Generalized Susceptible-Exposed-Infectious-Recovered model and its contributing factors for analysing the death and recovery rates of the COVID-19 pandemic

Dynamic zero-COVID strategy in controlling COVID-19 in Shanghai, China: A cost-effectiveness analysis

BACKGROUND

The sustainability and generalizability of China's dynamic zero-COVID strategy on eliminating SARS-CoV-2 transmission has casted doubt globally, mainly because it has exacted high social and economic cost. This study aimed to estimate the disease burden during the first wave of Omicron in China and compared the cost-effectiveness of implementing a Real-world strategy (adjusted dynamic zero-COVID strategy) with two simulated strategies (routine and stricter dynamic zero-COVID strategy) to inform appropriate strategies for COVID-19 pandemic control.

METHODS

A dynamic state-transition simulation model was developed to compare the health and cost outcomes between different dynamic zero-COVID strategies. Omicron-related healthcare costs were estimated from the societal perspective. Epidemiological parameter values were derived from data of real-world or generated by model calibration; costs and effectiveness parameter values were informed either by local data or published literature. The primary outcomes were total social cost, disability adjusted life-years (DALYs) and net monetary benefit (NMB). Deterministic sensitivity analyses (DSA) and scenario analyses were performed to assess the model robustness.

RESULTS

The first wave of Omicron in Shanghai resulted in 47,646 DALYs lost and 415 billion RMB losses. At a willingness-to-pay threshold of 173,630 RMB (the GDP per capita of Shanghai in 2021) per DALY saved, the Real-world strategy was considered as the most cost-effective strategy due to its highest NMB (-407 billion). Results from DSA confirmed the robustness of our findings.

CONCLUSION

Our finding supported the Real-world strategy taken by the Shanghai Municipal Government between March 1 and May 21, 2022 to control the first wave of Omicron outbreak. Moreover, our results indicated that whether the Stricter dynamic zero-COVID strategy is worth implementing at the beginning of the COVID-19 outbreak mainly depended on the infection rate of COVID-19 among primary contacts. Our analysis provides important evidence to inform policy makers to make appropriate decisions regarding COVID-19 pandemic management.

Modulating Immune Response in Viral Infection for Quantitative Forecasts of Drug Efficacy

The antiretroviral drug, the total level of viral production, and the effectiveness of immune responses are the main topics of this review because they are all dynamically interrelated. Immunological and viral processes interact in extremely complex and non-linear ways. For reliable analysis and quantitative forecasts that may be used to follow the immune system and create a disease profile for each patient, mathematical models are helpful in characterizing these non-linear interactions. To increase our ability to treat patients and identify individual differences in disease development, immune response profiling might be useful. Identifying which patients are moving from mild to severe disease would be more beneficial using immune system parameters. Prioritize treatments based on their inability to control the immune response and prevent T cell exhaustion. To increase treatment efficacy and spur additional research in this field, this review intends to provide examples of the effects of modelling immune response in viral infections, as well as the impact of pharmaceuticals on immune response.