexity of infection (COI). In malaria, with the advent of molecular surveillance, data is increasingly being available with enough resolution to capture MOI and integrate it into molecular surveillance strategies. The distribution of MOI on the population level scales with transmission intensities, while MOI on the individual level is a confounding factor when monitoring haplotypes of particular interests, e.g., those associated with drug-resistance. Particularly, in high-transmission areas, MOI leads to a discrepancy between the likelihood of a haplotype being observed in an infection (prevalence) and its abundance in the pathogen population (frequency). Despite its importance, MOI is not universally defined. Competing definitions vary from verbal ones to those based on concise statistical frameworks. Heuristic approaches to MOI are popular, although they do not mine the full potential of available data and are typically biased, potentially leading to misinferences. We introduce a formal statistical framework and suggest a concise definition of MOI and its distribution on the host-population level. We show how it relates to alternative definitions such as the number of distinct haplotypes within an infection or the maximum number of alleles detectable across a set of genetic markers. It is shown how alternatives can be derived from the general framework. Different statistical methods to estimate the distribution of MOI and pathogenic variants at the population level are discussed. The estimates can be used as plug-ins to reconstruct the most probable MOI of an infection and set of infecting haplotypes in individual infections. Furthermore, the relation between prevalence of pathogenic variants and their frequency (relative abundance) in the pathogen population in the context of MOI is clarified, with particular regard to seasonality in transmission intensities. The framework introduced here helps to guide the correct interpretation of results emerging from different definitions of MOI. Especially, it excels comparisons between studies based on different analytical methods.
Collapse
|
5
|
COVID-19 data reporting systems in Africa reveal insights for future pandemics. Epidemiol Infect 2022; 150:e119. [PMID: 35708156 PMCID: PMC9237488 DOI: 10.1017/s0950268822001054] [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] [Indexed: 11/05/2022] Open
Abstract
Globally, countries have used diverse methods to report data during the COVID-19 pandemic. Using international guidelines and principles of emergency management, we compare national data reporting systems in African countries in order to determine lessons for future pandemics. We analyse COVID-19 reporting practices across 54 African countries through 2020. Reporting systems were diverse and included summaries, press releases, situation reports and online dashboards. These systems were communicated via social media accounts and websites belonging to ministries of health and public health. Data variables from the reports included event detection (cases/deaths/recoveries), risk assessment (demographics/co-morbidities) and response (total tests/hospitalisations). Of countries with reporting systems, 36/53 (67.9%) had recurrent situation reports and/or online dashboards which provided more extensive data. All of these systems reported cases, deaths and recoveries. However, few systems contained risk assessment and response data, with only 5/36 (13.9%) reporting patient co-morbidities and 9/36 (25%) including total hospitalisations. Further evaluation of reporting practices in Cameroon, Egypt, Kenya, Senegal and South Africa as examples from different sub-regions revealed differences in reporting healthcare capacity and preparedness data. Improving the standardisation and accessibility of national data reporting systems could augment research and decision-making, as well as increase public awareness and transparency for national governments.
Collapse
|
6
|
Using Google Health Trends to investigate COVID-19 incidence in Africa. PLoS One 2022; 17:e0269573. [PMID: 35671301 PMCID: PMC9173636 DOI: 10.1371/journal.pone.0269573] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/19/2022] Open
Abstract
The COVID-19 pandemic has caused over 500 million cases and over six million deaths globally. From these numbers, over 12 million cases and over 250 thousand deaths have occurred on the African continent as of May 2022. Prevention and surveillance remains the cornerstone of interventions to halt the further spread of COVID-19. Google Health Trends (GHT), a free Internet tool, may be valuable to help anticipate outbreaks, identify disease hotspots, or understand the patterns of disease surveillance. We collected COVID-19 case and death incidence for 54 African countries and obtained averages for four, five-month study periods in 2020–2021. Average case and death incidences were calculated during these four time periods to measure disease severity. We used GHT to characterize COVID-19 incidence across Africa, collecting numbers of searches from GHT related to COVID-19 using four terms: ‘coronavirus’, ‘coronavirus symptoms’, ‘COVID19’, and ‘pandemic’. The terms were related to weekly COVID-19 case incidences for the entire study period via multiple linear and weighted linear regression analyses. We also assembled 72 variables assessing Internet accessibility, demographics, economics, health, and others, for each country, to summarize potential mechanisms linking GHT searches and COVID-19 incidence. COVID-19 burden in Africa increased steadily during the study period. Important increases for COVID-19 death incidence were observed for Seychelles and Tunisia. Our study demonstrated a weak correlation between GHT and COVID-19 incidence for most African countries. Several variables seemed useful in explaining the pattern of GHT statistics and their relationship to COVID-19 including: log of average weekly cases, log of cumulative total deaths, and log of fixed total number of broadband subscriptions in a country. Apparently, GHT may best be used for surveillance of diseases that are diagnosed more consistently. Overall, GHT-based surveillance showed little applicability in the studied countries. GHT for an ongoing epidemic might be useful in specific situations, such as when countries have significant levels of infection with low variability. Future studies might assess the algorithm in different epidemic contexts.
Collapse
|
7
|
Miran W, Long X, Huang W, Okamoto A. Current Production Capability of Drug-Resistant Pathogen Enables Its Rapid Label-Free Detection Applicable to Wastewater-Based Epidemiology. Microorganisms 2022; 10:microorganisms10020472. [PMID: 35208926 PMCID: PMC8875581 DOI: 10.3390/microorganisms10020472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/09/2022] [Accepted: 02/11/2022] [Indexed: 02/01/2023] Open
Abstract
A rapid and label-free method for the detection of drug-resistant pathogens is in high demand for wastewater-based epidemiology. As recently shown, the extent of electrical current production (Ic) is a useful indicator of a pathogen's metabolic activity. Therefore, if drug-resistant bacteria have extracellular electron transport (EET) capability, a simple electric sensor may be able to detect not only the growth as a conventional plating technique but also metabolic activity specific for drug-resistant bacteria in the presence of antibiotics. Here, one of the multidrug-resistant pathogens in wastewater, Klebsiella pneumoniae, was shown to generate Ic, and the extent of Ic was unaffected by the microbial growth inhibitor, kanamycin, while the current was markedly decreased in environmental EET bacteria Shewanella oneidensis. Kanamycin differentiated Ic in K. pneumonia and S. oneidensis within 3 h. Furthermore, the detection of K. pneumoniae was successful in the presence of S. oneidensis in the electrochemical cell. These results clarify the advantage of detecting drug-resistant bacteria using whole-cell electrochemistry as a simple and rapid method to detect on-site drug-resistant pathogens in wastewater, compared with conventional colony counting, which takes a few days.
Collapse
Affiliation(s)
- Waheed Miran
- School of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan;
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan; (X.L.); (W.H.)
| | - Xizi Long
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan; (X.L.); (W.H.)
| | - Wenyuan Huang
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan; (X.L.); (W.H.)
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo 060-8628, Japan
| | - Akihiro Okamoto
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan; (X.L.); (W.H.)
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo 060-8628, Japan
- Correspondence:
| |
Collapse
|
8
|
Martínez-García M, Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med (Lausanne) 2022; 8:784455. [PMID: 35145977 PMCID: PMC8821900 DOI: 10.3389/fmed.2021.784455] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/19/2022] Open
Abstract
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
Collapse
Affiliation(s)
- Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology ‘Ignacio Chávez’, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autnoma de Mexico, Mexico City, Mexico
| |
Collapse
|
9
|
White T, Blok E, Calhoun VD. Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Hum Brain Mapp 2022; 43:278-291. [PMID: 32621651 PMCID: PMC8675413 DOI: 10.1002/hbm.25120] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/12/2020] [Accepted: 06/22/2020] [Indexed: 12/19/2022] Open
Abstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
Collapse
Affiliation(s)
- Tonya White
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of RadiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Elisabet Blok
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
10
|
Grosche VR, Santos IA, Ferreira GM, Dutra JVR, Costa LC, Nicolau-Junior N, Queiroz ATL, José DP, Jardim ACG. Insights on the SARS-CoV-2 genome variability: the lesson learned in Brazil and its impacts on the future of pandemics. Microb Genom 2021; 7:000656. [PMID: 34730486 PMCID: PMC8743548 DOI: 10.1099/mgen.0.000656] [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: 01/13/2021] [Accepted: 07/19/2021] [Indexed: 11/18/2022] Open
Abstract
Since the beginning of the SARS-CoV-2 spread in Brazil, few studies have been published analysing the variability of viral genome. Herein, we described the dynamic of SARS-CoV-2 strains circulating in Brazil from May to September 2020, to better understand viral changes that may affect the ongoing pandemic. Our data demonstrate that some of the mutations identified are currently observed in variants of interest and variants of concern, and emphasize the importance of studying previous periods in order to comprehend the emergence of new variants. From 720 SARS-CoV-2 genome sequences, we found few sites under positive selection pressure, such as the D614G (98.5 %) in the spike, that has replaced the old variant; the V1167F in the spike (41 %), identified in the P.2 variant that emerged from Brazil during the period of analysis; and I292T (39 %) in the N protein. There were a few alterations in the UTRs, which was expected, however, our data suggest that the emergence of new variants was not influenced by mutations in UTR regions, since it maintained its conformational structure in most analysed sequences. In phylogenetic analysis, the spread of SARS-CoV-2 from the large urban centres to the countryside during these months could be explained by the flexibilization of social isolation measures and also could be associated with possible new waves of infection. These results allow a better understanding of SARS-CoV-2 strains that have circulated in Brazil, and thus, with relevant infomation, provide the potential viral changes that may have affected and/or contributed to the current and future scenario of the COVID-19 pandemic.
Collapse
Affiliation(s)
- Victória Riquena Grosche
- São Paulo State University, São José do Rio Preto, São Paulo, Brazil
- Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | | | | | | | - Larissa Catharina Costa
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
| | | | - Artur Trancoso Lopo Queiroz
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
| | - Diego Pandeló José
- Federal University of Triângulo Mineiro, Campus Universitário Iturama, Iturama, Minas Gerais, Brazil
| | - Ana Carolina Gomes Jardim
- São Paulo State University, São José do Rio Preto, São Paulo, Brazil
- Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| |
Collapse
|
11
|
Pecoraro F, Luzi D. Open Data Resources on COVID-19 in Six European Countries: Issues and Opportunities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:10496. [PMID: 34639796 PMCID: PMC8507931 DOI: 10.3390/ijerph181910496] [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] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/28/2022]
Abstract
Since the beginning of the COVID-19 pandemic in March 2020, national and international authorities started to develop and update datasets to provide data to researchers, journalists and health care providers as well as public opinion. These data became one of the most important sources of information, which are updated daily and analysed by scientists in order to investigate and predict the spread of this epidemic. Despite this positive reaction from both national and international authorities in providing aggregated information on the diffusion of COVID-19, different challenges have been underlined in previously published studies. Different papers have discussed strengths and weaknesses of these types of datasets by focusing on different quality perspectives, which include the statistical methods adopted to analyse them; the lack of standards and models in the adoption of data for their management and distribution; and the analysis of different data quality characteristics. These studies have analysed datasets at the general level or by focusing the attention on specific indicators such as the number of cases or deaths. This paper further investigates issues and opportunities in the diffusion of these datasets under two main perspectives. At the general level, it analyses how data are organized and distributed to scientific and non-scientific communities. Moreover, it further explores the indicators adopted to describe the spread of the COVID-19 epidemic while also highlighting the level of detail used to describe them in terms of gender, age ranges and territorial units. The paper focuses on six European countries: Belgium, France, Germany, Italy, Spain and UK.
Collapse
Affiliation(s)
- Fabrizio Pecoraro
- Institute for Research on Population and Social Policies, National Research Council, Via Palestro, 32, 00185 Rome, Italy;
| | | |
Collapse
|
12
|
Mukherjee AK, Mackessy SP. Prevention and improvement of clinical management of snakebite in Southern Asian countries: A proposed road map. Toxicon 2021; 200:140-152. [PMID: 34280412 DOI: 10.1016/j.toxicon.2021.07.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/10/2021] [Accepted: 07/13/2021] [Indexed: 10/20/2022]
Abstract
In the Southern Asian countries, snakebite takes a substantial toll in terms of human life, inflicts acute morbidity and long term disability both physical and psychological, and therefore represents a neglected socio-economic problem and severe health issue that requires immediate medical attention. The 'Big Four' venomous snakes, viz. Daboia russelii, Naja naja, Bungarus caeruleus and Echis carinatus, are prominent, medically important species and are the most dangerous snakes of this region; therefore, the commercial polyvalent antivenom (PAV) contains antibodies against the venoms of these snakes. However, envenomations by species other than the 'Big Four' snakes are grossly neglected, and PAV is only partially effective in neutralizing the venom of these snakes. Many issues confounding effective treatment of snakebite are discussed in this review, and these hurdles preventing successful treatment of snakebite must be addressed. However, in South Asian countries, the pre-hospital treatment and appropriate first aid are equally important to mitigate the problem of snakebite and therefore, these issues are also highlighted here. Further, this review suggests a roadmap and guidelines for the prevention of snakebite and improvement of hospital management of snakebite in these Southern Asian countries.
Collapse
Affiliation(s)
- Ashis K Mukherjee
- Division of Life Sciences, Institute of Advanced Study in Science and Technology, Vigyan Path Garchuk, Paschim Boragaon, Guwahati, 781035, Assam, India; Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, 78028, Assam, India; School of Biological Sciences, University of Northern Colorado, Greeley, CO, 80639-0017, USA.
| | - Stephen P Mackessy
- School of Biological Sciences, University of Northern Colorado, Greeley, CO, 80639-0017, USA
| |
Collapse
|
13
|
Badker R, Miller K, Pardee C, Oppenheim B, Stephenson N, Ash B, Philippsen T, Ngoon C, Savage P, Lam C, Madhav N. Challenges in reported COVID-19 data: best practices and recommendations for future epidemics. BMJ Glob Health 2021; 6:bmjgh-2021-005542. [PMID: 33958393 PMCID: PMC8103560 DOI: 10.1136/bmjgh-2021-005542] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 12/26/2022] Open
Abstract
The proliferation of composite data sources tracking the COVID-19 pandemic emphasises the need for such databases during large-scale infectious disease events as well as the potential pitfalls due to the challenges of combining disparate data sources. Multiple organisations have attempted to standardise the compilation of disparate data from multiple sources during the COVID-19 pandemic. However, each composite data source can use a different approach to compile data and address data issues with varying results. We discuss some best practices for researchers endeavouring to create such compilations while discussing three key categories of challenges: (1) data dissemination, which includes discrepant estimates and varying data structures due to multiple agencies and reporting sources generating public health statistics on the same event; (2) data elements, such as date formats and location names, lack standardisation, and differing spatial and temporal resolutions often create challenges when combining sources; and (3) epidemiological factors, including missing data, reporting lags, retrospective data corrections and changes to case definitions that cannot easily be addressed by the data compiler but must be kept in mind when reviewing the data. Efforts to reform the global health data ecosystem should bear such challenges in mind. Standards and best practices should be developed and incorporated to yield more robust, transparent and interoperable data. Since no standards exist yet, we have highlighted key challenges in creating a comprehensive spatiotemporal view of outbreaks from multiple, often discrepant, reporting sources and provided guidelines to address them. In general, we caution against an over-reliance on fully automated systems for integrating surveillance data and strongly advise that epidemiological experts remain engaged in the process of data assessment, integration, validation and interpretation to identify, diagnose and resolve data challenges.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Tanya Philippsen
- Metabiota Inc, San Francisco, California, USA.,University of Victoria, Victoria, British Columbia, Canada
| | | | | | - Cathine Lam
- Metabiota Inc, San Francisco, California, USA
| | - Nita Madhav
- Metabiota Inc, San Francisco, California, USA
| |
Collapse
|
14
|
Galaitsi SE, Cegan JC, Volk K, Joyner M, Trump BD, Linkov I. The challenges of data usage for the United States' COVID-19 response. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2021; 59:102352. [PMID: 33824545 PMCID: PMC8017563 DOI: 10.1016/j.ijinfomgt.2021.102352] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/29/2021] [Accepted: 03/29/2021] [Indexed: 01/08/2023]
Abstract
During the coronavirus pandemic, policy makers need to interpret available public health data to make decisions affecting public health. However, the United States’ coronavirus response faced data gaps, inadequate and inconsistent definitions of data across different governmental jurisdictions, ambiguous timing in reporting, problems in accessing data, and changing interpretations from scientific institutions. These present numerous problems for the decision makers relying on this information. This paper documents some of the data pitfalls in coronavirus public health data reporting, as identified by the authors in the course of supporting data management for New England’s coronavirus response. We provide recommendations for individuals to collect data more effectively during emergency situations such as a COVID-19 surge, as well as recommendations for institutions to provide more meaningful data for various users to access. Through this, we hope to motivate action to avoid data pitfalls during public health responses in the future.
Collapse
Affiliation(s)
- S E Galaitsi
- U.S. Army Corps of Engineers, Vicksburg, MS, United States
| | | | - Kaitlin Volk
- U.S. Army Corps of Engineers, Vicksburg, MS, United States
| | - Matthew Joyner
- U.S. Army Corps of Engineers, Vicksburg, MS, United States
| | | | - Igor Linkov
- U.S. Army Corps of Engineers, Vicksburg, MS, United States
| |
Collapse
|
15
|
Hassan R, Dosar AS, Mondol JK, Khan TH, Noman AA, Sayem MS, Hasan M, Juyena NS. Prediction of Epidemics Trend of COVID-19 in Bangladesh. Front Public Health 2020; 8:559437. [PMID: 33330309 PMCID: PMC7734053 DOI: 10.3389/fpubh.2020.559437] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 09/15/2020] [Indexed: 01/10/2023] Open
Abstract
Background: Amid a critical and emergent situation like the coronavirus disease (COVID-19) pandemic related to extreme health and economic repercussions, we used and presented the mathematical modeling like susceptible-infectious-recovered (SIR) to have a numerical demonstration that can shed light to decide the fate of the scourge in Bangladesh. To describe the idea about the factors influencing the outbreak data, we presented the current situation of the COVID-19 outbreak with graphical trends. Methods: Primary data were collected and analyzed by using a pre-created Google Survey form having a pre-set questionnaire on the social distancing status of different districts. Secondary data on the total and the daily number of laboratory tests, confirmed positive cases, and death cases were extracted from the publicly available sources to make predictions. We estimated the basic reproduction number (R◦) based on the SIR mathematical model and predicted the probable fate of this pandemic in Bangladesh. Results: Quarantine situations in different regions of Bangladesh were evaluated and presented. We also provided tentative forecasts until 31 May 2020 and found that the predicted curve followed the actual curve approximately. Estimated R◦-values (6.924) indicated that infection rate would be greater than the recovery rate. Furthermore, by calibrating the parameters of the SIR model to fit the reported data, we assume the ultimate ending of the pandemic in Bangladesh by December 2022. Conclusion: We hope that the results of our analysis could contribute to the elucidation of critical aspects of this outbreak and help the concerned authority toward decision making.
Collapse
Affiliation(s)
- Raguib Hassan
- Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh, Bangladesh
| | - Abu Sayem Dosar
- Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh, Bangladesh
| | - Joytu Kumar Mondol
- Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh, Bangladesh
| | - Tahmid Hassan Khan
- Faculty of Agricultural Engineering and Technology, Bangladesh Agricultural University, Mymensingh, Bangladesh
| | - Abdullah Al Noman
- Faculty of Agricultural Engineering and Technology, Bangladesh Agricultural University, Mymensingh, Bangladesh
| | - Mirajus Salehin Sayem
- Faculty of Agricultural Engineering and Technology, Bangladesh Agricultural University, Mymensingh, Bangladesh
| | - Moinul Hasan
- Department of Surgery and Obstetrics, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh, Bangladesh
| | - Nasrin Sultana Juyena
- Department of Surgery and Obstetrics, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh, Bangladesh
| |
Collapse
|
16
|
Peddireddy AS, Xie D, Patil P, Wilson ML, Machi D, Venkatramanan S, Klahn B, Porebski P, Bhattacharya P, Dumbre S, Raymond E, Marathe M. From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.10.27.20220830. [PMID: 33140060 PMCID: PMC7605571 DOI: 10.1101/2020.10.27.20220830] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper, we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid-19/dashboard/, which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling - an important emerging area in data science.
Collapse
Affiliation(s)
| | - Dawen Xie
- Biocomplexity Institute & Initiative, University of Virginia
| | | | - Mandy L Wilson
- Biocomplexity Institute & Initiative, University of Virginia
| | - Dustin Machi
- Biocomplexity Institute & Initiative, University of Virginia
| | | | - Brian Klahn
- Biocomplexity Institute & Initiative, University of Virginia
| | | | | | | | - Erin Raymond
- Biocomplexity Institute & Initiative, University of Virginia
| | - Madhav Marathe
- Biocomplexity Institute & Initiative, University of Virginia
| |
Collapse
|
17
|
Chitanvis M, Daughton AR, Altherr F, Parikh N, Fairchild G, Rosenberger W, Velappan N, Hollander A, Alipio-Lyon E, Vuyisich G, Aberle D, Deshpande A. Development of a Supervised Learning Algorithm for Detection of Potential Disease Reemergence: A Proof of Concept. Health Secur 2020; 17:255-267. [PMID: 31433278 DOI: 10.1089/hs.2019.0020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.
Collapse
Affiliation(s)
- Maneesha Chitanvis
- Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Ashlynn R Daughton
- Ashlynn R. Daughton, MPH, Nidhi Parikh, PhD, Geoffrey Fairchild, PhD, and William Rosenberger are Scientists, Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Forest Altherr
- Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Nidhi Parikh
- Ashlynn R. Daughton, MPH, Nidhi Parikh, PhD, Geoffrey Fairchild, PhD, and William Rosenberger are Scientists, Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Geoffrey Fairchild
- Ashlynn R. Daughton, MPH, Nidhi Parikh, PhD, Geoffrey Fairchild, PhD, and William Rosenberger are Scientists, Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM
| | - William Rosenberger
- Ashlynn R. Daughton, MPH, Nidhi Parikh, PhD, Geoffrey Fairchild, PhD, and William Rosenberger are Scientists, Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Nileena Velappan
- Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Attelia Hollander
- Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Emily Alipio-Lyon
- Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Grace Vuyisich
- Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Derek Aberle
- Derek Aberle is a Software Developer, Applied Modern Physics, Physics Division, Los Alamos National Laboratory, Los Alamos, NM
| | - Alina Deshpande
- Maneesha Chitanvis, MPH, and Forest Altherr, MPH, are Graduate Research Assistants; Nileena Velappan, MS, Attelia Hollander, Emily Alipio-Lyon, and Grace Vuyisich are Research Technologists; and Alina Deshpande, PhD, is Group Leader; all in Biosecurity and Public Health, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM
| |
Collapse
|
18
|
Romero-Alvarez D, Parikh N, Osthus D, Martinez K, Generous N, Del Valle S, Manore CA. Google Health Trends performance reflecting dengue incidence for the Brazilian states. BMC Infect Dis 2020; 20:252. [PMID: 32228508 PMCID: PMC7104526 DOI: 10.1186/s12879-020-04957-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 03/10/2020] [Indexed: 12/14/2022] Open
Abstract
Background Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags. Methods We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective. Results From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index. Conclusions The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.
Collapse
Affiliation(s)
- Daniel Romero-Alvarez
- Department of Ecology & Evolutionary Biology and Biodiversity Institute, University of Kansas, Lawrence, Kansas, USA. .,Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Nidhi Parikh
- Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Dave Osthus
- Statistical Sciences (CCS-6), Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Kaitlyn Martinez
- Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA.,Applied Math and Statistics, Colorado School of Mines, Golden, CO, USA
| | - Nicholas Generous
- National Security & Defense Program Office (GS-NSD), Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Sara Del Valle
- Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Carrie A Manore
- Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA
| |
Collapse
|