Public Health Intervention Framework for Reviving Economy (2): Use of Personalized Measures Beyond the Epidemiological Model Limits

Jianqing Wu, Ping Zha

Abstract


We previously proposed a public intervention framework concept that would allow people to resume personal and economic activities. We showed that intervention measures are used in a quantitative scale to reduce transmission probabilities and disease severity. In this article, we systematically examine the origin, assumptions, performance and limitations of epidemiological models from different angles used in published researches. We found that nearly all model assumptions fail to hold or are remote from reality; R0 is a variable depending on a large number of factors or has no utility in guiding treatment options; personalized intervention measures are vitally important due COVID-19 transmission characteristics, but current epidemiological models are unable to accurately assess the true benefits of personalized intervention measures. We found that poor performance of the models are attributed to flawed assumption that health/disease properties can be treated as transferable properties. The flaw creates a fiction that disease properties such as infection probabilities and death risks can be transferred from any vulnerable person to any other person in the population, and thus severely limits societal ability to fight the pandemic. We finally show that the benefits of personalized mitigation measures could be determined directly by using variable Ri values for infected persons (or nodes) together with assessment of death rate and disability rate; the attempt of avoiding the disease by defeating all potential transmission probabilities is unrealistic; but mitigating disease severity for specific persons is more feasible and reliable. A most reliable strategy for reviving economy should include personalized protective and mitigation measures for improving personal health.

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