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Winalai C, Anupong S, Modchang C, Chadsuthi S. LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach. Heliyon 2024; 10:e30319. [PMID: 38711630 PMCID: PMC11070856 DOI: 10.1016/j.heliyon.2024.e30319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 05/08/2024] Open
Abstract
The COVID-19 pandemic has significantly impacted public health and necessitated urgent actions to mitigate its spread. Monitoring and predicting the outbreak's progression have become vital to devise effective strategies and allocate resources efficiently. This study presents a novel approach utilizing Multivariate Long Short-Term Memory (LSTM) to analyze and predict COVID-19 trends in Central Thailand, particularly emphasizing the multi-feature selection process. To consider a comprehensive view of the pandemic's dynamics, our research dataset encompasses epidemiological, meteorological, and particulate matter features, which were gathered from reliable sources. We propose a multi-feature selection technique to identify the most relevant and influential features that significantly impact the spread of COVID-19 in the region to enhance the model's performance. Our results highlight that relative humidity is the key factor driving COVID-19 transmission in Central Thailand. The proposed multi-feature selection technique significantly improves the model's accuracy, ensuring that only the most informative variables contribute to the predictions, avoiding the potential noise or redundancy from less relevant features. The proposed LSTM model demonstrates its capability to forecast COVID-19 cases, facilitating informed decision-making for public health authorities and policymakers.
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Affiliation(s)
- Chanidapa Winalai
- Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
| | - Suparinthon Anupong
- Department of Chemistry, Mahidol Wittayanusorn School (MWIT), Salaya, Nakhon Pathom 73170, Thailand
| | - Charin Modchang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
- Centre of Excellence in Mathematics, CHE, Bangkok 10400, Thailand
- Thailand Center of Excellence in Physics, CHE, 328 Si Ayutthaya Road, Bangkok 10400, Thailand
| | - Sudarat Chadsuthi
- Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
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Wang Y, Yuan F, Song Y, Rao H, Xiao L, Guo H, Zhang X, Li M, Wang J, Ren YZ, Tian J, Yang J. Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China. PLoS One 2024; 19:e0301420. [PMID: 38593140 PMCID: PMC11003692 DOI: 10.1371/journal.pone.0301420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/17/2024] [Indexed: 04/11/2024] Open
Abstract
The COVID-19 pandemic has been present globally for more than three years, and cross-border transmission has played an important role in its spread. Currently, most predictions of COVID-19 spread are limited to a country (or a region), and models for cross-border transmission risk assessment remain lacking. Information on imported COVID-19 cases reported from March 2020 to June 2022 was collected from the National Health Commission of China, and COVID-19 epidemic data of the countries of origin of the imported cases were collected on data websites such as WHO and Our World in Data. It is proposed to establish a prediction model suitable for the prevention and control of overseas importation of COVID-19. Firstly, the SIR model was used to fit the epidemic infection status of the countries where the cases were exported, and most of the r2 values of the fitted curves obtained were above 0.75, which indicated that the SIR model could well fit different countries and the infection status of the region. After fitting the epidemic infection status data of overseas exporting countries, on this basis, a SIR-multiple linear regression overseas import risk prediction combination model was established, which can predict the risk of overseas case importation, and the established overseas import risk model overall P <0.05, the adjusted R2 = 0.7, indicating that the SIR-multivariate linear regression overseas import risk prediction combination model can obtain better prediction results. Our model effectively estimates the risk of imported cases of COVID-19 from abroad.
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Affiliation(s)
- Ying Wang
- Science and Technology Research Center of China Customs, Beijing, China
- School of Epidemiology and Public Health, Shanxi Medical University, Taiyuan, China
- Department of Preventive Medicine, Changzhi Medical College, Changzhi, China
| | - Fang Yuan
- Science and Technology Research Center of China Customs, Beijing, China
| | - Yueqian Song
- Science and Technology Research Center of China Customs, Beijing, China
| | - Huaxiang Rao
- Department of Preventive Medicine, Changzhi Medical College, Changzhi, China
| | - Lili Xiao
- Science and Technology Research Center of China Customs, Beijing, China
| | - Huilin Guo
- Science and Technology Research Center of China Customs, Beijing, China
| | - Xiaolong Zhang
- Science and Technology Research Center of China Customs, Beijing, China
| | - Mufan Li
- School of Epidemiology and Public Health, Shanxi Medical University, Taiyuan, China
| | - Jiayu Wang
- School of Epidemiology and Public Health, Shanxi Medical University, Taiyuan, China
| | - Yi zhou Ren
- School of Epidemiology and Public Health, Shanxi Medical University, Taiyuan, China
| | - Jie Tian
- Science and Technology Research Center of China Customs, Beijing, China
| | - Jianzhou Yang
- Department of Preventive Medicine, Changzhi Medical College, Changzhi, China
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Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
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Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
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Wang S, Liu Y, Liu Y, Zhang Y, Zhu X. BERT-5mC: an interpretable model for predicting 5-methylcytosine sites of DNA based on BERT. PeerJ 2023; 11:e16600. [PMID: 38089911 PMCID: PMC10712318 DOI: 10.7717/peerj.16600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
DNA 5-methylcytosine (5mC) is widely present in multicellular eukaryotes, which plays important roles in various developmental and physiological processes and a wide range of human diseases. Thus, it is essential to accurately detect the 5mC sites. Although current sequencing technologies can map genome-wide 5mC sites, these experimental methods are both costly and time-consuming. To achieve a fast and accurate prediction of 5mC sites, we propose a new computational approach, BERT-5mC. First, we pre-trained a domain-specific BERT (bidirectional encoder representations from transformers) model by using human promoter sequences as language corpus. BERT is a deep two-way language representation model based on Transformer. Second, we fine-tuned the domain-specific BERT model based on the 5mC training dataset to build the model. The cross-validation results show that our model achieves an AUROC of 0.966 which is higher than other state-of-the-art methods such as iPromoter-5mC, 5mC_Pred, and BiLSTM-5mC. Furthermore, our model was evaluated on the independent test set, which shows that our model achieves an AUROC of 0.966 that is also higher than other state-of-the-art methods. Moreover, we analyzed the attention weights generated by BERT to identify a number of nucleotide distributions that are closely associated with 5mC modifications. To facilitate the use of our model, we built a webserver which can be freely accessed at: http://5mc-pred.zhulab.org.cn.
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Affiliation(s)
- Shuyu Wang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui, China
| | - Yinbo Liu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui, China
| | - Yufeng Liu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui, China
| | - Yong Zhang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui, China
| | - Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui, China
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Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106731. [PMID: 35286874 PMCID: PMC8897838 DOI: 10.1016/j.cmpb.2022.106731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/28/2022] [Accepted: 03/03/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.
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Affiliation(s)
- Haseeb Hassan
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
| | - Zhaoyu Ren
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Chengmin Zhou
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Yi Pan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Jian Zhao
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Bingding Huang
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
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Liu Q, Vaci N, Koychev I, Kormilitzin A, Li Z, Cipriani A, Nevado-Holgado A. Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model. BMC Med 2022; 20:45. [PMID: 35101059 PMCID: PMC8805393 DOI: 10.1186/s12916-022-02250-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/11/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify the best treatment using routinely collected patient-level information. METHODS Six thousand eight hundred four patients aged 59-102 years with a diagnosis of dementia from two National Health Service (NHS) Foundation Trusts in the UK were used for model training/internal validation and external validation, respectively. A personalised prescription model based on the Recurrent Neural Network machine learning architecture was developed to predict the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores post-drug initiation. The drug that resulted in the smallest decline in cognitive scores between prescription and the next visit was selected as the treatment of choice. Change of cognitive scores up to 2 years after treatment initiation was compared for model evaluation. RESULTS Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended. After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02). In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01). A similar performance gap was seen when testing the model on patients prescribed with AChEIs only. CONCLUSIONS It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level. Routine care patients whose prescribed medications were the best fit according to the model had better cognitive performance after 2 years.
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Affiliation(s)
- Qiang Liu
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK.
| | - Nemanja Vaci
- Department of Psychology, University of Sheffield, Sheffield, UK
| | - Ivan Koychev
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Andrey Kormilitzin
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Institute of Mathematics, University of Oxford, Oxford, UK
| | - Zhenpeng Li
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Alejo Nevado-Holgado
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Akrivia Health, Oxford, UK
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Shafiekhani S, Namdar P, Rafiei S. A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit. Digit Health 2022; 8:20552076221085057. [PMID: 35355809 PMCID: PMC8961204 DOI: 10.1177/20552076221085057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 01/13/2022] [Accepted: 02/16/2022] [Indexed: 12/23/2022] Open
Abstract
Background Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy. Methods We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead. Results All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R2 as model assessment metrics showed that ANFIS model had better predictive power among all models. Conclusion Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.
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Affiliation(s)
- Sajad Shafiekhani
- Department of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics, Tehran, Iran
- Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Peyman Namdar
- Social Determinants of Health Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Sima Rafiei
- Department of Healthcare Management, School of Health, Qazvin University of Medical Sciences, Qazvin, Iran
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Hossain MS, Pathan AQMSU, Islam MN, Tonmoy MIQ, Rakib MI, Munim MA, Saha O, Fariha A, Reza HA, Roy M, Bahadur NM, Rahaman MM. Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning. INFORMATICS IN MEDICINE UNLOCKED 2021; 27:100798. [PMID: 34812411 PMCID: PMC8598266 DOI: 10.1016/j.imu.2021.100798] [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: 09/19/2021] [Revised: 11/06/2021] [Accepted: 11/15/2021] [Indexed: 01/31/2023] Open
Abstract
Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G\A, C > G\A, and A > T\C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.
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Affiliation(s)
- Md Shahadat Hossain
- Department of Biotechnology & Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - A Q M Sala Uddin Pathan
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Md Nur Islam
- Department of Biotechnology & Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | | | - Mahmudul Islam Rakib
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Md Adnan Munim
- Department of Biotechnology & Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Otun Saha
- Department of Microbiology, University of Dhaka, Dhaka, Bangladesh
| | - Atqiya Fariha
- Department of Biotechnology & Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Hasan Al Reza
- Department of Genetic Engineering and Biotechnology, University of Dhaka, Dhaka, Bangladesh
| | - Maitreyee Roy
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Bangladesh
| | - Newaz Mohammed Bahadur
- Department of Applied Chemistry and Chemical Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
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Abstract
With the implementation of strict quarantine and adjunct health measures, Fiji was largely spared from the COVID-19 pandemic in 2020. In April 2021, the arrival of the delta variant of SARS-CoV-2 resulted in a major outbreak that is only now under control. The country enacted swift countermeasures to reduce population movement, but the delta variant gained a foothold in the peri-urban areas of the largest city, Suva, and from there spread to other major urban centres of the largest island, Viti Levu. A very effective vaccination campaign has since been implemented resulting in a rate of two-dose vaccination, above 70%, that exceeds many higher income countries and, as of late October 2021, the numbers of COVID-19 cases were in decline. Fijian life, especially in rural settings, is based on hierarchical but supportive community structures and the responsibilities that flow from community membership, for example, attending large funerals, mitigated against the organised and systematic control response instituted by the Fijian Ministry of Health and Medical Services, including vaccination, and the other Government agencies. A positive consequence of the outbreak has been the development of new capabilities, and the distribution of molecular diagnostic technologies that can be exploited to help control other infectious diseases.
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