Source: expresscomputer.in
Mobilewalla works especially with devices such as mobile phones. They believe that this is one of the primary reasons why AI will work phenomenally well and would be creating a spread prediction analysis to trace people faster and arrest the spread.
However, does merely deploying technology like AI, Big Data and Machine Learning help in checking the disease, that has already been surging at an alarming rate?
Anindya Datta – CEO & Chairman, Mobilewalla gives us deeper insights.
How is Mobilewalla addressing the problem of the continuous surge in numbers?
At Mobilewalla 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.
Mobilewalla is working with various businesses and municipalities providing data around individual mobility that acts as a proxy for social distancing. Mobilewalla can provide both a social isolation score or the separate data attributes or features that can be used to build a custom score. This 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.
How are you different than the Aarogya Setu app out here?
Mobilewalla is using AI, particularly big data and machine learning techniques, 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 then 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 India is attempting to do with the Arogya Setu app) or by seeking data from other available sources.
The Arogya Setu app, is a worthy effort and could serve as a useful consumer tool to minimise risky behavior and receive current Covid19 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, depends 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 the less testing, the 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
- For the app to be useful, even given adequate testing and reliable risk models, enough people have to be using the app
- The app requires certain key disclosures from users, such as their infection status. In India, where social stigma still plays a key part in social interaction, one might question the likelihood of truthful disclosures at scale.
How are you ensuring data privacy? Has there been any breach?
Mobilewalla does not buy or collect data directly from consumers. We keep in mind the privacy laws prevalent when it comes to our data sources. Although companies have been utilizing third-party data for years, the proliferation of data generated by an increasingly connected world is causing concern.
Data and privacy regulations that came about in Europe two years ago regarding the privacy of a consumer didn’t change the fact that leveraging third-party data is essential for fueling predictive modeling, identifying new target audiences, and in emergency situations like COVID-19. Mobilewalla takes data privacy and consents compliance seriously. Compliance is in everything we do – from the data, we acquire to the products we produce and ultimately to the solutions we make available to our customers. We have a structured compliance framework across all of our processes to ensure data privacy and consent. As data moves through various stages in our processing, it remains in a de-identified and aggregated state at all times. We do not have data that is considered sensitive or can be tied directly to an individual.
How is technology helping you in your expedition?
The purpose of AI is to support decision making, at scale, by revealing patterns that emerge from large amounts of data. AI is particularly useful in scenarios where (a) data can be collected at scale allowing reliable patterns to emerge, and (b) where manual efforts to both collect and analyse data do not work well.
Data and AI technologies are being used in new ways to battle COVID-19, 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
- 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
- Risk assessment of individuals and locations, at scale, by understanding the movement of infected individuals
At Mobilewalla 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.
Can the use of AI and advanced technology help curb the speed of the virus? If so, how?
AI and data are being widely used to battle COVID-19, particularly in countries that adopt a scientific approach to public health. Countries need to be taking a holistic approach to fighting the spread of COVID-19. Data scientists are creating machine learning models to predict infection and mortality rates which drive resource need and allocation planning. Some countries are choosing to implement “tracking applications” which monitor individual movement as well as individual infection status and attempt to notify people when potential exposure to an infected individual or area occurs. Social isolation, increased testing to identify infected individuals and tracking the spread through contact with infected individuals are where countries are currently focusing their efforts. As this identifying and tracking need to be done at a mass scale, AI can be a powerful tool.
The key to the successful use of AI in any situation relies on the data attributes, known as “features”, that are fed into the models — If this data is inaccurate or lacks scale, the ability of the model to predict outcomes will be poor.
With a situation like the COVID-19 pandemic where the virus primarily spreads via direct, and even indirect, human contact the potential scale for the spread, as we are seeing, is significant.
Traditionally two techniques can be applied to curb the spread of a virus:
- Prevention/Cure: Medical solutions driven by vaccines (prevention), and therapeutics (cure).
- Mitigation: Arresting spread through human behavior, such as practicing hygiene and social isolation/distancing.
Due to the absence of prevention and cure methods, mitigation is the primary way the pandemic is currently being managed. Historically the approach to mitigating virus spread has been “contact tracing” – tracking the movements of infected people to identify who came in contact with them, based on interviews and other mostly manual methods. With the speed and scale of this pandemic, it is hard, if not, impossible to adequately map the spread in this way.