The present population of 7.8 billion people on Earth is expected to increase to 9.7 billion by 2050. Regrettably, as the population grows, so does the prevalence of infectious diseases. A number of factors influence how diseases evolve. These include the phenomena of urbanization, globalization, and climate change, the majority of which are partially caused by human activity. It is possible for diseases to emerge on their own, and these pathogens typically have viruses that evolve faster. An infectious disease develops when a virus from one person infects a different person or an animal.
Similar to the coronavirus COVID-19, it has the potential to have a huge societal impact and is thus an important issue. Since it has an adverse effect on society as a whole in addition to individuals, it is seen as a societal concern. Therefore, in order to forecast and detect deadly disease outbreaks and enhance the efficacy of the response to these outbreaks, it is imperative to identify high-risk locations for both infectious and non-infectious disease outbreaks that result in fatalities. Health officials can employ a range of machine learning (ML) technology to halt the spread of catastrophic infectious disease epidemics, such as COVID-19.
This could be accomplished by employing machine learning algorithms for anticipating, detecting, and reacting to deadly viral diseases. Machine learning algorithms can be trained on datasets containing information about known viruses, animal populations, human demographics, biology and biodiversity data, easily accessible physical infrastructures, global cultural and social practices, and the geographic locations of the diseases in order to predict disease outbreaks. For example, malaria outbreaks can be predicted using Artificial Neural Network (ANN) and Support Vector Machine (SVM) models.
The monthly averages for temperature, humidity, precipitation, positive and Plasmodium Falciparum (pF) cases, as well as the binary values indicating the number of outbreaks per month Using the predictors Root Mean Square Error (RMSE) and Receiver Operating Characteristic (ROC), the models’ performance is assessed, determining if the answer is yes or no. In order to develop effective diagnosis approaches, machine learning techniques may be included into an intelligent system to evaluate or mine social media data for any indications of any outliers linked to unusual flu symptoms.
Chae et al. proposed, for example, employing deep learning to predict infectious diseases. In their work, they simultaneously use social media data to increase detection performance and optimize the parameters of the deep learning system. Among the metrics are the number of confirmed diagnoses of infectious diseases, the amount of daily Google searches, the number of Twitter mentions of the disease, and the average humidity and temperature of South Korea. In order to reduce the harm caused by the consequence of infectious disease outbreaks, prompt and informed decision-making is necessary in the aftermath of illness events.
Machine learning approaches may also learn integrated multi-source data including travel route, demographic, logistics, and epidemiological data to predict the location and rate of spread of a disease. Medical practitioners can use machine learning techniques to accelerate the development of new therapies and improve the delivery of existing ones. For example, hospitals may use deep learning algorithms to model large data sets in order to learn anything about the medical data they collect. To assist clinicians in diagnosing the condition more rapidly, machine learning models may be trained using data from clinical tests conducted on coronavirus patients.
