PANDEMIC ANALYTICS TO ASSESS RISK OF COVID-19 OUTBREAK

Details

Hits: 1650

Volume 3 (1), June 2020, Pages 32-45

Tapalina Bhattasali

St. Xavier’s College (Autonomous), Kolkata, India, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

COVID-19 pandemic has spread all over the world within a short period. It has changed every aspect of our daily lives significantly. The number of infected cases and the number of deaths are increasing day by day in many countries. Consequently, the situation becomes out of control. Due to recent advances in computational technologies, this paper focuses on the analytics part to assess various risks associated with the COVID-19 outbreak, which can be used to combat the severe effects of pandemics. Pandemic analytics is used to understand the spread pattern of pandemics by using the concept of artificial intelligence, machine learning, blockchain, and big data analytics. It is also required to evaluate policy for disease control. Based on the nature of the pandemic, a theoretical mathematical model is designed to predict the risks associated with the population all over the world. The analysis part is capable of forecasting the status and of answering various questions that arise from various parts of the world, such as the dependency of COVID-19 infection with sex, age, location, temperature, etc. Pandemic analytics is also used to visualize the official data before making any significant decisions.

Keywords:

Pandemic; Analytics; Outbreak; Coronavirus; COVID-19

DOI: https://doi.org/10.32010/26166127.2020.3.1.32.45

Reference 

Bannister-Tyrrell, M., Meyer, A., Faverjon, C., & Cameron, A. (2020). Preliminary evidence that higher temperatures are associated with lower incidence of COVID-19, for cases reported globally up to 29^th February 2020. medRxiv.

Giordano, G., Blanchini, F., Bruno, R., et al. (2020). A SIDARTHE model of COVID-19 epidemic in Italy. arXiv preprint arXiv:2003.09861.

Hellewell, J., Abbott, S., Gimma, A., Bosse, N. I., et al. (2020). Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health.

Liu, Z., Magal, P., Seydi, O., & Webb, G. (2020). Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data. arXiv preprint arXiv:2002.12298.

Park, S. W., Bolker, B. M., Champredon, D., et al. (2020). Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak. MedRxiv.

Roser, M., Ritchie, H., Ortiz-Ospina, E., & Hasell, J. (2020). Coronavirus disease (COVID-19)–Statistics and research. Our World in data.

Russo, L., Anastassopoulou, C., Tsakris, A., et al. (2020). Tracing DAY-ZERO and Forecasting the Fade out of the COVID-19 Outbreak in Lombardy, Italy: A Compartmental Modelling and Numerical Optimization Approach. medRxiv.

Volpert, V., Banerjee, M., & Petrovskii, S. (2020). On a quarantine model of coronavirus infection and data analysis. Mathematical Modelling of Natural Phenomena, 15, 24.

Weber, A., Ianelli, F., & Goncalves, S. (2020). Trend analysis of the COVID-19 pandemic in China and the rest of the world. arXiv preprint arXiv:2003.09032.

World Health Organization. (2019). Retrieved from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019

Zhang, F., Zhang, J., Cao, M., & Hui, C. (2020). A simple ecological model captures the transmission pattern of the coronavirus COVID-19 outbreak in China. medRxiv.