How AI can shed light on societal implications of the pandemic

From plague outbreaks to COVID-19: On the value of ship traffic data for epidemic modeling of diseases

Ships are an important means of transportation for both people and goods, which also makes them potential hotspots for diseases, especially those with a longer incubation period. The recent clusters of COVID-19 infections on cruise ships such as The Diamond Princess and the Grand Princess are cases in point, but are certainly not the only examples. Based on previous research we conducted with vessel tracking data, we know that this data is a valuable source of information. Therefore, we set out to explore whether insights from this type of big data could be included in epidemic modelling of diseases to inform a more efficient and timely operational response.

Although passenger ships have nowadays largely been replaced by airlines, they still represent a substantial amount of traffic in island nations, and they continue to play an important role in international trade and tourism. Did you know for example that in 2018, cruise ships transported some 28.2 million passengers worldwide?

Automatic Identification System (AIS) data forms a global database of maritime traffic. Most large commercial, international, and passenger ships must be equipped with an AIS transmitter, which reports dynamic details about the ship’s position – such as its location, speed, and course over ground – and static details, including a ship’s identifier, type, and flag. This information is routinely used to monitor port security and detect fishing behaviour. More recently, it has also been used to study search and rescue operations at sea.

Check out our project on Using Big Data to Study Rescue Patterns in the Mediterranean

The current standard for calculating disease import risk at the international level is epidemic modelling that uses flight network data on commercial airline routes and capacity. However, vessels can also be global carriers for infected people and disease vectors.

We sought to understand how AIS traffic data can inform epidemic modelling using two case studies. The first looked at the plague outbreak that hit Madagascar in 2017, affecting an estimated 2,348 individuals and resulting in 202 deaths. The second study analysed the current pandemic caused by the novel coronavirus known as COVID-19. Our methodology consisted of gathering information from ports and ships and constructing an origin-destination matrix.

We learned that we can use AIS data to visualize individual ship trajectories to understand, for example, whether ships came to shore in cities with high risk of infections, and to account for their arrival times in future ports in near real-time. The study also showed that there is a widespread reach of ships travelling between islands, in our case with vessels reaching three or more continents within a one month period.

This type of information could be useful in complementing insights from flight data, especially when modelling the spread of diseases in port cities and island nations. We hope our research can inspire future work on how combining vessel tracking data and flight network data could change the outcome of disease simulations and risk estimates.

Download our paper From plague to COVID-19: On the value of ship traffic data for epidemic modeling.

Mapping the landscape of artificial intelligence applications against COVID-19

 

 

The number of COVID-19 cases continues to grow at an alarming rate – with over 487,000 people being infected worldwide as of March 26, 2020 – and predictions paint a gloomy picture of the next weeks and potentially months to come. Members of our data science team joined forces with researchers from the World Health Organization (WHO) and the MILA- Quebec AI Institute to map the landscape of artificial intelligence (AI) applications that are being built to tackle the COVID-19 pandemic.

With the continued growth in the number of cases of the novel coronavirus, researchers worldwide are working around the clock to better understand, mitigate, and suppress its spread. Our paper compiles the mounting studies which have been published about the potential of AI applications to help manage global response. Specifically, we focused on three main areas: individual patient diagnosis and treatment, protein and drug discovery related research, and the socio-economic impact of the disease.

How AI can inform medical research against COVID-19

When it comes to medical imaging, an AI model may perform certain tasks, such as reading CT lung scans, faster and, given the right data to train on, even more accurately than a medical professional. With the current pandemic, quick diagnostics using machine learning (ML) approaches could save lives. In several promising studies, AI models were trained to identify potential COVID-19 cases; others are combining off-the-shelf software with bespoke machine learning approaches; others are using a human-in-the-loop approach to reduce the time required to label the disease. All of these efforts are in an incipient phase, however, the preliminary results are certainly encouraging.

In addition, there is ongoing clinical research to discover drugs to combat the disease. Scientists are working to identify existing drugs that may be repurposed to treat COVID-19. One such example is the case of the much debated, and heavily publicised, Chloroquine and Hydroxychloroquine – two drugs typically used to treat malaria that have shown some promising results. Alongside this, there are ongoing efforts to discover new drugs that can counter the disease.

As time is of the essence, AI systems, methods, and models can act as a compact form of knowledge sharing that can be used to train other specialists and can be deployed widely. In order to facilitate the sharing of such data, clinical protocols and data sharing mechanisms will need to be designed and data governance frameworks must be put in place.

How AI can shed light on societal implications of the pandemic

The strain that the COVID-19 pandemic has put on our society is being felt at every level: from closed businesses, to economic distress, to schools grappling with online classes, to people isolated at home. Advice at the national and local level is changing daily as new information and model forecasts become available. Given the rapid progression of infections, real-time short-term forecasting can be a vital source of information both for medical professionals, and public policy decision makers. In particular, models must be flexible in order to adapt to changing protocols and procedures. With a wide range of possible factors impacting on the dynamics of the disease, AI models could prove a vital resource for epidemiologists in approximating the underlying complex behaviour.

In addition, being able to quantify the spread of information surrounding the pandemic will help us curb the propagation of misinformation and inaccuracies, which are increasingly prevalent. Social media and online platforms have become key distribution channels for news surrounding the virus.

Although national and international organizations have used these platforms to constructively communicate with the public, we are also seeing an ‘infodemic’ that is overwhelming people with all sorts of details. In our mapping exercise, we highlight a number of efforts currently underway to curate specific news content related to the virus and perform both manual and automated fact-checking and relevance analysis.