Estimation of Underreported Cases of Infections and Deaths from COVID-19 for Countries with Limited and Scarce Data: Examples from Nepal

COVID-19 pandemic has overburdened the public healthcare system around the world. Further, lockdown imposed to curb the spread of pandemic has shown to have an adverse effect on economic and health status of an individual. It has also compelled us to switch from the physical world to virtual world, thus depriving us of benefits of person-to-person direct contact. People from developing countries are specially affected. An average person here lacks basic skills needed to survive in the digital world. Due to limited COVID-19 testing capacities in such countries, there is also less testing. Less testing means less contact tracing, underreported cases, and rapid spread of disease. In this paper, the underreported cases of daily infections and daily deaths are predicted using mathematical models. This is based on daily data published by the Government of Nepal. Here, Kathmandu valley is taken as a model area for estimation of underreporting. The behavior of probability of infection, probability of recovery, and probability of deaths is also mathematically analyzed. A time-dependent susceptible infected and recovered model is also proposed. Here, the second wave of COVID-19 is analyzed in detail from 1 Feb 2021 to 1 June 2021. The effect of lockdown on the psychology of people is also modeled with principal components analysis. The inherent and latent factors affecting the people in lockdown are identified. This is based on detailed primary data collected from a survey of 277 households.