Disproportionately focusing vaccination efforts on the least advantaged populations benefits everyone

Newswise – A new study uses a smart data model to underscore the need to increase vaccination campaign budgets for disadvantaged and vulnerable populations to achieve maximum health benefits for everyone.

When vaccine access is prioritized for the most disadvantaged communities, it improves both social utility and equity – even when these populations have high vaccine hesitancy. An international research team co-led by Professor Pan HUI of Hong Kong University of Science and Technology (HKUST) and HKUST (Guangzhou), Professor James EVANS of the University of Chicago and Professor Yong LI of the University Tsinghua, reveals the key to breaking the dilemma of multiple ethical values ​​from a data-intelligent epidemic model that can accurately predict COVID-19 curves in US metropolitan areas.

Conventional epidemic models make strong assumptions about population mixing, i.e. people in an area mix homogeneously and therefore have equal infection/death rates and equal chances of spread the virus. This is clearly not the case in the COVID-19 pandemic. Instead, the research team designed a model that explicitly incorporates mobility behavior and demographic differences to capture the various COVID-19 risks associated with different communities. The joint incorporation of human mobility data and demographic structures, both at the neighborhood level, allowed the team to more realistically describe how different subpopulations mix. For example, it is crucial to note that low-income families are worse off in this COVID scenario as they must maintain their original level of mobility to earn a living, which puts them at higher risk. Therefore, they are more likely to spread the virus, making them a key group to vaccinate (compared to many white-collar workers who may be working from home).

The study produced two key results. First, it highlights the importance of jointly considering mobility behavior and demographics when designing vaccine prioritization policies. Most existing vaccination programs are designed based only on age or a combination of age and occupation. The US Centers for Disease Control and Prevention has a social vulnerability index that they use in certain regions to prioritize vaccines. Yet it fails to capture behavioral data and the differential likelihood of spreading and being exposed to COVID. In contrast, the proposed model greatly improves the ability to target disadvantaged people who make the most of limited vaccine resources to achieve the greatest benefit for everyone. The researchers also note that their smart model used coarse behavioral data, allaying concerns about privacy leaks. In fact, many sources of aggregated data can be used to feed epidemic models without worrying too much about privacy or other issues.

The second point to remember is that the authors argue for a considerable increase in budgets for vaccination campaigns for disadvantaged and vulnerable populations. This includes both awareness and the risk that vaccinations will be wasted, as uptake may not be as rapid among populations with greater vaccine hesitancy, some with good historical reasons. But more funding among these populations – who move and mix with others in the community – goes a long way to keeping everyone safe. The advantage is persistent even if the reluctance to vaccination of the most disadvantaged populations is five times greater than that of wealthy populations.

Professor Hui, Full Professor of Emerging Interdisciplinary Fields at HKUST and Full Professor of Media and Computational Arts at HKUST (Guangzhou), said: “Epidemics not only threaten our society as a whole, but also exacerbate the inequalities that can tear society apart, as disadvantaged communities face more barriers to reducing high-risk contact and seeking health care. In situations where medical resources are scarce, it is crucial to allocate them intelligently so that society can make the most of them. To this end, our study presents a possible pathway to improve vaccine distribution decisions through data intelligence. Hopefully, the accumulated data and lessons learned from the COVID-19 pandemic can help us better prepare for future challenges.

This work was recently published in the scientific journal Nature Human behavior.