A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates
Peter Congdon
| Year of publication: |
2022
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|---|---|
| Authors: | Congdon, Peter |
| Published in: |
Journal of geographical systems : geographical information, analysis, theory, and decision. - Berlin : Springer, ISSN 1435-5949, ZDB-ID 1481603-9. - Vol. 24.2022, 4, p. 583-610
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| Subject: | Autoregressive | Bayesian | Clustering | COVID-19 | Epidemic | Forecasting | Spatio-temporal | Coronavirus | Prognoseverfahren | Forecasting model | Autokorrelation | Autocorrelation | Epidemie | Bayes-Statistik | Bayesian inference | Infektionskrankheit | Infectious disease | VAR-Modell | VAR model |
freely available
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1007/s10109-021-00366-2 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013462086