IN THE CONTEXT OF DATA COLLECTION — IS BIGGER BETTER? Can big data help in our fight against COVID-19?

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?

It is crucial to adopt real-time modeling that is not only a computational algorithm but provides timely availability of relevant data, as stated by Birrell et al. (2020). Interdisciplinary collaboration both the U.S. and globally must embrace the promise of real-time modeling that provides a timely, relevant data available that could produce a novel cutting edge support tool and methodology for emergency and disaster management and public health policy.

References

Birrell, P. J., Wernisch, L., Tom, B. D., Held, L., Roberts, G. O., Pebody, R. G., & De Angelis, D. (2020). Efficient real-time monitoring of an emerging influenza pandemic: How feasible?. Annals of Applied Statistics, 14(1), 74-93.

Labilles, U. (2016). The New Public Health: Beyond Genetics and Social Inequalities. Unpublished manuscript, College of Health Sciences, Public Health, Epidemiology, Walden University, Minneapolis.

Labilles, U. (2017). Pathopoiesis Mechanism of Smoking and Shared Genes in Pancreatic Cancer.

Olson, D. R., Lopman, B. A., Konty, K. J., Mathes, R. W., Papadouka, V., Ternier, A., … & Pitzer, V. E. (2020). Surveillance data confirm multiyear predictions of rotavirus dynamics in New York City. Science advances, 6(9), eaax0586.

Petropoulos, F., & Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PloS one, 15(3), e0231236.