COVID-19 pandemic, predictions and control in Saudi Arabia using SIR-F and age-structured SEIR model

Durai, C. Anand Deva; Begum, A. Sumaiya; Jebaseeli, T. Jemima; Sabahath, Asfia

doi:10.1007/s11227-021-04149-w

Cited by 6 publications

(6 citation statements)

References 8 publications

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“… 3 , 4 In Saudi Arabia, the first COVID-19 case was confirmed on 2nd March 2020 (a female case in the Qatif region in the eastern area of Saudi Arabia had a history of returning from Iraq) to be followed by the announcement of the Ministry of Health for disaster declaration on 4th March in line with the WHO guidelines to promote significant concerns about the healthcare awareness, public mass gatherings, and preventive measurements such as “Umrah suspension, tourism, suspension to several countries, suspension of domestic/international flights, partial curfew for 21 days, Madinah and Makkah lockdown”. 5 After a while, these instructions followed by the closure of educational institutes, the non-essential business and “stay at home” call. 6 , 7 Despite these restrictions, Saudi Arabia had the highest number of COVID-19 cases compared with the other Gulf countries.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and Saudi Arabia: Awareness, Attitude, and Practice

Fawzy¹,

AlSadrah²

2022

JMDH

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The Severe Acute Respiratory Syndrome-CoronaVirus-2 (SARS-CoV-2) pandemic has transformed our appreciation of healthcare awareness worldwide. The amount of related data accumulated during this time has surpassed any other outbreak of a viral pathogen. Healthcare awareness is related to one of the nine pillars of the "World health organization (WHO) operational planning guidelines to support country preparedness and response". This review is structured around the awareness/knowledge, attitudes, and practices (KAP)-related publications in Saudi Arabia up to date to understand the impact of COVID-19 on these domains. The excellent communication effort response from governments, international, and individuals to keep the public informed about the outbreak is highlighted. Unraveling such impacts on the Saudi communities, including healthcare workers (HCWs)/professionals (HCPs), students, patients with different diseases, and non-professional individuals, is essential in containing the outbreak and planning preventive measures in case of future outbreaks.

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Section: Introductionmentioning

confidence: 99%

COVID-19 and Saudi Arabia: Awareness, Attitude, and Practice

Fawzy¹,

AlSadrah²

2022

JMDH

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“…Prior to the participants’ voluntary and forced repatriation, they were ensued with fear and anxiety upon realizing the severe impact of COVID-19 to the rest of the world. Following the first confirmed COVID-19 case in Saudi Arabia on 2 March 2020, the Ministry of Health has imposed the suspension of all tourism activities, domestic/ international flights, partial curfew for 21 days, Madinah and Makkah lockdown (Durai et al, 2021). Research participants developed fear and anxiety when this first case erupted followed by strict lockdowns and protocols.…”

Section: Findings and Discussionmentioning

confidence: 99%

Embodied Resilience: A Phenomenological Inquiry into the Reintegration Struggles and Triumphs of Transnational Mothers Amidst the COVID-19 Pandemic

Lanada

2024

The Family Journal

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The COVID-19 pandemic unleashed a global crisis with far-reaching impacts on various aspects of society, including labor migration and family dynamics. Among the vulnerable populations affected were transnational mothers who, having migrated in pursuit of economic opportunities, found themselves unexpectedly laid-off and compelled to return to their home countries. This phenomenological research endeavored to unravel the lived experiences of these laid-off transnational mothers as they navigate the complex journey of emergency return and reintegration during the pandemic. Drawing on a qualitative phenomenological approach, this study engaged in-depth interviews to explore the dimensions of these mothers’ experiences. The research journey illuminated the challenges, coping mechanisms, and transformations that emerged as these mothers embarked on a journey of returning to rebuild their lives and identities. Findings revealed that the sudden job loss and subsequent return disrupted established life trajectories, leading to a profound sense of dislocation and ambiguity. The mothers’ narratives captured the struggles of renegotiating roles within families and communities, grappling with economic instability, and confronting the intersections of gender, migration, and resilience. By bringing these individual stories to the forefront, the research underscored the urgent need for tailored support mechanisms, inclusive policy responses, and heightened awareness of the unique challenges faced by transnational mothers in crisis situations.

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“…The following faults are possible in this paper's epidemic identification task: (1) The model recognizes work information labels in the case of Id, such as "B-case id, I-work information, and I-case Id." (2) The initial label "B-case Id" should appear at the beginning of entity information, and the model forecasts in the middle of entity information, such as "I-case id, B-case id, and I-case id." These incorrect labels will have an impact on the model's overall accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Utilizing data mining and natural language processing technology to anticipate epidemic development trends and carry out intelligent security early warning has piqued the interest of academics. It has progressively become one of the hotspots in natural language processing [1][2][3]. According to the current standardization of epidemic pre-vention and control, the epidemic spread is characterized by random small-scale bursts, which leads to a shortage of case information data in the early stage of transmission, making the study of epidemic information more complex and problematic.…”

Section: Introductionmentioning

confidence: 99%

TBR-NER: Research on COVID-19 Text Information Extraction Based on Joint Learning of Topic Recognition and Named Entity Recognition

Feng

Hang

et al. 2022

Journal of Sensors

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There is a centralization of the core content in the text information of the new crown epidemic notification. This paper proposes a joint learning text information extraction method: TBR-NER (topic-based recognition named entity recognition) based on topic recognition and named entity recognition to predict the labeled risk areas and epidemic trajectory information in text information. Transfer learning and data augmentation are used to solve the problem of data scarcity caused by the initial local outbreak of the epidemic, and mutual understanding is achieved by topic self-labeling without introducing additional labeled data. Taking the epidemic cases in Hebei and Jilin provinces as examples, the reliability and effectiveness of the method are verified by five types of topic recognition and 15 types of entity information extraction. The experimental results show that, compared with the four existing NER methods, this method can achieve optimality faster through the mutual learning of each task at the early stage of training. The optimal accuracy in the independent test set can be improved by more than 20%, and the minimum loss value is significantly reduced. This also proves that the joint learning algorithm (TBR-NER) mentioned in this paper performs better in such tasks. The TBR-NER model has specific sociality and applicability and can help in epidemic prediction, prevention, and control.

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COVID-19 pandemic, predictions and control in Saudi Arabia using SIR-F and age-structured SEIR model

Cited by 6 publications

References 8 publications

COVID-19 and Saudi Arabia: Awareness, Attitude, and Practice

COVID-19 and Saudi Arabia: Awareness, Attitude, and Practice

Embodied Resilience: A Phenomenological Inquiry into the Reintegration Struggles and Triumphs of Transnational Mothers Amidst the COVID-19 Pandemic

TBR-NER: Research on COVID-19 Text Information Extraction Based on Joint Learning of Topic Recognition and Named Entity Recognition

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