Public Policy in a Pandemic: A Hazard-Control Perspective and a Case Study of the BCG Vaccine for COVID-19

The first cases of COVID-19 were reported in China in December 2019, quickly spreading to other parts of the world leading to a global pandemic. A number of potential interventions and treatments are being considered. However, in the midst of a pandemic, much early reporting can contain misleading and contradictory data. Thus, reliable information and reasoned perspectives by decision-makers must be attained to minimize the pandemic's current impact, as well as the impact in the likely second wave in the “flu season of 2020-2021.” One potential treatment is the use of booster doses of the Bacille Calmette-Guerin (BCG) vaccine; this vaccine is mandatory at birth in many lower-income nations. In this article, using widely available and reliable data, the relationship of per-capita gross domestic product (GDP) and the BCG vaccine's use on the impact of the virus is studied via statistical models. A strong association is seen between lower per-capita GDP and lower impact. Furthermore, a lower impact is witnessed in countries where the BCG vaccine is mandatory at birth, which suggests that clinical trials need to occur to determine the vaccine's efficacy. Perspectives in safety and risk mitigation needed for management of pandemics and similar events are also provided.

I. Introduction

THE Covid-19 pandemic has emerged from a corona virus that has the ability to spread rapidly across the world. The virus was first detected in China in December 2019. Many factors potentially play a significant role in how quickly diseases spread, e.g., the overall health of populations, history of vaccinations, the average age of the population, ventilation of homes, and social and cultural differences, to name a few. Governmental response to the pandemic has varied across countries, especially in its early stages. Unfortunately, there is a great deal of confusing and contradictory information available to strategic decision-makers in this time. Many measures, typically known as “interventions,” have been commonly recommended to control the spread and reduce loss of life: testing and tracing, quarantining of sick patients, social distancing, and use of masks [1]. However, there are significant issues related to these measures. First, reliable tests have been difficult to produce for corona viruses in general with high rates of false negatives and positives [2]. Second, data are hard to find for the impact of these interventions.