article · Results in Applied Mathematics
A mathematical model designated as SHEIQRD has been developed to track and understand the transmission dynamics of COVID-19 alongside public health interventions. The framework categorises populations across susceptible, stay-at-home, exposed, infected, quarantined, recovered, and deceased states. Analysis of the model identified both disease-free and endemic equilibrium points, alongside determining the basic reproduction number. The disease dies out when this reproduction number is less than or equal to one, whereas it persists in the community when the value exceeds one. Sensitivity analysis shows that specific public health measures play a significant role in disease mitigation. In particular, effective stay-at-home policies, precise identification and isolation of exposed and infected persons, lower transmission rates, and managing the rate at which individuals exit stay-at-home status all help mitigate the spread. Theoretical evaluations and numerical simulations demonstrate consistent results.
Understanding the mathematical thresholds that govern outbreak persistence provides public health planners with clearer targets for intervention. By evaluating how stay-at-home orders, testing, and quarantine interact, models like this demonstrate which combinations of public health strategies are required to drive the transmission rate down and prevent long-term disease spread in the wider community.
The abstract describes early-stage theoretical and numerical mathematical modelling rather than a commercial product. The model could potentially inform epidemiological software or decision-support tools used by public health agencies and policy planners to evaluate non-pharmaceutical interventions. However, the abstract does not indicate any current software deployment or commercialisation pathway, placing it at the foundational research stage.
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In this work, a researcher develops SHEIQRD (Susceptible–Stay-at-home–Exposed-Infected–Quarantine–Recovery–Death) coronavirus pandemic, spread model. The disease-free and endemic equilibrium points are computed and analyzed. The basic reproduction number R0 is acquired, and its sensitivity analysis conducted. COVID-19 pandemic spread dies out when R0≤1 and persists in the community whenever R0>1. Efficient stay-at-home rate, high coverage of precise identification and isolation of exposed and infected individuals, reduction of transmission, and stay-at-home return rate can mitigate COVID-19 pandemic. Finally, theoretical analysis and numerical results are shown to be consistent.
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DOI: 10.1016/j.rinam.2020.100123
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