The Epidemiological and Spatiotemporal Characteristics of the 2019 Novel Coronavirus Disease (COVID-19) in Libya

Daw, Mohamed A.; El-Bouzedi, Abdallah; Ahmed, Mohamed O.

doi:10.3389/fpubh.2021.628211

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…However, it is crucial to note that this does not necessarily indicate that men are more susceptible to anxiety. Instead, it likely reflects the fact that a higher number of COVID-19 confirmed cases were reported among male patients in Africa 28–31…”

Section: Lessons Learntmentioning

confidence: 99%

“…Instead, it likely reflects the fact that a higher number of COVID-19 confirmed cases were reported among male patients in Africa. [28][29][30][31] The findings from this project should be interpreted with the following limitations. First, the calls documented in the alert system were self-reported, and individuals may have over-reported or under-reported the COVID-19 symptoms experienced and/or travel history.…”

Section: Bmj Global Healthmentioning

confidence: 99%

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SARS-CoV-2 case detection using community event-based surveillance system—February–September 2020: lessons learned from Senegal

Seck¹,

Roka²,

N’Diaye³

et al. 2023

BMJ Glob Health

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The COVID-19 pandemic necessitated the rapid development and implementation of effective surveillance systems to detect and respond to the outbreak in Senegal. In this documentation, we describe the design and implementation of the Community Event-Based Surveillance (CEBS) system in Senegal to strengthen the existing Integrated Disease Surveillance and Response system. The CEBS system used a hotline and toll-free number to collect and triage COVID-19-related calls from the community. Data from the CEBS system were integrated with the national system for further investigation and laboratory testing. From February to September 2020, a total of 10 760 calls were received by the CEBS system, with 10 751 calls related to COVID-19. The majority of calls came from the Dakar region, which was the epicentre of the outbreak in Senegal. Of the COVID-19 calls, 50.2% were validated and referred to health districts for further investigation, and 25% of validated calls were laboratory-confirmed cases of SARS-CoV-2. The implementation of the CEBS system allowed for timely detection and response to potential COVID-19 cases, contributing to the overall surveillance efforts in the country. Lessons learned from this experience include the importance of decentralised CEBS, population sensitisation on hotlines and toll-free usage, and the potential role of Community Health Workers in triaging alerts that needs further analysis. This experience highlights the contribution of a CEBS system in Senegal and provides insights into the design and operation of such a system. The findings can inform other countries in strengthening their surveillance systems and response strategies.

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Section: Lessons Learntmentioning

confidence: 99%

Section: Bmj Global Healthmentioning

confidence: 99%

SARS-CoV-2 case detection using community event-based surveillance system—February–September 2020: lessons learned from Senegal

Seck¹,

Roka²,

N’Diaye³

et al. 2023

BMJ Glob Health

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“…The dynamics of infectious diseases is an important method to theoretically and quantitatively study the epidemic law of infectious diseases ( 7 , 8 ). It is based on the characteristics of population growth, the law of disease occurrence and transmission through the population and other relevant factors to establish a mathematical model that can reflect the dynamic characteristics of infectious diseases.…”

Section: Introductionmentioning

confidence: 99%

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

Wang

Zheng

Liu

2022

Front. Public Health

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The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.

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“…The North African countries experienced COVID-19 between February and March 2020 [3][4][5][6][7][8][9][10][11][12][13]. Between March 1 and 2, 2020, Morocco reported their first COVID-19 cases [6].…”

Section: Introductionmentioning

confidence: 99%

Prevalence and associated factors of COVID-19 among Moroccan physicians: A cross-sectional study

Baldi

Laghrissi²,

Marso³

et al. 2022

PLoS ONE

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Background Coronavirus disease (COVID-19) has emerged and spread rapidly worldwide and established a global public health crisis in early 2020. The first Moroccan case was reported on March 2, 2020. Since then, healthcare workers (HCWs) played a major role in saving human lives threatened by COVID-19. This study aimed to assess the prevalence of COVID-19 infection among Moroccan physicians and to report associated risk factors prior vaccination campaign. Methods A cross-sectional study was carried out in the Fez-Meknes region of Morocco, 545 physicians’ data was collected using a self-reported online questionnaire. The data collection was done between December 1, 2020, and February 1, 2021. Results The prevalence of COVID-19 among physicians was 27.3%. The mean age of the confirmed COVID-19 group was 38.4±12.9 years old. There was no association between COVID-19 infection and preventive measures compliance by physicians and healthcare authorities in the workplace. However, multivariate analysis strengthened the following factors such as increased risk of COVID-19 infection within men ☯aOR:1.896; 95% IC 1.272–2.828; p = 0.002]; the presence of at least one comorbidity ☯aOR:2.268; 95%IC 1.414–3.637; p = 0.001]; and working at a university or military hospitals ☯aOR:2.578; 95%IC 1.667–3.989; p = 0.001]. Conclusion This study allows comparing COVID-19 prevalence among healthcare workers before and after vaccination programs. This should support better preparation strategy for any future pandemics with appropriate and increased awareness for men, carrying comorbidity, and working environment with high COVID-19 disease management.

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The Epidemiological and Spatiotemporal Characteristics of the 2019 Novel Coronavirus Disease (COVID-19) in Libya

Cited by 11 publications

References 37 publications

SARS-CoV-2 case detection using community event-based surveillance system—February–September 2020: lessons learned from Senegal

SARS-CoV-2 case detection using community event-based surveillance system—February–September 2020: lessons learned from Senegal

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

Prevalence and associated factors of COVID-19 among Moroccan physicians: A cross-sectional study

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