The Global Patient: Understanding COVID-19's Cross-Border Dynamics
Authors/Creators
Description
Understanding the global spread of COVID-19 is crucial for effective pandemic management. This study delved into the interconnectedness of nations by examining daily case counts from January 2020 to January 2023. We sought to uncover hidden patterns in the virus's behaviour across borders, aiming to enhance forecasting accuracy. By analysing data from reliable sources like Johns Hopkins University and the World Health Organization, we discovered striking similarities in the COVID-19 trajectories of many countries. Over sixty nations shared strong connections, suggesting that the pandemic's evolution was influenced by shared global factors. These findings highlight the importance of a global perspective in predicting disease outbreaks. By identifying this interconnectedness, we can develop more precise forecasting models and provide policymakers with essential insights to combat future health crises.
Files
The Global Patient -Formatted Paper.pdf
Additional details
References
- 1. Taylor J.W, Taylor K.S(2023). Combining probabilistic forecasts of COVID-19 mortality in the United States. Eur J Oper Res: 304: 25–41.
- 2. Zhou F, Yu T, Du R, et al. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet; 395: 1054–1062.
- 3. Gianmoena L and Rios V. Forecasting the spread of the COVID-19 epidemic in Lombardy: a dynamic model averaging approach. medRxiv 2021: 2021.01.18.21250053.
- 4. Güngör M. Time series forecasting of the COVID-19 pandemic: a critical assessment in retrospect. Alphanumeric Journal 2023; 11: 85–100.
- 5. Miralles-Pechuán L, Kumar A and Suárez-Cetrulo AL. Forecasting COVID-19 cases using dynamic time warping and incremental machine learning methods. Expert Systems 2023; 40: e13237.
- 6. Sendur A and Cakir Z. A comparative study for COVID-19 forecasting models. International Conference on Scientific and Innovative Studies 2023; 1: 195–199.
- 7. Vega R, Shah Z, Ramazi P, et al. Modeling and forecasting COVID-19 cases using latent subpopulations. arXiv 2023; arXiv: 2302.04829.
- 8. Agarwal D, Patnaik N, Harinarayanan A, et al. Forecasting geo location of COVID-19 herd. Pertanika Journal of Science and Technology 2023; 31: JST-3831-2022.
- 9. Dong E, Du H and Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis 2020; 20: 533–534.
- 10. Musa HH, Musa TH, Musa IH, et al. Addressing Africa's pandemic puzzle: perspectives on COVID-19 transmission and mortality in sub-Saharan Africa. Int J Infect Dis 2021; 102: 483–488.