Spatio-temporal predictive modeling framework for infectious disease spread
Abstract A novel predictive modeling framework for the spread of infectious diseases using high-dimensional partial differential equations is developed and implemented. A scalar function representing the infected population is defined on a high-dimensional space and its evolution over all the direct...
Enregistré dans:
Auteurs principaux: | Sashikumaar Ganesan, Deepak Subramani |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/61735942d474410586d4d07cb189dab4 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England
par: B. Sartorius, et autres
Publié: (2021) -
Temporal Characteristics of the Chinese Aviation Network and their Effects on the Spread of Infectious Diseases
par: Jianhong Mou, et autres
Publié: (2017) -
Author Correction: Modelling and predicting the spatio‑temporal spread of COVID‑19, associated deaths and impact of key risk factors in England
par: B. Sartorius, et autres
Publié: (2021) -
A novel framework for spatio-temporal prediction of environmental data using deep learning
par: Federico Amato, et autres
Publié: (2020) -
A dynamic spatio-temporal model to investigate the effect of cattle movements on the spread of bluetongue BTV-8 in Belgium.
par: Chellafe Ensoy, et autres
Publié: (2013)