An assessment of meteorological parameters effects on COVID-19 pandemic in Bangladesh using machine learning models

Karmokar, Jaionto; Islam, Mohammad Aminul; Uddin, Md. Romij; Hassan, Md. Rakib; Yousuf, Md. Sayeed Iftekhar

doi:10.1007/s11356-022-20196-z

Cited by 11 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus far, few studies have simultaneously considered the meteorological variables, demographic factors, and policy response measures to investigate their joint effects in the development of the COVID-19 epidemic. In addition, most global studies were based on the national data source (Chung et al 2021 ; Fu et al 2021 ; Han et al 2022 ), while city level studies are limited in a single country or region (Grekousis et al 2022 ; Hariharan 2021 ; Karmokar et al 2022 ; Zhang et al 2022 ). The aim of this study is to conduct comprehensive analysis on the basis of geographically downscaled data source, utilize machine learning methods to assess the collective effects of the aforementioned elements on COVID-19 cases, and further to explore how these roles vary across different climate zones.…”

Section: Introductionmentioning

confidence: 99%

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones

Lyu

Wang

Jiang

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-023-27320-7.

show abstract

Section: Introductionmentioning

confidence: 99%

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones

Lyu

Wang

Jiang

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…Table 15 compares previous works involving Lung and other types of cancer classification from microarray gene datasets with the reference from Fathi et al [ 43 ] and Karmokar et al [ 48 ], which contains performance metrics, namely Accuracy, Precision, Recall and F1 Score. Fathi et al [ 43 ] utilized the PCC–DTCV (Pearson correlation–Decision Tree Cross Validation) model for lung cancer classification and reported accuracy in ranges from 93 to 95.…”

Section: Resultsmentioning

confidence: 99%

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene—A Paradigm Shift

S¹,

Rajaguru²,

Nair³

2023

Bioengineering

View full text Add to dashboard Cite

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

show abstract

“…Epidemiological studies have reached conflicting conclusions about the effects of RH and temperature on the transmission of SARS-CoV-2 in human populations [ 23 – 25 ]. Notably, RH has been found to correlate both positively [ 26 , 27 ] and negatively [ 28 – 31 ] with the spread of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Differences in airborne stability of SARS-CoV-2 variants of concern is impacted by alkalinity of surrogates of respiratory aerosol

Haddrell

Otero-Fernandez

Oswin

et al. 2023

J. R. Soc. Interface.

View full text Add to dashboard Cite

The mechanistic factors hypothesized to be key drivers for the loss of infectivity of viruses in the aerosol phase often remain speculative. Using a next-generation bioaerosol technology, we report measurements of the aero-stability of several SARS-CoV-2 variants of concern in aerosol droplets of well-defined size and composition at high (90%) and low (40%) relative humidity (RH) upwards of 40 min. When compared with the ancestral virus, the infectivity of the Delta variant displayed different decay profiles. At low RH, a loss of viral infectivity of approximately 55% was observed over the initial 5 s for both variants. Regardless of RH and variant, greater than 95% of the viral infectivity was lost after 40 min of being aerosolized. Aero-stability of the variants correlate with their sensitivities to alkaline pH. Removal of all acidic vapours dramatically increased the rate of infectivity decay, with 90% loss after 2 min, while the addition of nitric acid vapour improved aero-stability. Similar aero-stability in droplets of artificial saliva and growth medium was observed. A model to predict loss of viral infectivity is proposed: at high RH, the high pH of exhaled aerosol drives viral infectivity loss; at low RH, high salt content limits the loss of viral infectivity.

show abstract

An assessment of meteorological parameters effects on COVID-19 pandemic in Bangladesh using machine learning models

Cited by 11 publications

References 28 publications

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene—A Paradigm Shift

Differences in airborne stability of SARS-CoV-2 variants of concern is impacted by alkalinity of surrogates of respiratory aerosol

Contact Info

Product

Resources

About