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Top1. Introduction
The first case of pneumonia of unidentified cause was confirmed to the WHO (World Health Organization), China on December 2019. COVID-19's first case was associated with direct access to Wuhan's Huanan Seafood Wholesale Market, and from there the animal-to-human transmission was believed to be the main source of disease spread. Subsequent cases, however, were not found to be linked to the same exposure, and thus symptomatic human-to-human transmission was identified as the key cause of COVID-19 spread. At the same time, pre-symptomatic and asymptomatic transmission for the spread of the disease is equally likely (Cascella et al., 2020) (Khadidos et al., 2020). Since then, the disease spread at an unprecedented rate barring the geographical boundaries and thus the disease that emanated from China spread across the whole world. Resultantly, the epidemic of this virus became a matter of severe concern and within 1 month.
Considering the spread of this disease, the outbreak was declared an international public health emergency on 30 January 2020. Also, WHO coined a name for new coronavirus disease: COVID-19 and declared it a pandemic on 11th March 2020. This spread of novel coronavirus (SARS-CoV-2) infection (COVID-19) raised a wave of shock and concern around the world as there had been 3,181,642 cases and 2,24,301 deaths affecting 215 countries and territories (https://www.who.int/emergencies/diseases/novel-coronavirus-2019, n.d.) by on 1st May 2020. These numbers are further expected to rise significantly in future as there is no successful vaccine or antiviral strategy available against the disease. The whole world experienced a standstill and the world economy witnessed its worst phase. As a result, the control of this disease became a matter of international primacy. In some parts of the world, stringent policies like social distancing and lockdown were imposed by governing authorities in order to control the spread as these were considered as the only viable strategy to stop the viral transmission. The spread of this virus also attracted various researchers across the globe to understand the nature of virus and its intricacies. In this context, the study by various researchers established various factors such as epidemiological and personal health parameters.
Several demographic factors, including gender, age, and blood type, have also been identified as risk factors for the spread of infection. However, the research till date is not adequate to recognize the complex associations among several variables. Further, owing to elevated transmissibility of this disease of zoonotic origin, identifying the drivers and spatial diffusion is a great challenge. This further intricate the designing of an efficient model to understand. Fortunately, with the emergence of fuzzy logic and genetic algorithms applications, computers can now find correlations between variables and find out the most promising ones.
Efficient correlation among the various factors and timely information about the origin and epidemiological characteristics can prove to be a great help to contain this virus. In this direction, efficient integration of tools like epidemiology, fuzzy approach and bioinformatics can prove to be a great help to handle the challenging task. In this manuscript, authors aim to employ fuzzy approach to recognize the impact of various epidemiological and clinical factors towards spread of the disease. The authors chose fuzzy approach owing to lack of historical dataset. Further, the crisp values for considered parameters is also missing. This lack of data prevents efficient deployment of any established data processing tool. In such scenario, fuzzy model stands a promising chance to comprehend the data and may give acceptable performance. The motive behind selection of epidemiological and clinical factors is their demonstrated impact on spread of disease in various studies (Hellewell et al., 2020).
The manuscript works on the broad classification of factors involved in the spread of COVID’19 as risk and preventive factors. A fuzzy model based on Mamdani approach is presented to model the inter-relationship and dependency of the factors. Three fuzzy models are presented; First based on the mapping of various factors on the risk evaluation; second based on the preventive factors mapping. The final model is the mapping of the risk and preventive factors to evaluate the susceptibility index.