Big data and simple models used to track the spread of COVID-19 in cities
Understanding the dynamics of SARS-CoV-2 infections could help to limit viral spread. Analysing mobile-phone data to track human contacts at different city venues offers a way to model infection risks and explain infection disparities.
Behind the highly politicized disagreements over COVID-19 control measures lies a widely shared desire to return economic and social life to sustainable levels as soon and for as long as possible, while preserving health-care systems and minimizing severe illness and death. The main arguments are about the extent to which these goals are mutually reinforcing, and whether there is a trade-off between greater viral transmission and increased social and economic activity. The difficulty in identifying control measures that are both effective and minimally disruptive motivates the search for new approaches to modelling transmission. Given the limited data available from epidemiological studies on how interventions can curb infection, such models can provide an initial framework for evaluating hypothetical control measures and help to guide policy decisions.
This data-rich model has enabled them to generate and, to some extent, test hypotheses on where the virus is transmitted, how racial and socio-economic disparities in COVID-19 infections arise, and how effective different types of control measure might be.
Challenges of Modeling COVID-19
Part of the success of models used to predict the spread of the virus is based on the quantity of data. The more data ingested, the more accurate the model, and there is a lot of data that needs to be fed into the model. Take, for example, the quantity of data needed to trace all the contacts of people confirmed infected or suspected of being infected by the virus.
The challenge is that the amount of data required can stress out standard data processing systems, resulting in models that take too long to run or sacrifice quality by reading in less data due to limitations of computing power.
There is technology that can enable models to ingest higher volumes of data and run more quickly. A data analytics acceleration platform can speed up model run time and scale to read in more data while providing a single unified view to enable more data scientists to access the data. This platform eliminates the need for pre-aggregation or pre-modeling of data, which streamlines data preparation. To reduce model run time, GPU-powered servers can deliver faster performance at a fraction of the cost of competing CPU-only solutions. For example, query time can be reduced from days to hours and hours to minutes with the ability to add even more data without increasing the query time.
Data analytics is critical for tracking the spread of COVID-19. For both epidemiological and machine learning models, organizations need to integrate data quickly and reliably from both internal and external sources. Smarter architectures designed to scale and run models more quickly can lead to better and faster insights to enable healthcare organizations to be better prepared to treat patients and reduce the spread of COVID-19.
Conclusion and discussion
Effective Models have contributed vital insights into the COVID-19 pandemic, which can help evaluate and predict the effect of the implementation of different guidelines and protocols through changing parameters. We have measured the transmission capacity of potential cases and intervention of follow-up isolation, which shown the lockdown strategy effective.
The theoretical analysis of the model shows that public awareness of prevention and control, medical follow-up isolation and adequate medicine care are critical to the spread of the epidemic. Centralized treatment played a key role in the rapid decline of the peak number of infected people. We have opened a window to evaluate the effect of public health intervention and expect to search the faster way to prevent the virus.
Bibliography
https://www.nature.com/articles/d41586-020-02964-4
https://tdwi.org/articles/2020/10/28/ba-all-challenges-of-modeling-covid-19.aspx