Forecasting the spread of COVID-19 using LSTM network
| Autor: | Alok Sharma, Thirumananseri Kumarevel, Ronesh Sharma, Tatsuhiko Tsunoda, Shiu Kumar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19) Computer science QH301-705.5 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Computer applications to medicine. Medical informatics R858-859.7 01 natural sciences Biochemistry 03 medical and health sciences Structural Biology 0103 physical sciences Humans Economic impact analysis Biology (General) 010301 acoustics Molecular Biology Biological sciences Pandemics 030304 developmental biology End date prediction 0303 health sciences Government Pandemic SARS-CoV-2 Applied Mathematics Research COVID-19 Take over Computer Science Applications Futures studies Risk analysis (engineering) Communicable Disease Control Long short-term memory (LSTM) Forecasting New Zealand |
| Zdroj: | BMC Bioinformatics BMC Bioinformatics, Vol 22, Iss S6, Pp 1-9 (2021) |
| ISSN: | 1471-2105 |
| Popis: | Background The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed. Results The results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand’s data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19. Conclusion The forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19. |
| Databáze: | OpenAIRE |
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