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Spatial prediction of COVID-19 epidemic using ARIMA techniques in India

Roy, Santanu ; Bhunia, Gouri Sankar ; Shit, Pravat Kumar

Modeling earth systems and environment, 2021-06, Vol.7 (2), p.1385-1391 [Periódico revisado por pares]

Cham: Springer International Publishing

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Título:
Spatial prediction of COVID-19 epidemic using ARIMA techniques in India
Autor: Roy, Santanu ; Bhunia, Gouri Sankar ; Shit, Pravat Kumar
Assuntos: Analysis ; Autoregressive models ; Chemistry and Earth Sciences ; Computer Science ; Coronaviruses ; COVID-19 ; Digital media ; Earth and Environmental Science ; Earth Sciences ; Earth System Sciences ; Ecosystems ; Environment ; Epidemics ; Epidemiology ; Forecasting ; Geographical information systems ; Health risks ; Infectious diseases ; Math. Appl. in Environmental Science ; Mathematical Applications in the Physical Sciences ; Model accuracy ; Physics ; Prediction models ; Public health ; Risk analysis ; Root-mean-square errors ; Short Communication ; Social media ; Spatial distribution ; Statistics for Engineering ; Viral diseases
É parte de: Modeling earth systems and environment, 2021-06, Vol.7 (2), p.1385-1391
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Descrição: The latest Coronavirus (COVID-19) has become an infectious disease that causes millions of people to infect. Effective short-term prediction models are designed to estimate the number of possible events. The data obtained from 30th January to 26 April, 2020 and from 27th April 2020 to 11th May 2020 as modelling and forecasting samples, respectively. Spatial distribution of disease risk analysis is carried out using weighted overlay analysis in GIS platform. The epidemiologic pattern in the prevalence and incidence of COVID-2019 is forecasted with the Autoregressive Integrated Moving Average (ARIMA). We assessed cumulative confirmation cases COVID-19 in Indian states with a high daily incidence in the task of time-series forecasting. Such efficiency metrics such as an index of increasing results, mean absolute error (MAE), and a root mean square error (RMSE) are the out-of-samples for the prediction precision of model. Results shows west and south of Indian district are highly vulnerable for COVID-2019. The accuracy of ARIMA models in forecasting future epidemic of COVID-2019 proved the effectiveness in epidemiological surveillance. For more in-depth studies, our analysis may serve as a guide for understanding risk attitudes and social media interactions across countries.
Editor: Cham: Springer International Publishing
Idioma: Inglês

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Spatial prediction of COVID-19 epidemic using ARIMA techniques in India

Roy, Santanu ; Bhunia, Gouri Sankar ; Shit, Pravat Kumar

Cham: Springer International Publishing

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