As the COVID-19 pandemic has demonstrated, our current societal dynamics such as the ease of international travel and extensive daily social interaction can enable the rapid spread of infectious diseases (IDs). Artificial intelligence (AI) and other technologies are continuously being developed to aid the government and public health officials in controlling the spread of potential ID outbreaks. These recent technical advances have enabled the prediction of ID sources, early detection of outbreaks, innovative contact tracing approaches, and improved symptom detection methods.

Predicting IDs

Early detection of emerging diseases via AI allows for prompt implementation of public safety policies to prevent potential outbreaks. For instance, given that it is estimated that 3 out of every 4 emerging IDs are zoonotic, a study used an AI machine learning system to analyze common variables associated with zoonotic diseases. They predicted the probability of certain rodents to be a source of novel infectious agents with 90% accuracy and pinpointed vulnerable geographical locations. Another example of early ID detection is through the AI platform BlueDot, a Canadian software developed to flag potential global ID threats. Potential infectious threats are identified through machine learning analyses of data acquired from public health reports, air travel information, farm and livestock health updates and population demographics. Using their technology, BlueDot forecasted the Zika virus outbreak in Florida 6 months earlier. Moreover, BlueDot flagged multiple pneumonia cases reported around a marketplace in Wuhan more than a week before the World Health Organization (WHO) announced the novel coronavirus outbreak in China, which were later identified as COVID-19. 

Tracking the Spread of IDs

Contact tracing is vital in controlling IDs. By identifying the line of transmission from an infected person to another individual, it is possible to break the chain of infection and prevent further spread. During the COVID-19 pandemic, the Canadian government promoted an app called ‘COVID Alert’ for contact tracing. A temporary code is provided by the medical professional communicating the diagnosis to the COVID-19 positive patient. The patient can then use the code to anonymously report their diagnosis on the app – preventing false reports. Bluetooth technology enables other citizens with the app to be alerted of potential exposure to an infected individual. Alerted individuals can then immediately take health and safety precautions. This is particularly useful for COVID-19 patients who are asymptomatic or who have yet to manifest symptoms post-exposure. However, a significant limitation of this method is that it requires most of the population to have the app downloaded. Additionally, individuals who are COVID-19 positive must be diligent to record their diagnosis in the app for it to be effective.

Symptom Detection

Early identification of ID-specific symptoms is crucialto limit the contact of infected individuals with the general population. Individuals might not know how to properly identify their symptoms based on information online. Thus, innovative methods that can recognize common symptoms of certain IDs can be valuable. For example,technology developed by Foresight Autonomous Holdings Ltd. was adapted into a touch-free symptom detection system during the pandemic. Although other symptom detection methods exist, they often solely detect high body temperature. The AI system by Foresight Autonomous Holdings Ltd. utilizes visible-light and thermal cameras to screen for multiple common symptoms of COVID-19, including cough, fatigue and fever. The development of such devices promotes mass symptom detection that can be implemented in populated settings such as hospitals, malls, airports, and schools. The ability to screen individuals in public places allows for more control in identifying infected individuals and potentially mitigate community spread of IDs.


As technology continues to be developed to combat IDs, it is important to identify the most effective ways to implement these mechanisms into society to help as many individuals as possible. COVID-19 has proved to be a catalyst for methods used for predictive modelling, contact tracing and symptom detection. Implementation of AI and other innovations during the COVID-19 pandemic will provide government officials with data to make informed decisions regarding regulatory policies to ensure the health and safety of their citizens upon another pandemic occurring.


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Christina Ditlof

Christina is a Master of Science student in the Department of Immunology at the University of Toronto, with a research focus in childhood food allergy.
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