Since 2020, the outbreak of the coronavirus disease 2019 (COVID-19) pandemic has affected the entire world, and networks of human connections were identified as a factor that had potentially impacted the geographical spread of COVID-19. With the help of social media platforms, these networks have connected populations across the word and allowed people to view each other in close virtual proximity. Consequently, the Social Connectedness Index (SCI) is used to measure the strength of social connectivity across geographical regions through friendship ties on Facebook. The importance of social networks—and their relation to human connections—may correlate with the spread of COVID-19. Since these networks can have a potential effect on the spread of COVID-19, it is crucial to identify the factors that were associated with its spread during the pandemic. In order to analyze SCI data, a social network analysis was conducted to define the network parameters and perform calculations using graph theory. A correlation analysis was also performed to identify factors that correlated with the spread of COVID-19 cases using the data in the United States (US). Finally, the machine learning model was used to create a case prediction paradigm from the network parameters. The results showed that SCI can be used as a parameter to create a pandemic prediction model. Multiple linear regression also yielded satisfactory results that predicted the total number of positive cases measured by adjusted R². In terms of the time frame, this study suggested that the parameters from the previous week can be used to predict the number of weekly infections. The findings showed that social networks had a greater impact on the prediction of current active cases than total positive cases. The social networks between counties within a state also held more importance than those across states.

1. [1] World Health Organization (WHO), “Weekly Operational Update on COVID-19 – 22 February 2021,” 2021.
2. [2] Facebook, “‘Social Connectedness Index,’ Facebook Data for Good,” 2020. https://dataforgood.facebook.com/dfg/tools/social-connectedness-index [Accessed August 31, 2022]
3. [3] A. Schuchat, “Public Health Response to the Initiation and Spread of Pandemic COVID-19 in the United States, February 24–April 21, 2020,” W. Cockerham and G. Cockerham (Eds.), “The COVID-19 Reader: The Science and What It Says About the Social,” pp. 142-151, Routledge, 2020.
4. [4] S. Rath, A. Tripathy, and R. A. Tripathy, “Prediction of New Active Cases of Coronavirus Disease (COVID-19) Pandemic Using Multiple Linear Regression Model,” Diabetes Metab. Syndr. Clin. Res. Rev., Vol.14, No.5, pp. 1467-1474, 2020.
5. [5] S. Wasserman and K. Faust, “Social Network Analysis: Methods and Applications,” Cambridge University Press, 1994.
6. [6] R. Sandhu, H. K. Gill, and S. K. Sood, “Smart Monitoring and Controlling of Pandemic Influenza A (H1N1) Using Social Network Analysis and Cloud Computing,” J. Comput. Sci., Vol.12, pp. 11-22, 2016.
7. [7] M. K. P. So, A. Tiwari, A. M. Y. Chu, J. T. Y. Tsang, and J. N. L. Chan, “Visualizing COVID-19 Pandemic Risk Through Network Connectedness,” Int. J. Infect. Dis., Vol.96, pp. 558-561, 2020.
8. [8] T. Kuchler, D. Russel, and J. Stroebel, “JUE Insight: The Geographic Spread of COVID-19 Correlates with the Structure of Social Networks as Measured by Facebook,” J. Urban Econ., Vol.127, 103314, 2022.
9. [9] The COVID Tracking Project, “Data Summary,” 2021. https://covidtracking.com/about-data/data-summary [Accessed March 31, 2021]
10. [10] Humantarian Data Exchange, “Facebook Social Connectedness Index,” 2020. https://data.humdata.org/dataset/social-connectedness-index [Accessed October 31, 2021]
11. [11] M. Bailey, R. Cao, T. Kuchler, J. Stroebel, and A. Wong, “Social Connectedness: Measurement, Determinants, and Effects,” J. Econ. Perspect., Vol.32, No.3, pp. 259-280, 2018.
12. [12] N. Shrestha, “Detecting Multicollinearity in Regression Analysis,” Am. J. Appl. Math. Stat., Vol.8, No.2, pp. 39-42, 2020.
13. [13] D. C. Montgomery, E. A. Peck, and G. G. Vining, “Introduction to Linear Regression Analysis,” 5th Edition, John Wiley & Sons, Inc., 2012.
14. [14] L. L. Nathans, F. L. Oswald, and K. Nimon, “Interpreting Multiple Linear Regression: A Guidebook of Variable Importance,” Pract. Assess. Res. Eval., Vol.17, Article 9, 2012. https://doi.org/10.7275/5fex-b874
15. [15] Y. J. Jeng and A. Martin, “Residuals in Multiple Regression Analysis,” J. Pharm. Sci., Vol.74, No.10, pp. 1053-1057, 1985.
16. [16] R. Nau, “Notes on Linear Regression Analysis,” 2014. https://people.duke.edu/rnau/notes_on_linear_regression_analysis–robert_nau.pdf [Accessed July 31, 2022]
17. [17] J. Tang et al., “Agent-Based Simulation and Modeling of COVID-19 Pandemic: A Bibliometric Analysis,” J. Disaster Res., Vol.17, No.1, pp. 93-102, 2022.