tetano
Editor, Senior Moderator
Adv Respir Med
. 2020;88(5):400-405.
doi: 10.5603/ARM.a2020.0156.
Using a simple open-source automated machine learning algorithm to forecast COVID-19 spread: A modelling study
Shahir Asfahan[SUP] 1 [/SUP], Maya Gopalakrishnan[SUP] 1 [/SUP], Naveen Dutt[SUP] 1 [/SUP], Ram Niwas[SUP] 1 [/SUP], Gopal Chawla[SUP] 2 [/SUP], Mehul Agarwal[SUP] 1 [/SUP], Mahendera Kumar Garg[SUP] 1 [/SUP]
Affiliations
Abstract
Introduction: Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics.
Material and methods: Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea's centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric.
Results: As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period.
Conclusion: Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in na?ve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic.
Keywords: COVID-19; South Korea; coronavirus; machine learning; pandemic.
. 2020;88(5):400-405.
doi: 10.5603/ARM.a2020.0156.
Using a simple open-source automated machine learning algorithm to forecast COVID-19 spread: A modelling study
Shahir Asfahan[SUP] 1 [/SUP], Maya Gopalakrishnan[SUP] 1 [/SUP], Naveen Dutt[SUP] 1 [/SUP], Ram Niwas[SUP] 1 [/SUP], Gopal Chawla[SUP] 2 [/SUP], Mehul Agarwal[SUP] 1 [/SUP], Mahendera Kumar Garg[SUP] 1 [/SUP]
Affiliations
- PMID: 33169811
- DOI: 10.5603/ARM.a2020.0156
Abstract
Introduction: Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics.
Material and methods: Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea's centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric.
Results: As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period.
Conclusion: Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in na?ve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic.
Keywords: COVID-19; South Korea; coronavirus; machine learning; pandemic.