The United States is slowly relaxing stay-at-home orders and reopening businesses to reverse the nation’s ailing economy after the spread of the coronavirus that killed millions of jobs and pummeled the global economy. The virus was initially identified as a cluster of pneumonia cases in Wuhan, Hubei Province of China. Later reclassified as a Novel Coronavirus on December 31, 2019, by the World Health Organization (WHO), and health experts in the US, Canada, Germany, Russia, China, Korea, Japan, and Nigeria declared COVID-19 as a Global Pandemic. During the peak of the COVID pandemic, the Centers for Disease and Prevention, the U.S. Centers for Medicare and Medicaid Services, and professional organizations issued countermeasures to flatten the curve, such as elective surgical procedure cancellation. The coronavirus infections are still on the upswing in Texas, Arizona, Florida, and Utah.
A system to forecast the transmission of the virus based on live and current data is critical (Petropoulos & Makridakis, 2020). Cumulative first wave data aggregated nationally and globally could provide accurate forecasting of probable second wave COVID-19 transmission. While accurate forecasting of the virus’s spread is essential, it is critical to establish guidelines to avoid a Coronavirus rebound and frivolous lawsuits that could stunt economic recovery. In the absence of liability protection that shields medical facilities, and employers, it is critical to developing a scheduling point system that controls the number of patients in the waiting area that could be exposed to the asymptomatic transmission of COVID-19. Even with robust liability protection, negligent facilities could still be punished and be sued for damages. The average wait period is 18 minutes during a medical visit. The patients have enough time to mingle, increasing the probability of transmission of infectious diseases based on the amount and nature of contacts between healthy and infected individuals (Goscé & Johansson, 2018). A priori tumor grading scale in scheduling Mohs patients in the post-COVID period as it relates to the parameters for diffusion is vital for the safety and protection of both the patients and healthcare workers. Depending on the tumor grade, the overall degree of connectivity, comorbidity in association to tumor history using the Labilles “United Paradigm of Cancer Causation” (2017), the patient needs to be seen as the earliest. The grading scale introduced on the “Manual of Frozen Section Processing for Mohs Micrographic Surgery” (2008) is an urgency-scoring system to assist Mohs surgeons, administrators, and staff in triaging surgery patients.
References
Beggs, C. B., Shepherd, S. J., & Kerr, K. G. (2010). Potential for airborne transmission of infection in the waiting areas of healthcare premises: stochastic analysis using a Monte Carlo model. BMC infectious diseases, 10(1), 247.
Franczyk, B. (2019, July). An Intelligent and Data-Driven Decision Support Solution for the Online Surgery Scheduling Problem. In Enterprise Information Systems: 20th International Conference, ICEIS 2018, Funchal, Madeira, Portugal, March 21-24, 2018, Revised Selected Papers (Vol. 363, p. 82). Springer.
Friedman, E., Friedman, J., Johnson, S., & Landsberg, A. (2020). Transitioning out of the coronavirus lockdown: A framework for zone-based social distancing. arXiv preprint arXiv:2004.08504.
Goscé, L., & Johansson, A. (2018). Analysing the link between public transport use and airborne transmission: mobility and contagion in the London underground. Environmental Health, 17(1), 84.
Labilles, U. (2017). Pathopoiesis mechanism of smoking and shared genes in pancreatic cancer (Copyright Registration No. TX0008490984) [Ph.D. dissertation, Walden University]. ProQuest Dissertations Publishing.
McCulloch, Hetzer, Geddis, McConnell, Brock, Keating, Labilles, Marino, Beck, Wade & Fisher (2008). Manual of Frozen Section Processing for Mohs Micrographic Surgery. In F. Fish III (Ed.). Labilles, U, Immunohistochemistry (p.1201). American College of Mohs Surgery.
Petropoulos, F., & Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PloS one, 15(3), e0231236.
The traditional researcher concept that big data equates statistical significance should not eclipse the importance of understanding the interrelationship between the effect size, power, and sample size that could translate to both practical and statistical significance. It is critical to be guided by a working theory that gives birth to a useful solution to any given problem, such as in cancer epidemiology, most importantly, to the current COVID-19 pandemic. In 2017, the ”Unified Paradigm of Cancer Causation (UPCC),” a metatheory on cancer epidemiology, was introduced. The premise of this theory can be used in contact tracing as it relates to correlation with socio-behavioral risk factors, environmental interaction, and demographic determinants (SEDD). Understanding the trajectory of COVID-19 transmission could benefit from lessons learned from past and existing legal measures on efficient delivery of emergency and disaster relief not only by updating research reporting protocols but using a theory that bridge the gap between the federal, state, and local government. Petropoulos and Makridakis (2020) highlighted the integral significance of investigating the unknown variables associated with COVID-19 transmission. It is vital to explore the compounding factors that derail emergency relief. Should there be an improved, coordinated effort from the federal, state, and local governments? Are there other compounding factors in establishing an effective strategy in flattening the curve? In the next series, the questions on the COVID-19 epidemiology and its impact on Public Health will be explored, as well as how vital to streamline the Public Health Emergency Preparedness and Disaster Relief Systems. Months after the first case of the virus in the U.S, should we already have started establishing a seasonality pattern prediction protocol? This protocol does include not only age-specific social dynamics but also other compounding factors that complicate the public health countermeasure on social distancing?
Modern epidemiology is a direct result of the paradigm shift from a population-based (upstream) to a downstream (individual) approach. The impact of modern epidemiology such as ‘molecular’ and ‘genetic’ epidemiology (Loomis & Wing, 1990; Diez-Roux, 1998) requires an explanatory power that mostly dependent upon the advances in technology and information systems. Moreover, before estimating the economic effect of a specific intervention before it is implemented, nor assess the economic and/or quality-of-life value of an ongoing or anticipated intervention (Rothermel, 2013); it is critical to recognize not only the significance of sophisticated technologies that go beyond the established genome, proteome, and gene expression platforms, but also new techniques of study design and data analysis (Pearce, 1996; Verma, Khoury & Ioannidis, 2013). Given the remarkable progress in the last decade in advanced technology and new methods for biologic measurements, the reductionist approach of modern epidemiology often ignored the significant causes of disease. Pearce (1996) argue that epidemiology must reintegrate itself into public health and must rediscover the population perspective. However, while the new paradigm could produce a lifestyle approach to social policy, the cumulative outcome of research in cancer epidemiology could equate positive implications to population health.
