The computer model achieved the result by recommending timely and country-specific advice on the optimal application and duration of COVID-19 interventions, such as home quarantines, social distancing measures, and personal protective measures that would help to thwart the negative impact of the pandemic.The team also showed NSGA-II could make predictions on the daily increases of COVID-19 confirmed cases and deaths that were highly accurate, at a confidence level of 95 per cent, compared to the actual cases that took place in the four countries over the past year.
Harnessing the power of machine learning, the research team developed NSGA-II by inputting large amounts of data on COVID-19 mortalities and infections worldwide that is available for the whole of 2020, helping it learn the dynamics of the pandemic. The research was reported in the peer-reviewed scientific journal Sustainable Cities and Society in August.
Assistant Professor Zhang Limao from NTU’s School of Civil and Environmental Engineering, who led the study, said: "The main goal of our study is to aid health authorities to make data-driven decisions in fighting the global COVID-19 pandemic. As we have observed in global efforts, there is no one-size-fits-all solution, and we hope our comprehensive programme would be able to help governments tailor the solutions at an early stage to best fit their country’s needs at different stages of the pandemic."
The NTU-developed computer programme could serve as a useful tool to help governments formulate strategies and interventions at an early stage to limit or even counter a predicted surge in cases, reducing infections and mortality rates.
Asst Prof Zhang added: "Alongside existing bioinformatics and medical methodologies for virus mechanism study, our programme shows that data science is an approach that can provide advantages in battling the pandemic. The practical value of our programme lies in two aspects. On one hand, it can well capture the transmission dynamics of the virus for an accurate prediction under consideration of environmental and social variables. On the other hand, it can systematically analyse and optimise the relevant factors on the targeted objectives for adaptive control."
The team plans to introduce more variables, such as economic status and cultural differences, into the model to further improve its accuracy. They are seeking to validate its efficacy by including data from additional countries in Europe and North America, providing insights into COVID-19 evolution across different geographies.
MEDICA-tradefair.com; Source: Nanyang Technological University
