Predicating COVID19 Epidemic in Nepal Using the SIR Model

Thapa, P.

Background: The propagation of infectious diseases through a population is a natural spatial and temporal process of great importance for modern society. Thus, the SIR model of COVID 19 proposed a greater understanding of the disease’s spatial diffusion and prediction. It develops a model that simulates the spread of illness. Methods: The study creates a simulating SIR model in a worst-case scenario COVID-19. It estimates the models’ parameters by minimizing the negative log-likelihood function using the Nelder- Mead method. According to this model, the pandemic height reached by the mid of August, the end of October. About 1 million people of age group 10–19 and 20–29 with a probability of 30% infected by then, which translates to about 552 severe cases, about 175 cases in need of intensive care (there are about 1595 ICU beds, and 840 ventilators are available in 194 hospitals in Nepal, WHO, 2020) and up to 30 deaths per day. Conclusion: This simulation type can help improve comprehension of disease spread dynamics and provide control measures for an epidemic outbreak. The suggestion that authorities need to implement a strict containment strategy over a long period to control disease spreading on the community and humans. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.