Role of Machine Learning in Predicting Disease Outbreaks

In this article we try to understand how machine learning can be beneficial for predicting disease outbreaks

By 2050, the 7.8 billion people who currently live on the planet are projected to number 9.7 billion. Unfortunately, the rate of infectious diseases is rising as a result of population rise. The development of diseases is influenced by several variables. These include urbanization, globalization, and climate change, and most of these phenomena are to some degree a result of human activity. Pathogens themselves may be prone to emerging, and the emerging pathogens tend to have more quickly evolving viruses. When a virus from one person infects a different person or an animal, an infectious illness ensues.

It can impact society on a large scale, similar to the coronavirus COVID-19, and is consequently a significant societal issue. It is viewed as a societal issue since it not only negatively affects people but also negatively affects society as a whole. Therefore, identifying high-risk locations for fatal infectious and non-infectious disease outbreaks is crucial in order to undertake prediction and detection of deadly disease outbreaks and improve the effectiveness of the response to these deadly disease outbreaks. To stop the spread of catastrophic infectious disease outbreaks (like COVID-19), health officials can use a variety of machine learning (ML) technologies.

This may be achieved by utilizing machine learning algorithms for both forecasting and identifying lethal infectious diseases as well as for responding to them. In order to predict disease outbreaks, machine learning algorithms can be used to learn datasets that include details about known viruses, animal populations, human demographics, biology and biodiversity data, readily accessible physical infrastructures, cultural/social practices around the world, and the geographic locations of the diseases. For instance, Support Vector Machine (SVM) and Artificial Neural Network (ANN) models can be used to predict malaria outbreaks.

Average monthly precipitation, temperature, humidity, the total number of positive cases, the total number of Plasmodium Falciparum (pF) cases, and the binary values of the number of outbreaks each month Yes or No, as the models' performance is evaluated using their predictors, Root Mean Square Error (RMSE), and Receiver Operating Characteristic (ROC). The machine learning techniques may be integrated into an intelligent system to assess or mine social media data for any indicators of any outliers associated to uncommon flu symptoms in order to build efficient diagnostic methods.

As an illustration, Chae et al. suggested using deep learning to forecast infectious illnesses. In their study, deep learning algorithm parameters are improved while simultaneously using social media data to improve detection performance. The metrics include things like the frequency of verified infectious disease diagnoses, the volume of daily Google searches, the volume of Twitter mentions of the illness, and the average temperature and humidity in South Korea as a whole. After a disease event is recognized, responding to infectious disease outbreaks requires making a swift, well-informed choice in order to minimize the harm caused by the effect of the disease outbreaks.

In order to forecast the location and pace of spread of a disease, machine learning techniques may also learn integrated multi-source data from travel itinerary, population, logistics, and epidemiological data. Machine learning techniques can be applied by medical professionals to enhance the delivery of current therapies and hasten the development of novel ones. For instance, to learn any medical data gathered by the hospitals, they may utilize deep learning algorithms to model massive data sets. For instance, data from coronavirus patient clinical testing may be used as input for machine learning models to help clinicians identify the disease more quickly.