- Introduction
In the world of public health, optimization is commonly used to implement the best policies to maintain public safety. This optimization is especially important when it comes to worldwide pandemics like COVID-19. COVID-19 has affected individuals in countless ways. In holistic terms, it has affected the world in 3 big categories – economy, mortality(death) and mobility(ability to move), and social and international relationships.
In general, the financial gap between the wealthy and the poor increased as COVID-19 crushed those dependent on the movement of people while leaving those dependent on the movement of information unscathed or benefitted. In terms of mortality and mobility, many experienced the loss of freedom to move and feel safe outdoors. As of October 2021, there are a total number of 247 million cases and over 5 million deaths worldwide, showing the severity of this issue. Finally, numerous populations have been targeted for hate crimes and bias since the start of the pandemic, preventing many from feeling safe and proud of their own identities.
As can be seen by these issues, the effects of the pandemic must be mitigated as soon as possible. The best way to do so is vaccination, which prevents disease transmissions and symptoms. Vaccine allocation will lower the stigma and fear associated with COVID-19 as it will create herd immunity, lower death rates, and mitigate the symptoms, allowing people to view COVID-19 as more of a common cold/flu rather than a deadly virus. Although the federal government has access to the vaccines, each state can decide its vaccine allocation. This article will discuss a possible usage of constrained optimization in determining a state-wide(Massachusetts) vaccine allocation plan to minimize the COVID-19-associated deaths.
- Constrained Optimization and Consideration of Factors
Constrained optimization is crucial to public health as it allows the consideration of multi-variables. Optimization in public health means considering different factors and relationships to come up with the best possible solution for a problem, which can be death, transmission, prevalence, and more. When there is a limitation to how this problem can be solved, this is called constrained optimization. For instance, such constraints can be financial budget, availability of supply, and time. Some real-world examples of constrained optimization include providing solutions to maximize the distribution of donation supplies with a constraint of supply availability or the distribution of first responders in an emergency with limited human resources.
When using constrained optimization, we need to determine the goal, the objective function that involves the necessary factors to consider(listed below), and the constraint. In this article, the goal is to minimize the COVID-19-associated deaths with successful vaccine allocation. This optimization will suggest which county to distribute vaccines first and the number of vaccines to distribute per age group. The factors to consider are the number of susceptible individuals, contact rate, and the age ratio(>65 years to <65 years) in each Massachusetts county. Lastly, the constraint is vaccine availability. As this optimization is useful at the beginning of vaccine implementation when determining the allocation policies, we will use 70,000 doses as our constraint (In the first and second weeks of vaccine implementation in Massachusetts, the daily average was around 10,000 doses per day. We will consider one week’s worth of doses as our constraint).
To be continued…
references:
1. https://www.brookings.edu/research/explaining-the-economic-impact-of-covid-19-core-industries-and-the-hispanic-workforce/
2. https://www.worldometers.info/coronavirus/coronavirus-death-toll/
3. https://www.hrw.org/news/2020/05/12/covid-19-fueling-anti-asian-racism-and-xenophobia-worldwide#
4. https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html