Forecasting the spread of COVID-19 using LSTM network

• Published in: BMC bioinformatics Vol. 22; no. Suppl 6; pp. 316 - 9
• Main Authors: Kumar, Shiu, Sharma, Ronesh, Tsunoda, Tatsuhiko, Kumarevel, Thirumananseri, Sharma, Alok
• Format: Journal Article
• Language: English
• Published: London BioMed Central 10.06.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Air pollution; Algorithms; Bioinformatics; Biomedical and Life Sciences; Communicable Disease Control; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Control; Coronaviruses; COVID-19; COVID-19 vaccines; Decision making; Disease prevention; Disease transmission; Economic impact; End date prediction; Epidemics; Forecasting; Forecasts and trends; Government industry relations; Humans; Impact analysis; Infections; Life Sciences; Long short-term memory; Long short-term memory (LSTM); Machine learning; Mathematical models; Microarrays; Neural networks; New Zealand; Outdoor air quality; Pandemic; Pandemics; Prediction models; SARS-CoV-2; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Shutdowns; Viral diseases; New Zealand; Saudi Arabia; Canada; Italy; India; COVID-19; Pandemic; Long short-term memory (LSTM); End date prediction
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04224-2

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.