The COVID-19 outbreak has highlighted the importance of working on public health and technology together in order to fight the crisis. Countries across the world are opting for different measures where several technologies are at play to tap the positive COVID-19 cases to stop the further spread of the virus.
China was the first country to report COVID-19 cases and is now witnessing the return of normalcy, but it also had to resort to technology to contain the spread. China used technologies such as smart imaging, drones and mobile apps to trace virus-carrying individuals.
The US and Europe, however, took a slightly different approach, using data derived via artificial intelligence to stop the spread of the virus. One such data provider is US-based Mobilewall, which serves countries with data to serve public health.
In an interview with Sputnik, Anind Datta, the CEO and chairman of Mobilewall, a consumer intelligence platform that is working with US task forces and other municipalities to fight the coronavirus, reflects on the importance of the use of artificial intelligence technologies to deal with the present-day crisis, especially in highly densely populated regions like South Asia.
Question: Where has Mobilewall successfully carried out data distribution?
Anind Datta: Mobilewall data is being used by health services organizations and governmental entities around the world to better predict the spread of the Novel Coronavirus at both the macro (city/county/state/country) and micro (predicting patients at a hospital) level. Mobilewall is working with various businesses and municipalities, providing data around individual mobility that acts as a proxy for social distancing. We can provide both a social isolation score and separate data attributes, features that can be used to build a custom score. Such data includes individual mobility metrics (indicating the daily distance traveled and unique locations), cluster identification (gatherings of a high number of devices) and individual device data at both the micro and macro levels. These are all foundational inputs that can be used in COVID-19 prediction models.
Question: In a country where a huge population resides in rural areas, how can AI be implemented?
Anind Datta: The purpose of AI is to support decision making by revealing patterns that emerge from large amounts of data. AI is particularly useful in scenarios where (a) data can be collected at a scale allowing reliable patterns to emerge, and (b) where manual efforts to both collect and analyse data do not work well.
In remote rural areas, manual data collection is challenging, and even if possible, such data is reliability-challenged due to the social barriers against honest disclosures of questions perceived as personal. In the current COVID-19 crisis, where data collection involves gathering information about personal habits and symptoms related to infection, these impediments only increase. Yet, a lot of this information can be gathered from behaviour exhibited on mobile phones, which have spread well into India's rural areas. Mobile data, accumulated at a scale, can allow for inferences to be made to help critical decision-making both in urban and rural areas.
Sputnik: Please, describe the ways in which AI and data can be used to battle COVID-19.
Anind Datta: In the context of COVID-19, data and AI technologies are being used in new ways, particularly in countries that adopt a scientific approach to public health. Data scientists are creating machine learning models to predict infection and mortality rates and to determine resource needs and allocation based on these predictions.
AI can be used to power two key tasks of pandemic mitigation: infection tracking and infection spread prediction. If done correctly, AI can help uncover three foundational pieces of information, crucial to tracking and predicting the spread: measuring social isolation by observing individual mobility, identifying clusters of more than a certain number of individuals and identifying the corresponding locations; and risk assessment of individuals and locations, at scale, by understanding the movement of infected individuals.
Question: Do you have some suggestion for the government regarding use of AI in slums and high density population?
Anind Datta: AI is particularly suited for analysing large amounts of data collected via machines. In slums and other high density areas, in context of the COVID-19 crisis, it is difficult to both maintain and track social distancing. For this reason, these regions can be triggers of infection waves that could provide deadly for the entire country. AI offers a mechanism to both collect and track behavioural signals from this area, which can then inform early-warning and alert systems that can drive tactical pandemic management activities.
AI, particularly, big data and machine learning techniques can be used to identify the infection risk of individuals, which can then be projected to those individuals and others in the geographic locations they have visited. Data scientists are creating models to track the spread of the virus and to determine resource needs and allocation based on the prediction of hard-hit areas. AI is an enabler; it identifies patterns and provides insights at speeds well beyond what humans can do manually.
But, the key to the successful use of AI relies on the data that is being fed into the models. If this data is inaccurate or lacks scale the ability of the model to predict outcomes will be impacted in a negative way. Data can be obtained in various ways, either by requesting information directly from individuals (such as what populous countries is attempting to do with the Arogya Setu app or by seeking data from other available sources.
Question: Government's have been advocating app's which is also a mobile platform to fight against COVID-19. How useful is app in terms of contact tracing?
Anindya Datta: Arogya Setu app is a worthy effort and could serve as a useful consumer tool to minimise risky behaviour and receive current COVID-19 information. However, it is important to understand that the app by itself is simply a front end to information delivery. The effectiveness of the app is only as good as the information it has access to, but the app itself is not producing that information.
The quality of the risk information and therefore, the usefulness of the app, depend on a number of variables outside of the control of the app, including the magnitude of infection detection, which depends on testing. It is easy to see that less the testing, lower the value of the information disseminated via the app. What also matters is the risk models that are being used to build risk scores for geographies and sub-geographies. If the risk models are ineffective, even with adequate testing, the information delivered will be of little value.
In South Asia, where social stigma still plays a key part in social interaction, one might question the likelihood of truthful disclosures at scale.
Another, perhaps more reliable option, is to use other available data sources that can model the activities of the population at scale. In many cases location data and behavioural data can be used as inputs to COVID-19 predictive models.
Question: Certain groups have been opposing the medics. Can AI help medics find ways to track them without going to the location?
Anind Datta: Yes, location data of these groups can help doctors to track them. Location-based data can be used to track individual mobility without in-person engagement. Depending on the source of the data, it is also possible to use this data to communicate risk of infection in an anonymous manner using digital identification or communication through mobile devices.