A Covid-19 Epidemiological Analysis and Forecasting Dashboard for Hospitals using Time-Series Analysis and Deep Learning

被引：0

作者：

Famadico, Nichol John F. ^{[1
]}

Solano, Geoffrey A. ^{[1
]}

Caoili, Janice C. ^{[2
,3
]}

机构：

[1] Univ Philippines, Coll Arts & Sci, Dept Phys Sci & Math, Manila, Philippines

[2] Makati Med Ctr, Infect Prevent & Control Dept, Makati, Philippines

[3] Trop Dis Fdn, Makati, Philippines

来源：

2024 IEEE 3RD INTERNATIONAL CONFERENCE ON COMPUTING AND MACHINE INTELLIGENCE, ICMI 2024 | 2024年

关键词：

Covid-19; dashboard; time series forecasting; ARIMA; RNN; LSTM;

D O I：

10.1109/ICMI60790.2024.10585832

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that, to date, has over 774 million confirmed cases and claimed over 7 million lives. Forecasts are therefore essential as they aide in the necessary preparation of healthcare logistics. In this study, forecasting models such as ARIMA, RNN and LSTM were developed to predict Covid-19 time-series data relevant to hospitals, which were then evaluated and compared using MAE and RMSE. Across the different variables, LSTM had the least error. A web-based dashboard was also developed for forecasting these time-series variables for healthcare institutions, particularly those which are accommodating Covid-19 patients.

引用

页数：5

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