Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach

Haq, Iqramul; Hossain, Md. Ismail; Saleheen, Ahmed Abdus Saleh; Nayan, Md. Iqbal Hossain; Mila, Mafruha Sultana

doi:10.1155/2022/8570089

Cited by 6 publications

(1 citation statement)

References 48 publications

(54 reference statements)

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“…This study will also help public health decision-makers to make better preparations if another epidemic like this ever hits Bangladesh [17].…”

Section: Introductionmentioning

confidence: 99%

Vaccine efficacy of COVID-19 in Bangladesh: Does vaccination prevent the pandemic?

Khan,

Begum,

Afeef

et al. 2023

GANIT: J. Bangladesh Math. Soc.

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We adopted a commonly used epidemiologic model known as Susceptible-Infectious-Recovered (SIR) by adding a compartment vaccination to locate the COVID-19 hotspot in Bangladesh. Using data from the Directorate General of Health Services (DGHS), we utilized the Susceptible-Vaccinated-Infectious-Recovered (SVIR) model to assess vaccine efficacy. Vaccination against COVID-19 has begun in industrialized nations and will be completed by early December 2020. Before 2023, mass vaccination for impoverished or low-income nations would be extremely difficult. Bangladesh, a lower-middle-income country, started a statewide COVID- 19 immunization effort in early February 2021. This study aimed to recognize the effectiveness of vaccination on the infected population in Bangladesh. Additionally, the study aimed to create public awareness of the importance of vaccination during a pandemic. GANIT J. Bangladesh Math. Soc. 43.1 (2023) 045- 062

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“…This study will also help public health decision-makers to make better preparations if another epidemic like this ever hits Bangladesh [17].…”

Section: Introductionmentioning

confidence: 99%

Vaccine efficacy of COVID-19 in Bangladesh: Does vaccination prevent the pandemic?

Khan,

Begum,

Afeef

et al. 2023

GANIT: J. Bangladesh Math. Soc.

View full text Add to dashboard Cite

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Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces

Yadav,

Khan,

Tiwari

et al. 2024

Spatial and Spatio-temporal Epidemiology

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Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review

Alam,

Sajib,

Rahman

et al. 2024

J Med Internet Res

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Background The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies. Objective This scoping review aims to collate (1) the existing research in Bangladesh’s health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research. Methods MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English. Results With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%). Conclusions This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh’s health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.

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Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach

Cited by 6 publications

References 48 publications

Vaccine efficacy of COVID-19 in Bangladesh: Does vaccination prevent the pandemic?

Vaccine efficacy of COVID-19 in Bangladesh: Does vaccination prevent the pandemic?

Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review

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