Vector Autoregression in Forecasting COVID-19 Under-Reporting–Nepal as a Case Study

Devkota, Jyoti U.

doi:10.3126/jnms.v5i2.50016

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This functional behaviour will probably lead to the study of the common functional characteristics and further could be used to identify functional clusters of countries in the world. In addition, such as different health indices could have made a significant contribution to the functional behaviour of the spread of the disease and its prevention [3,4]. Therefore, the study of the country wise factors, the study mainly considers Functional data analysis (FDA) as the tool to model the counts of COVID-19 cases which are updated daily in the form of discrete observations with this, the functional models convert discrete time intervals into continuous functional forms.…”

Section: Introductionmentioning

confidence: 99%

Functional Data Analysis on Global COVID-19 Data

Silva

Abeysundara²

2023

AJPAS

View full text Add to dashboard Cite

No one can deny that COVID-19 has spread around the world and is still emerging as a new variation in some areas. Since the time of its breakout, three coronavirus waves have emerged. Daily cases of COVID-19 from 79 countries around the world were selected for the study. The main objective of this research was to model and analyze the behavior of the disease's first wave in the world and on the Asian continent using functional data analysis methods. Functional models were fitted to the data using B-spline basis functions at different orders, and the best-fit curves were further analyzed with respect to their functional behavior and the rate of change. These curves were visualized during the preliminary analysis and later clustered within each continent using functional cluster analysis. The results indicated that all continents, apart from Asia, had two clusters based on their functional behavior, whereas the world data had three clusters. All the continents except the Asian continent had different functional forms and numbers of peaks, but they all had the same number of clusters. The world's 18 countries were divided into two categories, with the remaining 61 countries clustered into a single group. The identified cluster indices were further modeled using multinomial logistic regression models with six popular health index variables. In the world, people over the age of 70 made a significant contribution to selecting cluster 2 over cluster 1 and cluster 3 over cluster 2. On the Asian continent, the female smoker variable preferred cluster 2 over cluster 1, while cluster 3 over cluster 1 could be determined by the median age variable. The findings of the overall study would be helpful for the researchers to understand the spread of the disease and the impact of the health indices on its functional behavior.

show abstract

Section: Introductionmentioning

confidence: 99%