Data-driven models for the risk of infection and hospitalization during a pandemic: Case study on COVID-19 in Nepal

• Published in: Journal of theoretical biology 2023-10, Vol.574, p.111622-111622, Article 111622
• Main Authors: Adhikari, Khagendra, Gautam, Ramesh, Pokharel, Anjana, Uprety, Kedar Nath, Vaidya, Naveen K.
• Format: Article
• Language: English
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2023.111622

The newly emerging pandemic disease often poses unexpected troubles and hazards to the global health system, particularly in low and middle-income countries like Nepal. In this study, we developed mathematical models to estimate the risk of infection and the risk of hospitalization during a pandemic which are critical for allocating resources and planning health policies. We used our models in Nepal’s unique data set to explore national and provincial-level risks of infection and risk of hospitalization during the Delta and Omicron surges. Furthermore, we used our model to identify the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate COVID-19 in various groups of people in Nepal. Our analysis shows no significant difference in reproduction numbers in provinces between the Delta and Omicron surge periods, but noticeable inter-provincial disparities in the risk of infection (for example, during Delta (Omicron) surges, the risk of infection of Bagmati province is: ∼ 98.94 (89.62); Madhesh province: ∼ 12.16 (5.1); Karnali province ∼31.16 (3) per hundred thousands). Our estimates show a significantly low level of hospitalization risk during the Omicron surge compared to the Delta surge (hospitalization risk is: ∼10% in Delta and ∼2.5% in Omicron). We also found significant inter-provincial disparities in the hospitalization rate (for example, ∼ 6% in Madhesh province and ∼ 21% in Sudur Paschim) during the Delta surge. Moreover, our results show that closing only schools, colleges, and workplaces reduces the risk of infection by one-third, while a complete lockdown reduces the infections by two-thirds. Our study provides a framework for the computation of the risk of infection and the risk of hospitalization and offers helpful information for controlling the pandemic. •We develop a data-driven model to estimate a real-time risk of infection of COVID-19.•We develop a data-driven model to estimate a real-time risk of hospitalization for COVID-19.•We estimate the risk of infection and hospitalization during the Delta and Omicron waves in Nepal.•We evaluate non-pharmaceutical interventions to reduce the risk of infection.•The developed models help manage healthcare resources to minimize the burden of pandemics.