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de Oliveira EV, Aragão DP, Gonçalves LMG. A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:497. [PMID: 38673408 PMCID: PMC11049878 DOI: 10.3390/ijerph21040497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024]
Abstract
The SARS-CoV-2 global pandemic prompted governments, institutions, and researchers to investigate its impact, developing strategies based on general indicators to make the most precise predictions possible. Approaches based on epidemiological models were used but the outcomes demonstrated forecasting with uncertainty due to insufficient or missing data. Besides the lack of data, machine-learning models including random forest, support vector regression, LSTM, Auto-encoders, and traditional time-series models such as Prophet and ARIMA were employed in the task, achieving remarkable results with limited effectiveness. Some of these methodologies have precision constraints in dealing with multi-variable inputs, which are important for problems like pandemics that require short and long-term forecasting. Given the under-supply in this scenario, we propose a novel approach for time-series prediction based on stacking auto-encoder structures using three variations of the same model for the training step and weight adjustment to evaluate its forecasting performance. We conducted comparison experiments with previously published data on COVID-19 cases, deaths, temperature, humidity, and air quality index (AQI) in São Paulo City, Brazil. Additionally, we used the percentage of COVID-19 cases from the top ten affected countries worldwide until May 4th, 2020. The results show 80.7% and 10.3% decrease in RMSE to entire and test data over the distribution of 50 trial-trained models, respectively, compared to the first experiment comparison. Also, model type#3 achieved 4th better overall ranking performance, overcoming the NBEATS, Prophet, and Glounts time-series models in the second experiment comparison. This model shows promising forecast capacity and versatility across different input dataset lengths, making it a prominent forecasting model for time-series tasks.
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Affiliation(s)
| | | | - Luiz Marcos Garcia Gonçalves
- Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Av. Salgado Filho, 3000, Campus Universitário, Lagoa Nova, Natal 59078-970, RN, Brazil; (E.V.d.O.); (D.P.A.)
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2
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Koichubekov B, Takuadina A, Korshukov I, Sorokina M, Turmukhambetova A. The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling. Healthcare (Basel) 2023; 11:2968. [PMID: 37998460 PMCID: PMC10671669 DOI: 10.3390/healthcare11222968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. MATERIALS AND METHODS We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. RESULTS Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection's spread was highest within families, with most COVID-19 cases occurring in the 26-59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a "no intervention" scenario, yielding an estimated economic benefit of 40%. CONCLUSION The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.
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Affiliation(s)
- Berik Koichubekov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Aliya Takuadina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Ilya Korshukov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Marina Sorokina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Anar Turmukhambetova
- Institute of Life Sciences, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan;
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Shamsi Gamchi N, Esmaeili M. A novel mathematical model for prioritization of individuals to receive vaccine considering governmental health protocols. THE EUROPEAN JOURNAL OF HEALTH ECONOMICS : HEPAC : HEALTH ECONOMICS IN PREVENTION AND CARE 2023; 24:633-646. [PMID: 35900675 PMCID: PMC9330986 DOI: 10.1007/s10198-022-01491-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 06/09/2022] [Indexed: 05/12/2023]
Abstract
Infectious diseases drive countries to provide vaccines to individuals. Due to the limited supply of vaccines, individuals prioritize receiving vaccinations worldwide. Although, priority groups are formed based on age groupings due to the restricted decision-making time. Governments usually ordain different health protocols such as lockdown policy, mandatory use of face masks, and vaccination during the pandemics. Therefore, this study considers the case of COVID-19 with a SEQIR (susceptible-exposed-quarantined-infected-recovered) epidemic model and presents a novel prioritization technique to minimize the social and economic impacts of the lockdown policy. We use retail units as one of the affected parts to demonstrate how a vaccination plan may be more effective if individuals such as retailers were prioritized and age groups. In addition, we estimate the total required vaccine doses to control the epidemic disease and compute the number of vaccine doses supplied by various suppliers. The vaccine doses are determined using optimal control theory in the solution technique. In addition, we consider the effect of the mask using policy in the number of vaccine doses allocated to each priority group. The model's performance is evaluated using an illustrative scenario based on a real case.
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Affiliation(s)
- N Shamsi Gamchi
- Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
| | - M Esmaeili
- Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran.
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Guo K, Lu Y, Geng Y, Lu J, Shi L. Assessing the medical resources in COVID-19 based on evolutionary game. PLoS One 2023; 18:e0280067. [PMID: 36630442 PMCID: PMC9833555 DOI: 10.1371/journal.pone.0280067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 12/20/2022] [Indexed: 01/12/2023] Open
Abstract
COVID-19 has brought a great challenge to the medical system. A key scientific question is how to make a balance between home quarantine and staying in the hospital. To this end, we propose a game-based susceptible-exposed-asymptomatic -symptomatic- hospitalized-recovery-dead model to reveal such a situation. In this new framework, time-varying cure rate and mortality are employed and a parameter m is introduced to regulate the probability that individuals are willing to go to the hospital. Through extensive simulations, we find that (1) for low transmission rates (β < 0.2), the high value of m (the willingness to stay in the hospital) indicates the full use of medical resources, and thus the pandemic can be easily contained; (2) for high transmission rates (β > 0.2), large values of m lead to breakdown of the healthcare system, which will further increase the cumulative number of confirmed cases and death cases. Finally, we conduct the empirical analysis using the data from Japan and other typical countries to illustrate the proposed model and to test how our model explains reality.
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Affiliation(s)
- Keyu Guo
- Information School, The University of Sheffield, Sheffield, United Kingdom
| | - Yikang Lu
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China
| | - Yini Geng
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Jun Lu
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China
| | - Lei Shi
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China
- Interdisciplinary Research Institute of Data Science, Shanghai Lixin University of Accounting and Finance, Shanghai, China
- * E-mail:
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Ansatbayeva T, Kaidarova D, Kunirova G, Khussainova I, Rakhmetova V, Smailova D, Semenova Y, Glushkova N, Izmailovich M. Early integration of palliative care into oncological care: a focus on patient-important outcomes. Int J Palliat Nurs 2022; 28:366-375. [PMID: 36006790 DOI: 10.12968/ijpn.2022.28.8.366] [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: 11/11/2022]
Abstract
BACKGROUND Globally, cancer remains one of the leading causes of mortality. Palliative care is designed to meet a range of cancer patients' priority issues, including the management of pain and other cancer-associated symptoms. Routine palliative care envisages the provision of not just medical therapy, but also psychological support, social support and spiritual assistance. What constitutes the best model for palliative care remains a matter of debate. AIM This review was undertaken with the aim to discuss different aspects of early integration of palliative care into oncological care, with a focus on patient-important outcomes. METHODS A comprehensive search of publications was conducted with a focus on integrative palliative care for incurable cancer patients. For this purpose, the following databases and search engines were used: Scopus, PubMed, Cochrane Library, Research Gate, Google Scholar, eLIBRARY and Cyberleninka. RESULTS A comprehensive approach with early integration of different medical services appears to be the most promising. Integrative palliative care is best provided via specialised interdisciplinary teams, given that all members maintain systemic communications and regularly exchange information. This model ensures that timely and adequate interventions are provided to address the needs of patients. CONCLUSION Further research is needed to pinpoint the most optimal strategies to deliver palliative care and make it as tailored to the patient's demands as possible.
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Affiliation(s)
- Tolganay Ansatbayeva
- Asfendiyarov Kazakh National Medical University; Oncologist of a Mobile Palliative Home Care Team, City Oncological Center of Almaty, Kazakhstan
| | - Dilyara Kaidarova
- Doctor of Medicine; Professor; Academician of the National Academy of Sciences of the Republic of Kazakhstan; Chairperson of the Board, JSC Kazakh Institute of Oncology and Radiology; Head of the Oncology Department, JSC Asfendiyarov Kazakh National Medical University, Kazakhstan
| | - Gulnara Kunirova
- President, Kazakhstan Association for Palliative Care Board of Directors, International Association for Hospice and Palliative Care; Executive Director, Together Against Cancer, Kazakhstan
| | - Ilmira Khussainova
- Assistant Professor of General and Applied Psychology, al-Farabi Kazakh National University; Head of the Department of Psychological and Social Assistance, Kazakh Insititute of Oncology and Radiology, Kazakhstan
| | - Venera Rakhmetova
- Professor of Department of Internal Diseases, Astana Medical University, Kazakhstan
| | - Dariga Smailova
- Head of Department of Epidemiology, Evidence-based Medicine and Biostatistics, Kazakhstan School of Public Health, Kazakhstan
| | - Yuliya Semenova
- Assistant Professor, Nazarbayev University School of Medicine, Kazakhstan
| | - Natalya Glushkova
- Associate Professor of the Department of Epidemiology, Biostatistics and Evidence Based Medicine, Al-Farabi Kazakh National University, Kazakhstan
| | - Marina Izmailovich
- Assistant Professor, Department of Internal Diseases, Karaganda Medical University, Kazakhstan
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Semenova Y, Trenina V, Pivina L, Glushkova N, Zhunussov Y, Ospanov E, Bjørklund G. The lessons of COVID-19, SARS, and MERS: Implications for preventive strategies. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2051126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yuliya Semenova
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
- CONEM Kazakhstan Environmental Health and Safety Research Group, Semey Medical University, Semey, Kazakhstan
| | - Varvara Trenina
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
| | - Lyudmila Pivina
- CONEM Kazakhstan Environmental Health and Safety Research Group, Semey Medical University, Semey, Kazakhstan
- Department of Emergency Medicine, Semey Medical University, Semey, Kazakhstan
| | - Natalya Glushkova
- Department of Epidemiology, Biostatistics & Evidence Based Medicine, Al-Farabi Kazakh National University, Almaty, Kazakhstan
| | | | - Erlan Ospanov
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
| | - Geir Bjørklund
- Council for Nutritional and Environmental Medicine (CONEM), Mo i Rana, Norway
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Olsen F, Schillaci C, Ibrahim M, Lipani A. Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran's I loss function. RESULTS IN PHYSICS 2022; 35:105374. [PMID: 35228988 PMCID: PMC8865939 DOI: 10.1016/j.rinp.2022.105374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/20/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
Following its identification in late 2019, COVID-19 has spread around the globe, and been declared a pandemic. With this in mind, modelling the spread of COVID-19 remains important for responding effectively. To date research has focused primarily on modelling the spread of COVID-19 on national and regional scales with just a few studies doing so on a city and sub-city scale. However, no attempts have yet been made to design and optimize a model explicitly for accurately forecasting the spread of COVID-19 at sub-city scale. This research aimed to address this research gap by developing an experimental LSTM-ANN deep learning model. The model is largely autoregressive in nature as it considers temporally lagged borough-level COVID-19 cases data from the last 9 days, but also considers temporally lagged (i) borough-level NO2 concentration data, (ii) government stringency data, and (iii) climatic data from the last 9 days, as well as non-temporally variable borough-level urban characteristics data when modelling and forecasting the spread of the disease. The model was also encouraged to learn the spatial relationships between boroughs with regards to the spread of COVID-19 by a novel MSE-Moran's I loss function. Overall, the model's performance appears promising and so the model represents a useful tool for assisting the decision making and interventions of governing bodies within cities. A sensitivity analysis also indicated that of the non COVID-19 variables, the government stringency is particularly important in the modelling process, with this being closely followed by the climatic variables, the NO2 concentration data, and finally the urban characteristics data. Additionally, the introduction of the novel MSE-Moran's I loss function appeared to improve the model's forecasting performance, and so this research has implications at the intersection of deep learning and disease modelling. It may also have implications within spatio-temporal forecasting more generally because such a feature may have the potential to improve forecasting in other spatio-temporal applications.
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Affiliation(s)
- Frederik Olsen
- Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), England
| | - Calogero Schillaci
- Department of Agricultural and Environmental Science, University of Milan, Via Celoria 2 Milan, Italy
| | - Mohamed Ibrahim
- Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), England
| | - Aldo Lipani
- Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), England
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Zhamankulov A, Rozenson R, Morenko M, Akhmetova U, Tyo A, Poddighe D. Comparison between SARS-CoV-2 positive and negative pneumonia in children: A retrospective analysis at the beginning of the pandemic. World J Exp Med 2022; 12:26-35. [PMID: 35433317 PMCID: PMC8968470 DOI: 10.5493/wjem.v12.i2.26] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/29/2021] [Accepted: 02/27/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Even though coronavirus 2019 disease (COVID-19) clinical course in children is much milder than in adults, pneumonia can occur in the pediatric population as well. Here, we reported a single-center pediatric case series of COVID-19 from Kazakhstan during the first wave of pandemic.
AIM To analyze the main clinical and laboratory aspects in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) positive and negative children diagnosed with pneumonia.
METHODS This is a retrospective analysis of 54 children, who were medically assessed as close contacts of COVID-19 adults in their family setting, between June and September 2020. These children were all hospitalized: We compared the clinical and laboratory characteristics of children affected with pneumonia in the presence (group 1) or absence (group 2) of SARS-CoV-2 infection.
RESULTS Overall, the main clinical manifestations at the admission were fever, cough, loss of appetite, fatigue/weakness, nasal congestion and/or rhinorrhea, and dyspnea. Based on the SARS-CoV-2 polymerase chain reaction (PCR) test, 24 positive children with pneumonia (group 1) and 20 negative children with pneumonia (group 2) were identified; 10 positive children did not show any radiological findings of pneumonia. No significant differences were found between the two pneumonia study groups for any clinical and laboratory parameters, except for C-reactive protein (CRP). Of course, both pneumonia groups showed increased CRP values; however, the COVID-19 pneumonia group 1 showed a significantly higher increase of CRP compared to group 2.
CONCLUSION In our case series of children assessed for SARS-CoV-2 infection based on contact tracing, the acute inflammatory response and, in detail, CRP increase resulted to be more pronounced in COVID-19 children with pneumonia than in children with SARS-CoV-2-unrelated pneumonia. However, because of multiple limitations of this study, larger, controlled and more complete clinical studies are needed to verify this finding.
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Affiliation(s)
- Adil Zhamankulov
- Department of Children's diseases, Astana Medical University, First Children's Municipal Hospital, Nur-Sultan 010000, Kazakhstan
| | - Rafail Rozenson
- Department of Children's diseases, Astana Medical University, First Children's Municipal Hospital, Nur-Sultan 010000, Kazakhstan
| | - Marina Morenko
- Department of Children's diseases, Astana Medical University, First Children's Municipal Hospital, Nur-Sultan 010000, Kazakhstan
| | - Ulzhan Akhmetova
- Department of Children's diseases, Astana Medical University, First Children's Municipal Hospital, Nur-Sultan 010000, Kazakhstan
| | - Alina Tyo
- Department of Children's diseases, Astana Medical University, First Children's Municipal Hospital, Nur-Sultan 010000, Kazakhstan
| | - Dimitri Poddighe
- Clinical Academic Department of Pediatrics, National Research Center for Maternal and Child Health, University Medical Center, Nur-Sultan 010000, Kazakhstan
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Semenova Y, Kalmatayeva Z, Oshibayeva A, Mamyrbekova S, Kudirbekova A, Nurbakyt A, Baizhaxynova A, Colet P, Glushkova N, Ivankov A, Sarria-Santamera A. Seropositivity of SARS-CoV-2 in the Population of Kazakhstan: A Nationwide Laboratory-Based Surveillance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042263. [PMID: 35206453 PMCID: PMC8872132 DOI: 10.3390/ijerph19042263] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/02/2022] [Accepted: 01/21/2022] [Indexed: 02/01/2023]
Abstract
The data on seroprevalence of anti-SARS-CoV-2 antibodies in Kazakhstani population are non-existent, but are needed for planning of public health interventions targeted to COVID-19 containment. The aim of the study was to estimate the seropositivity of SARS-CoV-2 infection in the Kazakhstani population from 2020 to 2021. We relied on the data obtained from the results from “IN VITRO” laboratories of enzyme-linked immunosorbent assays for class G immunoglobulins (IgG) and class M (IgM) to SARS-CoV-2. The association of COVID-19 seropositivity was assessed in relation to age, gender, and region of residence. Additionally, we related the monitoring of longitudinal seropositivity with COVID-19 statistics obtained from Our World in Data. The total numbers of tests were 68,732 for SARS-CoV-2 IgM and 85,346 for IgG, of which 22% and 63% were positive, respectively. The highest rates of positive anti-SARS-CoV-2 IgM results were seen during July/August 2020. The rate of IgM seropositivity was the lowest on 25 October 2020 (2%). The lowest daily rate of anti-SARS-CoV-2 IgG was 17% (13 December 2020), while the peak of IgG seropositivity was seen on 6 June 2021 (84%). A longitudinal serological study should be envisaged to facilitate understanding of the dynamics of the epidemiological situation and to forecast future scenarios.
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Affiliation(s)
- Yuliya Semenova
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey 071400, Kazakhstan;
| | - Zhanna Kalmatayeva
- School of Medicine, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (Z.K.); (S.M.); (N.G.)
| | - Ainash Oshibayeva
- Administrative Office, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkestan 161204, Kazakhstan;
| | - Saltanat Mamyrbekova
- School of Medicine, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (Z.K.); (S.M.); (N.G.)
| | - Aynura Kudirbekova
- Invitro-Kazakhstan Laboratory, Medical Department, Almaty 050000, Kazakhstan;
| | - Ardak Nurbakyt
- Department of Public Health, Asfendiyarov Kazakh National Medical University, Almaty 050012, Kazakhstan;
| | - Ardak Baizhaxynova
- Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 020000, Kazakhstan; (A.B.); (P.C.)
| | - Paolo Colet
- Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 020000, Kazakhstan; (A.B.); (P.C.)
| | - Natalya Glushkova
- School of Medicine, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (Z.K.); (S.M.); (N.G.)
| | | | - Antonio Sarria-Santamera
- Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 020000, Kazakhstan; (A.B.); (P.C.)
- Correspondence:
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AlArjani A, Nasseef MT, Kamal SM, Rao BVS, Mahmud M, Uddin MS. Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022; 47:10163-10186. [PMID: 35018276 PMCID: PMC8739391 DOI: 10.1007/s13369-021-06419-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 11/17/2021] [Indexed: 12/23/2022]
Abstract
The entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper summarizes all the available mathematical models that have been used in predicting the transmission of COVID-19. A total of nine mathematical models have been thoroughly reviewed and characterized in this work, so as to understand the intrinsic properties of each model in predicting disease transmission dynamics. The application of these nine models in predicting COVID-19 transmission dynamics is presented with a case study, along with detailed comparisons of these models. Toward the end of the paper, key behavioral properties of each model, relevant challenges and future directions are discussed.
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Affiliation(s)
- Ali AlArjani
- Department of Mechanical & Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, AlKharj, 16273 Saudi Arabia
| | - Md Taufiq Nasseef
- Douglas Hospital Research Center, Department of Psychiatry, School of Medicine, McGill University, Montreal, QC Canada
| | - Sanaa M. Kamal
- Department of Internal Medicine, College of medicine, Prince Sattam Bin Abdulaziz University, AlKharj, 11942 Saudi Arabia
| | - B. V. Subba Rao
- Dept of Information Technology, PVP Siddhartha Institute of Technology, Chalasani Nagar, Kanuru, Vijayawada, Andhra Pradesh 520007 India
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton, Nottingham, NG11 8NS UK
- Medical Technologies Innovation Facility, Nottingham Trent University, Clifton, Nottingham, NG11 8NS UK
- Computing and Informatics Research Centre, Nottingham Trent University, Clifton, Nottingham, NG11 8NS UK
| | - Md Sharif Uddin
- Department of Mechanical & Industrial Engineering, Prince Sattam Bin Abdulaziz University, AlKharj, 16273 Saudi Arabia
- Department of Mathematics, Jahangirnagar University, Savar, Dhaka, 1342 Bangladesh
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11
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Kuznetsov A, Sadovskaya V. Spatial variation and hotspot detection of COVID-19 cases in Kazakhstan, 2020. Spat Spatiotemporal Epidemiol 2021; 39:100430. [PMID: 34774254 PMCID: PMC8096755 DOI: 10.1016/j.sste.2021.100430] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 04/29/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND COVID-19 is the life-threatening infectious disease of zoonotic origin that has epidemic spread in Kazakhstan. The use of geoepidemiological techniques to detect territories of risk (hotspots) is essential for implementing control measures in the target area. This study aims to conduct spatial analysis of the COVID-19 epidemic in Kazakhstan to increase understanding of the current features of the virus distribution and to explore its geographical patterns, especially its spatial clustering. METHODS We used geographic information software (QGIS, GeoDa) to perform spatial analysis (Nearest Neighbour Analysis, Global Moran's I, Getis-Ord Gi*, LISA) and to detect COVID-19 risk clusters in the entire territory of Kazakhstan. RESULTS Clusters of COVID-19 cases were detected using the Getis-Ord GI* analysis (with first order Queen Continuity matrix) in two oblasts of Kazakhstan: Almaty (Iliyskiy, Karasayskiy, Raiymbekskiy, Talgarskiy rayons and city of Almaty) and Aqmola (Arshalynskiy, Ereymengauskiy, Korgalzhynskiy and Shortandinskiy rayons). LISA defined four High-High clusters of COVID-19 cases in the Almaty oblast (Iliyskiy, Karasayskiy and Talgarskiy rayons) and city of Almaty.
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Affiliation(s)
- Andrey Kuznetsov
- National Scientific Center of Especially Dangerous Infections, Almaty, Kazakhstan.
| | - Veronika Sadovskaya
- National Scientific Center of Especially Dangerous Infections, Almaty, Kazakhstan
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Pivina L, Messova AM, Zhunussov YT, Urazalina Z, Muzdubayeva Z, Ygiyeva D, Muratoglu M, Batenova G, Uisenbayeva S, Semenova Y. Comparative Analysis Of Triage Systems At Emergency Departments Of Different Countries: Implementation In Kazakhstan. RUSSIAN OPEN MEDICAL JOURNAL 2021. [DOI: 10.15275/rusomj.2021.0301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Medical sorting is aimed at assessment of disease severity and has to be carried out within a short time to determine the priorities for patient care and transportation to the most appropriate place for future treatment. The goal of this study was to provide an integrative review by analyzing the publications on the most common triage systems worldwide in order to select and implement the most reliable system at emergency departments. We searched for publications relevant to our comparative analysis in evidence-based medicine databases. A total of 1,740 literary sources were identified, of which 42 were selected for analysis. Comparative analysis of different triage systems may help implementing the most efficient system in Kazakhstan. The Emergency Severity Index is considered the most reliable and accurate tool used in international practice, and it could provide a basis for introduction of triage system at emergency departments in Kazakhstan.
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Ahammed T, Anjum A, Rahman MM, Haider N, Kock R, Uddin MJ. Estimation of novel coronavirus (COVID-19) reproduction number and case fatality rate: A systematic review and meta-analysis. Health Sci Rep 2021; 4:e274. [PMID: 33977156 PMCID: PMC8093857 DOI: 10.1002/hsr2.274] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND AIMS Realizing the transmission potential and the magnitude of the coronavirus disease 2019 (COVID-19) aids public health monitoring, strategies, and preparation. Two fundamental parameters, the basic reproduction number (R 0) and case fatality rate (CFR) of COVID-19, help in this understanding process. The objective of this study was to estimate the R 0 and CFR of COVID-19 and assess whether the parameters vary in different regions of the world. METHODS We carried out a systematic review to find the reported estimates of the R 0 and the CFR in articles from international databases between January 1 and August 31, 2020. Random-effect models and Forest plots were implemented to evaluate the mean effect size of R 0 and the CFR. Furthermore, R 0 and CFR of the studies were quantified based on geographic location, the tests/thousand population, and the median population age of the countries where the studies were conducted. To assess statistical heterogeneity among the selected articles, the I 2 statistic and the Cochran's Q test were used. RESULTS Forty-five studies involving R 0 and 34 studies involving CFR were included. The pooled estimation of R 0 was 2.69 (95% CI: 2.40, 2.98), and that of the CFR was 2.67 (2.25, 3.13). The CFR in different regions of the world varied significantly, from 2.49 (2.08, 2.94) in Asia to 3.40 (2.81, 4.04) in North America. We observed higher mean CFR values for the countries with lower tests (3.15 vs 2.16) and greater median population age (3.13 vs 2.27). However, R 0 did not vary significantly in different regions of the world. CONCLUSIONS An R 0 of 2.69 and a CFR of 2.67 indicate the severity of the COVID-19. Although R 0 and CFR may vary over time, space, and demographics, we recommend considering these figures in control and prevention measures.
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Affiliation(s)
- Tanvir Ahammed
- Department of StatisticsShahjalal University of Science and TechnologySylhetBangladesh
| | - Aniqua Anjum
- Department of StatisticsShahjalal University of Science and TechnologySylhetBangladesh
| | - Mohammad Meshbahur Rahman
- Department of Health Statistics (Meta‐analysis & Geriatric Health)Biomedical Research FoundationDhakaBangladesh
| | - Najmul Haider
- The Royal Veterinary CollegeUniversity of LondonHertfordshireUnited Kingdom
| | - Richard Kock
- The Royal Veterinary CollegeUniversity of LondonHertfordshireUnited Kingdom
| | - Md Jamal Uddin
- Department of StatisticsShahjalal University of Science and TechnologySylhetBangladesh
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Zhussupov B, Saliev T, Sarybayeva G, Altynbekov K, Tanabayeva S, Altynbekov S, Tuleshova G, Pavalkis D, Fakhradiyev I. Analysis of COVID-19 pandemics in Kazakhstan. J Res Health Sci 2021; 21:e00512. [PMID: 34465636 PMCID: PMC8957677 DOI: 10.34172/jrhs.2021.52] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/05/2021] [Accepted: 05/09/2021] [Indexed: 12/17/2022] Open
Abstract
Background: This study aimed to analyze the demographic and epidemiological features of identified COVID-19 cases in Kazakhstan.
Study design: A cross-sectional study.
Methods: This cross-sectional study aimed to analyze COVID-19 cases (n=5116) collected from March 13 to June 6, 2020, in Kazakhstan. The data were obtained from a state official medical electronic database. The study investigated the geographic and demographic data of patients as well as the association of COVID-19 cases with gender and age. The prevalence of symptoms, the presence of comorbidities, complications, and COVID-19 mortality were determined for all patients.
Results: The mean ±SD age of the patients in this study was 34.8 ±17.6 years, and the majority (55.7%) of COVID-19 cases were male and residents of cities (79.6%). In total, 80% of the cases had the asymptomatic/mild form of the disease. Cough (20.8 %) and sore throat (17.1%) were the most common symptoms among patients, and pneumonia was diagnosed in 1 out of 5 cases. Acute respiratory distress syndrome (ARDS) was recorded in 1.2% of the patients. The fatality rate was 1% in the study population and lethality was 2.6 times higher in males compared to females. Each additional year in age increased the probability of COVID-19 infection by 1.06 times. The presence of cardiovascular, diabetes, respiratory, and kidney diseases affected the rate of mortality (P<0.05).
Conclusions: The results demonstrated a high proportion (40%) of the asymptomatic type of coronavirus infection in the Kazakhstan population. The severity of COVID-19 symptoms and lethality were directly related to the age of patients and the presence of comorbidities.
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Affiliation(s)
- Baurzhan Zhussupov
- National Center for Public Health, Nur-Sultan, Kazakhstan.,S. D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | - Timur Saliev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | | | - Kuanysh Altynbekov
- Republican Scientific and Practical Centre of Mental Health of the Ministry of Health of the Republic of Kazakhstan, Almaty, Kazakhstan
| | - Shynar Tanabayeva
- S. D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | - Sagat Altynbekov
- S. D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | | | | | - Ildar Fakhradiyev
- S. D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan.
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Yegorov S, Goremykina M, Ivanova R, Good SV, Babenko D, Shevtsov A, MacDonald KS, Zhunussov Y. Epidemiology, clinical characteristics, and virologic features of COVID-19 patients in Kazakhstan: A nation-wide retrospective cohort study. THE LANCET REGIONAL HEALTH. EUROPE 2021; 4:100096. [PMID: 33880458 PMCID: PMC8050615 DOI: 10.1016/j.lanepe.2021.100096] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The earliest coronavirus disease-2019 (COVID-19) cases in Central Asia were announced in March 2020 by Kazakhstan. Despite the implementation of aggressive measures to curb infection spread, gaps remain in the understanding of the clinical and epidemiologic features of the regional pandemic. METHODS We did a retrospective, observational cohort study of patients with laboratory-confirmed COVID-19 hospitalized in Kazakhstan between February and April 2020. We compared demographic, clinical, laboratory and radiological data of patients with different COVID-19 severities on admission. Logistic regression was used to assess factors associated with disease severity and in-hospital death. Whole-genome SARS-CoV-2 analysis was performed in 53 patients. FINDINGS Of the 1072 patients with laboratory-confirmed COVID-19 in March-April 2020, the median age was 36 years (IQR 24-50) and 484 (45%) were male. On admission, 683 (64%) participants had asymptomatic/mild, 341 (32%) moderate, and 47 (4%) severe-to-critical COVID-19 manifestation; 20 in-hospital deaths (1•87%) were reported by 5 May 2020. Multivariable regression indicated increasing odds of severe disease associated with older age (odds ratio 1•05, 95% CI 1•03-1•07, per year increase; p<0•001), the presence of comorbidities (2•34, 95% CI 1•18-4•85; p=0•017) and elevated white blood cell count (WBC, 1•13, 95% CI 1•00-1•27; p=0•044) on admission, while older age (1•09, 95% CI 1•06-1•13, per year increase; p<0•001) and male sex (5•63, 95% CI 2•06-17•57; p=0•001) were associated with increased odds of in-hospital death. The SARS-CoV-2 isolates grouped into seven phylogenetic lineages, O/B.4.1, S/A.2, S/B.1.1, G/B.1, GH/B.1.255, GH/B.1.3 and GR/B.1.1.10; 87% of the isolates were O and S sub-types descending from early Asian lineages, while the G, GH and GR isolates were related to lineages from Europe and the Americas. INTERPRETATION Older age, comorbidities, increased WBC count, and male sex were risk factors for COVID-19 disease severity and mortality in Kazakhstan. The broad SARS-CoV-2 diversity suggests multiple importations and community-level amplification predating travel restriction. FUNDING Ministry of Education and Science of the Republic of Kazakhstan.
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Affiliation(s)
- Sergey Yegorov
- School of Sciences and Humanities, Nazarbayev University, 53 Kabanbay Batyr Ave, Nur-Sultan 010000, Kazakhstan
- Michael G. DeGroote Institute for Infectious Disease Research, Faculty of Health Sciences, McMaster University, 1280 Main St. West, Hamilton, Ontario, L8S 4L8, Canada
| | - Maiya Goremykina
- Department of Rheumatology and Non-Infectious Diseases, Semey Medical University 103 Abai street, Semey 071400, Kazakhstan
| | - Raifa Ivanova
- Department of Rheumatology and Non-Infectious Diseases, Semey Medical University 103 Abai street, Semey 071400, Kazakhstan
| | - Sara V. Good
- Department of Biology, University of Winnipeg, 599 Portage Avenue, Winnipeg, Manitoba R3B2E9, Canada
| | - Dmitriy Babenko
- Department of Biology, University of Winnipeg, 599 Portage Avenue, Winnipeg, Manitoba R3B2E9, Canada
- Research Centre, Karaganda Medical University, 40 Gogol St, Karaganda, 100008 Kazakhstan
| | - Alexandr Shevtsov
- National Centre for Biotechnology, 13/5 Kurgalzhynskoye road, Nur-Sultan 010000, Kazakhstan
| | - COVID-19 Genomics Research Group
- School of Sciences and Humanities, Nazarbayev University, 53 Kabanbay Batyr Ave, Nur-Sultan 010000, Kazakhstan
- Department of Rheumatology and Non-Infectious Diseases, Semey Medical University 103 Abai street, Semey 071400, Kazakhstan
- Department of Biology, University of Winnipeg, 599 Portage Avenue, Winnipeg, Manitoba R3B2E9, Canada
- Research Centre, Karaganda Medical University, 40 Gogol St, Karaganda, 100008 Kazakhstan
- National Centre for Biotechnology, 13/5 Kurgalzhynskoye road, Nur-Sultan 010000, Kazakhstan
- Departments of Medicine, Microbiology & Immunology, Max Rady College of Medicine, University of Manitoba, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada
- JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, 745 Logan Avenue, Winnipeg, Manitoba R3E3L5, Canada
- Department of Public Health, Semey Medical University, 103 Abai street, Semey 071400, Kazakhstan
- Michael G. DeGroote Institute for Infectious Disease Research, Faculty of Health Sciences, McMaster University, 1280 Main St. West, Hamilton, Ontario, L8S 4L8, Canada
| | - Kelly S. MacDonald
- Departments of Medicine, Microbiology & Immunology, Max Rady College of Medicine, University of Manitoba, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada
- JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, 745 Logan Avenue, Winnipeg, Manitoba R3E3L5, Canada
| | - Yersin Zhunussov
- Department of Public Health, Semey Medical University, 103 Abai street, Semey 071400, Kazakhstan
| | - Semey COVID-19 Epidemiology Research Group
- School of Sciences and Humanities, Nazarbayev University, 53 Kabanbay Batyr Ave, Nur-Sultan 010000, Kazakhstan
- Department of Rheumatology and Non-Infectious Diseases, Semey Medical University 103 Abai street, Semey 071400, Kazakhstan
- Department of Biology, University of Winnipeg, 599 Portage Avenue, Winnipeg, Manitoba R3B2E9, Canada
- Research Centre, Karaganda Medical University, 40 Gogol St, Karaganda, 100008 Kazakhstan
- National Centre for Biotechnology, 13/5 Kurgalzhynskoye road, Nur-Sultan 010000, Kazakhstan
- Departments of Medicine, Microbiology & Immunology, Max Rady College of Medicine, University of Manitoba, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada
- JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, 745 Logan Avenue, Winnipeg, Manitoba R3E3L5, Canada
- Department of Public Health, Semey Medical University, 103 Abai street, Semey 071400, Kazakhstan
- Michael G. DeGroote Institute for Infectious Disease Research, Faculty of Health Sciences, McMaster University, 1280 Main St. West, Hamilton, Ontario, L8S 4L8, Canada
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Savaris RF, Pumi G, Dalzochio J, Kunst R. Stay-at-home policy is a case of exception fallacy: an internet-based ecological study. Sci Rep 2021; 11:5313. [PMID: 33674661 PMCID: PMC7935901 DOI: 10.1038/s41598-021-84092-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 02/01/2021] [Indexed: 12/16/2022] Open
Abstract
A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission. The second wave of cases in Europe, in regions that were considered as COVID-19 controlled, may raise some concerns. Our objective was to assess the association between staying at home (%) and the reduction/increase in the number of deaths due to COVID-19 in several regions in the world. In this ecological study, data from www.google.com/covid19/mobility/ , ourworldindata.org and covid.saude.gov.br were combined. Countries with > 100 deaths and with a Healthcare Access and Quality Index of ≥ 67 were included. Data were preprocessed and analyzed using the difference between number of deaths/million between 2 regions and the difference between the percentage of staying at home. The analysis was performed using linear regression with special attention to residual analysis. After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons after epidemiological weeks 9 to 34.
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Affiliation(s)
- R F Savaris
- School of Medicine, Department of Obstetrics and Gynecology, Universidade Federal do Rio Grande do Sul, Rua Ramiro Barcelos 2400, Porto Alegre, RS, CEP 90035-003, Brazil.
- Serv. Ginecologia e Obstetrícia, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, RS, CEP 90035-903, Brazil.
- Postgraduate of BigData, Data Science and Machine Learning Course, Unisinos, Porto Alegre, RS, Brazil.
| | - G Pumi
- Mathematics and Statistics Institute and Programa de Pós-Graduação em Estatística, Universidade Federal do Rio Grande do Sul, 9500, Bento Gonçalves Avenue, Porto Alegre, RS, 91509-900, Brazil
| | - J Dalzochio
- Applied Computing Graduate Program, University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos, 950, São Leopoldo, RS, 93022-750, Brazil
| | - R Kunst
- Applied Computing Graduate Program, University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos, 950, São Leopoldo, RS, 93022-750, Brazil
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Dyusupova A, Faizova R, Yurkovskaya O, Belyaeva T, Terekhova T, Khismetova A, Sarria-Santamera A, Bokov D, Ivankov A, Glushkova N. Clinical characteristics and risk factors for disease severity and mortality of COVID-19 patients with diabetes mellitus in Kazakhstan: A nationwide study. Heliyon 2021; 7:e06561. [PMID: 33763618 PMCID: PMC7972671 DOI: 10.1016/j.heliyon.2021.e06561] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/14/2021] [Accepted: 03/16/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Diabetes mellitus (DM) is associated with higher risk of developing infectious disease and COVID-19 is not the exception. There is a need to generate more data on clinical characteristics and risks of COVID19 patients presenting with DM. In this retrospective study we aimed to report on demographic features, clinical data, and outcomes of COVID-19 patients with DM in comparison with age- and sex-matched patients without DM. METHODS This was a retrospective study that relied on the nationwide data on all COVID-19 patients who were diagnosed from 14 March to 18 April, 2020. Overall, there were 31 cases with DM for which we randomly matched 4 patients without DM by age and sex. RESULTS COVID-19 patients with associated DM had less beneficial outcomes and more severe disease course both at hospital admission and final diagnosis, as compared with the age and sex-matched non-DM patients. Diabetics were more predisposed to impaired breathing (29.0 % versus 4.9 % in controls), nausea/vomiting (6.5 % versus 0 % in controls) and weakness/lethargy (45.2 % versus 26.0 % in controls). Finally, 48.4 % of diabetics showed the signs of pneumonia on CT scans versus 20.3 % of non-diabetics (p = 0.001), and 32.3 % of DM patients were admitted to intensive care units as compared with just 5.7 % of non-DM patients (p<0.001). CONCLUSION There is a need to envisage early status monitoring and supportive care in this vulnerable category of patients to enable better prognosis.
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Affiliation(s)
- Azhar Dyusupova
- Department of Personalized Medicine, Semey Medical University, Semey, Kazakhstan
| | - Raida Faizova
- Department of Personalized Medicine, Semey Medical University, Semey, Kazakhstan
| | - Oksana Yurkovskaya
- Department of Personalized Medicine, Semey Medical University, Semey, Kazakhstan
| | - Tatiana Belyaeva
- Department of Personalized Medicine, Semey Medical University, Semey, Kazakhstan
| | - Tatiana Terekhova
- Department of Personalized Medicine, Semey Medical University, Semey, Kazakhstan
| | - Amina Khismetova
- Department of Personalized Medicine, Semey Medical University, Semey, Kazakhstan
| | | | - Dmitry Bokov
- Institute of Pharmacy, Sechenov First Moscow State Medical University, Moscow, Russia
- Laboratory of Food Chemistry, Federal Research Center of Nutrition, Biotechnology and Food Safety, Moscow, Russia
| | - Alexandr Ivankov
- Department of Postgraduate Education, Kazakh Medical University of Continuing Education, Almaty, Kazakhstan
| | - Natalya Glushkova
- Department of Epidemiology, Evidence-Based Medicine and Biostatistics, Kazakhstan Medical University Higher School of Public Health, Almaty, Kazakhstan
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Kim K, Choi JW, Moon J, Akilov H, Tuychiev L, Rakhimov B, Min KS. Clinical Features of COVID-19 in Uzbekistan. J Korean Med Sci 2020; 35:e404. [PMID: 33230989 PMCID: PMC7683242 DOI: 10.3346/jkms.2020.35.e404] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/09/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND As of April 30, 2020, a total of 2,039 cases of the novel coronavirus disease 2019 (COVID-19) were confirmed in the Republic of Uzbekistan after the first detection on March 15. Reports on symptoms of COVID-19 are non-specific and known to vary from asymptomatic, mild to severe, or fatal. This study aimed to analyze the symptomatic and clinical characteristics of study participants based on the medical records of participants hospitalized with COVID-19 in Uzbekistan. METHODS We collected all data from medical records of COVID-19 confirmed patients in 19 hospitals from 13 regions of Uzbekistan between March 15 and April 30. We selected 1,030 patients discharged from the hospitals after COVID-19 treatment as study participants, excluding those with missing data. Further, we collected demographics, symptoms, clinical outcomes, and treatment data through medical records. RESULTS More than half (57.6%) of confirmed cases of COVID-19 were males, and the median age was 36.0 years. The most frequent symptoms at the first inspection on hospital admission of all patients were fatigue (59.7%), dry cough (54.1%), pharyngalgia (31.6%), headache (20.6%), and anorexia (12.5%). Compared to the oldest group, the youngest group showed a lower frequency of symptoms. About half of the group aged 18-49 years reported that they came from abroad. One-fifth of patients in group 50-84 received oxygen support, while no patients in group aged 0-17 years received oxygen support. About two-thirds of the participants from intensive care unit (ICU) came from abroad, whereas 42.1% of the non-ICU group returned from other countries. Regarding symptoms, 16.9% of the patients in the ICU group were asymptomatic, while 5.8% in the non-ICU group were asymptomatic. CONCLUSION This study suggests that the medical delivery system and resource distribution need to be implemented based on clinical characteristics by age and severity to delay and effectively respond to the spread of infections in the future. This study analyzed symptoms of COVID-19 patients across Uzbekistan, which is useful as primary data for policies on COVID-19 in Uzbekistan.
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Affiliation(s)
- KyungHee Kim
- Institute for Environmental Health, Korea University, Seoul, Korea
| | - Jae Wook Choi
- Institute for Environmental Health, Korea University, Seoul, Korea
- Graduate School of Public Health, Korea University, Seoul, Korea
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea.
| | - Juyoung Moon
- Institute for Environmental Health, Korea University, Seoul, Korea
- Graduate School of Public Health, Korea University, Seoul, Korea
| | - Habibulla Akilov
- The Tashkent Institute of Postgraduate Medical Education, Tashkent, Uzbekistan
| | | | | | - Kwang Sung Min
- Department of International Development Cooperation, Graduate School of Pan-Pacific International Studies, Kyung Hee University, Seoul, Korea
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19
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Semenova Y, Pivina L, Khismetova Z, Auyezova A, Nurbakyt A, Kauysheva A, Ospanova D, Kuziyeva G, Kushkarova A, Ivankov A, Glushkova N. Anticipating the Need for Healthcare Resources Following the Escalation of the COVID-19 Outbreak in the Republic of Kazakhstan. J Prev Med Public Health 2020; 53:387-396. [PMID: 33296578 PMCID: PMC7733753 DOI: 10.3961/jpmph.20.395] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 09/10/2020] [Indexed: 12/13/2022] Open
Abstract
Objectives The lack of advance planning in a public health emergency can lead to wasted resources and inadvertent loss of lives. This study is aimed at forecasting the needs for healthcare resources following the expansion of the coronavirus disease 2019 (COVID-19) outbreak in the Republic of Kazakhstan, focusing on hospital beds, equipment, and the professional workforce in light of the developing epidemiological situation and the data on resources currently available. Methods We constructed a forecast model of the epidemiological scenario via the classic susceptible-exposed-infected-removed (SEIR) approach. The World Health Organization’s COVID-19 Essential Supplies Forecasting Tool was used to evaluate the healthcare resources needed for the next 12 weeks. Results Over the forecast period, there will be 104 713.7 hospital admissions due to severe disease and 34 904.5 hospital admissions due to critical disease. This will require 47 247.7 beds for severe disease and 1929.9 beds for critical disease at the peak of the COVID-19 outbreak. There will also be high needs for all categories of healthcare workers and for both diagnostic and treatment equipment. Thus, Republic of Kazakhstan faces the need for a rapid increase in available healthcare resources and/or for finding ways to redistribute resources effectively. Conclusions Republic of Kazakhstan will be able to reduce the rates of infections and deaths among its population by developing and following a consistent strategy targeting COVID-19 in a number of inter-related directions.
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Affiliation(s)
- Yuliya Semenova
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
| | - Lyudmila Pivina
- Department of Internal Medicine, Semey Medical University, Semey, Kazakhstan
| | - Zaituna Khismetova
- Department of Public Health, Semey Medical University, Semey, Kazakhstan
| | - Ardak Auyezova
- Head Office, Kazakhstan Medical University Higher School of Public Health, Almaty, Kazakhstan
| | - Ardak Nurbakyt
- Department of Epidemiology, Evidence Medicine and Biostatistics, Kazakhstan Medical University Higher School of Public Health, Almaty, Kazakhstan
| | - Almagul Kauysheva
- Department of Research and International Affairs Kazakhstan Medical University Higher School of Public Health, Almaty, Kazakhstan
| | - Dinara Ospanova
- Department of Public Health, Kazakh Medical University of Continuing Education, Almaty, Kazakhstan
| | - Gulmira Kuziyeva
- Department of Epidemiology, Biostatistics and Evidence-Based Medicine, Al-Farabi Kazakh National University, Almaty, Kazakhstan
| | | | - Alexandr Ivankov
- Department of Postgraduate Education, Kazakh Medical University of Continuing Education, Almaty, Kazakhstan
| | - Natalya Glushkova
- Department of Epidemiology, Evidence Medicine and Biostatistics, Kazakhstan Medical University Higher School of Public Health, Almaty, Kazakhstan
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Nussbaumer-Streit B, Mayr V, Dobrescu AI, Chapman A, Persad E, Klerings I, Wagner G, Siebert U, Ledinger D, Zachariah C, Gartlehner G. Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review. Cochrane Database Syst Rev 2020; 9:CD013574. [PMID: 33959956 PMCID: PMC8133397 DOI: 10.1002/14651858.cd013574.pub2] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is a rapidly emerging disease classified as a pandemic by the World Health Organization (WHO). To support the WHO with their recommendations on quarantine, we conducted a rapid review on the effectiveness of quarantine during severe coronavirus outbreaks. OBJECTIVES To assess the effects of quarantine (alone or in combination with other measures) of individuals who had contact with confirmed or suspected cases of COVID-19, who travelled from countries with a declared outbreak, or who live in regions with high disease transmission. SEARCH METHODS An information specialist searched the Cochrane COVID-19 Study Register, and updated the search in PubMed, Ovid MEDLINE, WHO Global Index Medicus, Embase, and CINAHL on 23 June 2020. SELECTION CRITERIA Cohort studies, case-control studies, time series, interrupted time series, case series, and mathematical modelling studies that assessed the effect of any type of quarantine to control COVID-19. We also included studies on SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) as indirect evidence for the current coronavirus outbreak. DATA COLLECTION AND ANALYSIS Two review authors independently screened abstracts and titles in duplicate. Two review authors then independently screened all potentially relevant full-text publications. One review author extracted data, assessed the risk of bias and assessed the certainty of evidence with GRADE and a second review author checked the assessment. We used three different tools to assess risk of bias, depending on the study design: ROBINS-I for non-randomised studies of interventions, a tool provided by Cochrane Childhood Cancer for non-randomised, non-controlled studies, and recommendations from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) for modelling studies. We rated the certainty of evidence for the four primary outcomes: incidence, onward transmission, mortality, and costs. MAIN RESULTS We included 51 studies; 4 observational studies and 28 modelling studies on COVID-19, one observational and one modelling study on MERS, three observational and 11 modelling studies on SARS, and three modelling studies on SARS and other infectious diseases. Because of the diverse methods of measurement and analysis across the outcomes of interest, we could not conduct a meta-analysis and undertook a narrative synthesis. We judged risk of bias to be moderate for 2/3 non-randomized studies of interventions (NRSIs) and serious for 1/3 NRSI. We rated risk of bias moderate for 4/5 non-controlled cohort studies, and serious for 1/5. We rated modelling studies as having no concerns for 13 studies, moderate concerns for 17 studies and major concerns for 13 studies. Quarantine for individuals who were in contact with a confirmed/suspected COVID-19 case in comparison to no quarantine Modelling studies consistently reported a benefit of the simulated quarantine measures, for example, quarantine of people exposed to confirmed or suspected cases may have averted 44% to 96% of incident cases and 31% to 76% of deaths compared to no measures based on different scenarios (incident cases: 6 modelling studies on COVID-19, 1 on SARS; mortality: 2 modelling studies on COVID-19, 1 on SARS, low-certainty evidence). Studies also indicated that there may be a reduction in the basic reproduction number ranging from 37% to 88% due to the implementation of quarantine (5 modelling studies on COVID-19, low-certainty evidence). Very low-certainty evidence suggests that the earlier quarantine measures are implemented, the greater the cost savings may be (2 modelling studies on SARS). Quarantine in combination with other measures to contain COVID-19 in comparison to other measures without quarantine or no measures When the models combined quarantine with other prevention and control measures, such as school closures, travel restrictions and social distancing, the models demonstrated that there may be a larger effect on the reduction of new cases, transmissions and deaths than measures without quarantine or no interventions (incident cases: 9 modelling studies on COVID-19; onward transmission: 5 modelling studies on COVID-19; mortality: 5 modelling studies on COVID-19, low-certainty evidence). Studies on SARS and MERS were consistent with findings from the studies on COVID-19. Quarantine for individuals travelling from a country with a declared COVID-19 outbreak compared to no quarantine Very low-certainty evidence indicated that the effect of quarantine of travellers from a country with a declared outbreak on reducing incidence and deaths may be small for SARS, but might be larger for COVID-19 (2 observational studies on COVID-19 and 2 observational studies on SARS). AUTHORS' CONCLUSIONS The current evidence is limited because most studies on COVID-19 are mathematical modelling studies that make different assumptions on important model parameters. Findings consistently indicate that quarantine is important in reducing incidence and mortality during the COVID-19 pandemic, although there is uncertainty over the magnitude of the effect. Early implementation of quarantine and combining quarantine with other public health measures is important to ensure effectiveness. In order to maintain the best possible balance of measures, decision makers must constantly monitor the outbreak and the impact of the measures implemented. This review was originally commissioned by the WHO and supported by Danube-University-Krems. The update was self-initiated by the review authors.
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Affiliation(s)
- Barbara Nussbaumer-Streit
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
| | - Verena Mayr
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
| | - Andreea Iulia Dobrescu
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
| | - Andrea Chapman
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
| | - Emma Persad
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
| | - Irma Klerings
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
| | - Gernot Wagner
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
| | - Uwe Siebert
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
- Division of Health Technology Assessment and Bioinformatics, Oncotyrol - Center for Personalized Cancer Medicine, Innsbruck, Austria
- Center for Health Decision Science, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, USA
- Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Casey Zachariah
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
| | - Gerald Gartlehner
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria
- RTI International, Research Triangle Park, North Carolina, USA
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