Modeling the spread of dangerous pandemics with the utilization of a hybrid-statistical–Advanced-Fuzzy-Cognitive-Map algorithm: the example of COVID-19

PURPOSE: The novel Coronavirus SARS-coV-2 outbreak late in 2019 and early 2020, known today as the COVID-19 pandemic, has spread fast throughout the world. It has considerably affected the lives of all people around the globe while the number of deaths related to the pandemic keeps increasing worldwide. Being able to predict the spread of the pandemic has been very helpful to governments to decide on actions. Statistical prediction models are capable of modeling a single snapshot but have several well-known weaknesses, such as linear assumptions between pandemic variables, while they cannot confirm the actual causality between studied factors. In the present work, the authors propose a state space Advanced Fuzzy Cognitive Maps (AFCM) approach model to predict the spread of the pandemic, using dynamic cause and effect relationships between pre-defined factors. METHODS: State-Space Advanced Fuzzy Cognitive Maps are proposed for modeling the spread of the pandemic, utilizing several social, policy, and healthcare factors. Statistical data from Greece, South Korea, and Germany are gathered to evaluate the performance of the proposed model. RESULTS: The proposed methodology was able to predict the pandemic trend in the studied countries, in terms of the total number of confirmed patient cases, yielding a coefficient of determination of 0.99, 0.94, and 0.97 respectively. The Pearson’s correlation coefficient was found to be 0.99, 0.97, and 0.98 respectively. CONCLUSION: The results demonstrate the effectiveness and the advantages of the proposed methodology when modeling uncertain and dynamic situations, like novel pandemics.