COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning

Torres-Signes, Antoni; Frías, María Pilar; Ruiz-Medina, M. D.

doi:10.1007/s00477-021-02021-0

Cited by 7 publications

(3 citation statements)

References 68 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This framework has also been adopted in Torres-Signes et al. ( 2021 ), for COVID-19 mortality prediction by applying multivariate curve regression and machine learning. As an alternative, to analyze the spatial interaction between log-risk curves at different regions, in Frías et al.…”

Section: Introductionmentioning

confidence: 99%

Point pattern analysis and classification on compact two-point homogeneous spaces evolving time

Frías

Torres

Ruiz-Medina

2023

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

This paper introduces a new modeling framework for the statistical analysis of point patterns on a manifold defined by a connected and compact two-point homogeneous space, including the special case of the sphere. The presented approach is based on temporal Cox processes driven by a -valued log-intensity. Different aggregation schemes on the manifold of the spatiotemporal point-referenced data are implemented in terms of the time-varying discrete Jacobi polynomial transform of the log-risk process. The n -dimensional microscale point pattern evolution in time at different manifold spatial scales is then characterized from such a transform. The simulation study undertaken illustrates the construction of spherical point process models displaying aggregation at low Legendre polynomial transform frequencies (large scale), while regularity is observed at high frequencies (small scale). K -function analysis supports these results under temporal short, intermediate and long range dependence of the log-risk process.

show abstract

Section: Introductionmentioning

confidence: 99%

Point pattern analysis and classification on compact two-point homogeneous spaces evolving time

Frías

Torres

Ruiz-Medina

2023

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…Measures include of fast diagnostic testing of suspected cases, contact tracking and isolation of people, social distance, face mask use in public, and a community-wide lockdown; see for example Torres-Signes et al. ( 2021 ), Eikenberry et al. ( 2020 ) and the references therein.…”

Section: Introductionmentioning

confidence: 99%

An analytical approach to evaluate the impact of age demographics in a pandemic

Abdulrashid

Friji

Topuz

et al. 2023

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model’s efficiency is proved by testing the age-stratified model’s performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population.

show abstract

“…In [28], an Autoregressive Hilbertian process (ARH(1) process) framework was adopted to represent the dynamics of the spatiotemporal log-risk process. This framework has also been adopted in [31] for COVID-19 mortality prediction by applying multivariate curve regression and machine learning. As an alternative, to analyze the spatial interaction between log-risk curves at different regions, in [15], a Spatial Autoregressive Hilbertian process (SARH(1) process) based modeling was applied.…”

Section: Introductionmentioning

confidence: 99%

Point pattern analysis and classification on compact two–point homogeneous spaces evolving time

Frías

Torres-Signes

Ruiz-Medina

2022

Preprint

View full text Add to dashboard Cite

This paper introduces a new modeling framework for the statistical analysis of point patterns on a manifold M_d , defined by a connected and compact two–point homogeneous space, including the special case of the sphere. The presented approach is based on temporal Cox processes driven by a L2 (M_d)– valued log–intensity. Different aggregation schemes on the manifold of the spatiotemporal point–referenced data are implemented in terms of the time– varying discrete Jacobi polynomial transform of the log–risk process. The n–dimensional microscale point pattern evolution in time at different manifold spatial scales is then characterized from such a transform. The simulation study undertaken illustrates the construction of spherical point process models displaying aggregation at low Legendre polynomial transform frequencies (large scale), while regularity is observed at high frequencies (small scale). K–function analysis supports these results under temporal short–, intermediate– and long–range dependence of the log–risk process.

show abstract

COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning

Cited by 7 publications

References 68 publications

Point pattern analysis and classification on compact two-point homogeneous spaces evolving time

Point pattern analysis and classification on compact two-point homogeneous spaces evolving time

An analytical approach to evaluate the impact of age demographics in a pandemic

Point pattern analysis and classification on compact two–point homogeneous spaces evolving time

Contact Info

Product

Resources

About