Analysis of COVID-19 Trends in Bangladesh: A Machine Learning Analysis

Samrin, Nishat Ahmed; Suzan, Md. Mahmudul Hasan; Hossain, Md. Selim; Mollah, Mohammad Sarwar Hossain; Haque, Md. Dulal

doi:10.1007/978-3-030-90618-4_31

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…As shown in [1] , [46] , [74] , the use of multivariate features can help improve the accuracy of the forecasting models. Finally, despite the success of various deep learning models it is important to remember that the traditional autoregressive and mathematical models may still provide a useful alternative [78] .…”

Section: Discussionmentioning

confidence: 99%

Deep learning for Covid-19 forecasting: State-of-the-art review.

Kamalov¹,

Rajab²,

Cherukuri³

et al. 2022

Neurocomputing

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Section: Discussionmentioning

confidence: 99%

Deep learning for Covid-19 forecasting: State-of-the-art review.

Kamalov¹,

Rajab²,

Cherukuri³

et al. 2022

Neurocomputing

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“…4 As of June 2021, it has been noted that Sylhet has the lowest confirmed cases rate (9.75%) out of all eight divisions in Bangladesh, while Khulna division possesses the highest known cases rate (19.94%). 5 Several underlying medical conditions are associated with increasing the risk of COVID-19 severity and are associated with a higher mortality rate. 6 Hypertension (HTN) is a major disease that increases the risk of acute respiratory failure, hospital admission and mortality rate among patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Patients with Hypertension Admitted for COVID-19 Treatment in Khulna: Single-Centre Cross-Sectional Study

Muhammad Ashraful Kabir,

Biplab Kumar Roy,

Baishakhi Islam

et al. 2024

Mediscope

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Background: The outbreak of coronavirus disease 2019 (COVID-19) has become a large threat to global health due to its high contagious nature and varied mortality. Arterial hypertension represents one of the most common comorbidities in patients with COVID-19. Close connections between inflammation and blood pressure (BP) have been described, and inflammation plays a key role in the outcome for patients with COVID-19. Objective: This study aims to investigate the clinical characteristics of patients with hypertension admitted for COVID-19 treatment in Gazi Medical College Hospital (GMCH), Khulna, Bangladesh. Methods: This was a cross-sectional type of observational study on patients with hypertension who were diagnosed with COVID-19 based on laboratory and/or radiological findings and admitted to Gazi Medical College Hospital, Khulna, Bangladesh, from July 1 to July 31, 2021. In that timeframe, overall, 215 patients with COVID-19 were admitted. Among them, 37 patients having hypertension fulfilled the inclusion criteria for the study. Demographic, clinical, laboratory and radiological data of those 37 patients were recorded and analyzed. Results: Among 37 study subjects, 54.1% were female. The mean age of the patients was 59.6±13.9 years. Among the study subjects, diabetes (56.8%) was the most common comorbidity. Regarding laboratory parameters, the mean Hb of the patients was 11.4±1.9 g/dl. In addition, the mean random blood sugar (RBS) level was 11.6±2.9 mmol/l. Neutrophilia and lymphocytopenia were observed in 86.5% and 83.8% cases, respectively. The mean D-dimer (2.4 mg/l) and CRP (72.5 mg/l) levels were well above their normal limits. In high resolution CT scan of the chest (HRCT chest), bilateral lung involvement was present in 89.2% cases. Ground-glass appearance was the most frequent (91.9%) radiologic pattern. Among antihypertensive drugs, losartan potassium was the most frequently used category (29.7%). Total 7 (18.9%) patients were severely affected with COVID-19 and admitted to the ICU, requiring mechanical ventilation. During our study, 2 patients (5.4%) died either due to multiple organ dysfunction syndrome or cardio-respiratory failure. Conclusion: The study provides key information to better understand the clinical characteristics of patients with hypertension admitted for COVID-19 treatment, which may help physicians to identify the factors associated with adverse outcomes in this disease. Mediscope 2024;11(1): 34-40

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Analysis of COVID-19 Trends in Bangladesh: A Machine Learning Analysis

Cited by 2 publications

References 12 publications

Deep learning for Covid-19 forecasting: State-of-the-art review.

Deep learning for Covid-19 forecasting: State-of-the-art review.

Characteristics of Patients with Hypertension Admitted for COVID-19 Treatment in Khulna: Single-Centre Cross-Sectional Study

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