A Mathematical Epidemiological Model (SEQIJRDS) to Recommend Public Health Interventions Related to COVID-19 in Sri Lanka

Abstract

Coronavirus disease 2019 (COVID-19) has been causing negative impacts on various sectors in Sri Lanka, as a result of the public health interventions that the government had to implement in order to reduce the spread of the disease. Equivalent work carried out in this context is outdated and close to ideal models. This paper presents a mathematical epidemiological model, called SEQIJRDS, having additional compartments for quarantine and infected people divided into two compartments as diagnosed and non diagnosed, compared to the SEIR model. We have presented the rate equations for the model and the basic reproduction number is derived. This model considers the effect of vaccination, the viral load of the variants, mask use, mobility, contact tracing and quarantine, natural immunity development of the infected people, and immunity waning of the recovered group as key developments of the model. The model has been validated for the COVID-19 pandemic in Sri Lanka by parameter derivation using mathematical formulations with the help of the existing data, the literature, and by model fitting for historical data. We present a comparison of the model projections for hospitalized infected people, the cumulative death count, and the daily death count against the ground truth values and projections of the SEIR and SIR models during the model validation. The validation results show that the proposed SEQIJRDS model’s 12-week projection performance is significantly better than both the SEIR and SIR models; the 2-, 6-, 8-, and 10-week projection performance is always better, and the 4-week projection performance is only slightly inferior to other models. Using the proposed SEQIJRDS model, we project mortality under different lockdown procedures, vaccination procedures, quarantine practices, and different mask-use cases. We further project hospital resource usage to understand the best intervention that does not exhaust hospital resources. At the end, based on an understanding of the effect of individual interventions, this work recommends combined public health interventions based on the projections of the proposed model. Specifically, three recommendations—called minimum, sub-optimum, and optimum recommendations—are provided for public health interventions.