A recent perspective article published in the prestigious journal Science highlights how the use of network-driven strategies for adequately informing rapidly emergent epidemic responses is not only evidence-based but also an equitable way forward for the current coronavirus disease (COVID-19) pandemic and future respiratory pandemics.
Even though each contact carries a certain risk of acquiring an infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), real-world social networks in the context of an ongoing COVID-19 pandemic should be viewed as rather complex and extremely heterogeneous.
In reality, risk factors tend to coalesce among a smaller number of individuals with inordinate higher exposure to the virus and subsequent transmission risks. The patterns of such individual heterogeneity are visible in the data, indicating higher risks of infection as a result of increased exposure frequency and multiple contacts.
The experience from many different countries shows that individuals working in public-facing professions or low-paid jobs had the highest risk of infection; furthermore, there is also an obvious intersection of COVID-19 risk and socioeconomic inequities (especially considering poverty, job insecurity and crowded housing).
And, albeit certain high-frequency contacts can be driven by large gatherings and get-togethers (which can be addressed by education and enforcements), a bulk of high-risk exposures are in fact non-modifiable risks due to working and living conditions that cannot be addressed purely by changing individual choices.
In the recent viewpoint published in the journal Science, Dr. Müge Çevik (a clinical lecturer in infectious diseases and medical virology at the University of St Andrews School of Medicine) and Dr. Stefan D. Baral (an Associate Professor at Johns Hopkins Bloomberg School of Public Health) discuss some of these implications in depth.
Structural conditions more predictive than individual choices
As already hinted before, there is a consensus that states that individuals residing in the most economically disadvantaged neighborhoods and largest families/households have a significantly increased risk of infection and disease burden. At the same time, inequities can additionally augment that risk through connections between their networks.
Consequently, Dr. Çevik and Dr. Baral argue that structural conditions affecting an individual’s network and exposure risk can be far more predictive than individual choices in appraising whether a certain infection will be a terminal event, or whether it will result in manifold downstream infections.
Hence, a comprehensive and targeted approach towards the needs of a few that harbor disproportionate risks can prevent more downstream infections than simply eliminating a small risk that is present among many. This is something that should be taken into account in any modeling and mitigation strategy against COVID-19.
Addressing individual heterogeneity and infection risk
In a nutshell, public health policies that are implemented based on the premise of equal acquisition and transmission risk across all socioeconomic, age, and occupational groups will leave specific communities more vulnerable to a higher risk of infection with SARS-CoV-2, which subsequently translates to a differential disease burden.
“Leveraging network heterogeneity in infectious disease models may better demonstrate these differential risks observed in real-life epidemiological analyses and the benefits of prioritizing intensive and targeted interventions to those with differential risks, given the potential for larger numbers of averted downstream infections”, say Dr. Çevik and Dr. Baral in this viewpoint.
Thus, all policy interventions should take into account the overall number of contacts of a specific individual, as well as downstream infections averted on the basis of differential impacts in various communities. Moreover, targeted interventions could (and should) be leveraged in accordance with individual and network-level needs.
The advantages of network-driven strategies
“The focus of COVID-19 response strategies has often been on behavior change as a primary means of decreasing contact networks and thus transmission chains”, explains Dr. Çevik and Dr. Baral. “However, contact patterns are driven, in large part, by socioeconomic inequities and structural racism and are non-modifiable at the individual level in the absence of specific support,” they emphasize.
Therefore, non-adaptive public health interventions fall short in addressing individual heterogeneities and increase the risk of infection, fatal outcomes and austerity in socioeconomically marginalized communities. Furthermore, as lower vaccine uptake could sustain existing inequalities among these communities, it is pivotal to strengthen community-led vaccine delivery strategies.
“Using network-driven strategies to inform rapidly emergent epidemic responses represents an evidence-based and equitable path forward where the aim is to invest more to prevent infections in a person with disproportionate risks because disease burden and downstream infection risks vary substantially”, concludes Dr. Çevik and Dr. Baral in their Science commentary.
Taking into account historical data on pandemics, the disparities that have defined the epidemiology of COVID-19 could have been predicted and addressed. And since the next global outbreak of respiratory disease may also arise in the context of similar disparities, these lessons should be considered as we go forward with our pandemic preparedness plans.
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