Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces.
Spat Spatiotemporal Epidemiol
; 48: 100634, 2024 Feb.
Article
in En
| MEDLINE
| ID: mdl-38355258
ABSTRACT
SARS-CoV-2, the virus responsible for COVID-19, posed a significant threat to the world. We analyzed COVID-19 dissemination data in the top ten Indian provinces by infection incidences using the Susceptible-Infectious-Removed (SIR) model, an Autoregressive Integrated Moving Average (ARIMA) time series model, a machine learning model based on the Random Forest, and distribution fitting. Outbreaks are expected to continue if the Basic Reproduction Number (R0) > 1, and infection waves are anticipated to end if the R0 < 1, as determined by the SIR model. Different parametric probability distributions are also fitted. Data collected from December 12, 2021, to March 31, 2022, encompassing data from both before and during the implementation of strict control measures. Based on the estimates of the model parameters, health agencies and government policymakers can develop strategies to combat the spread of the disease in the future, and the most effective technique can be recommended for real-world application for other outbreaks of COVID-19. The best method out of these could be also implemented further on the epidemiological data of other similar infectious agents.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
Asia
Language:
En
Journal:
Spat Spatiotemporal Epidemiol
/
Spat. spatiotemporal epidemiol. (Print)
/
Spatial and spatio-temporal epidemiology (Print)
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: