Heterologous vaccination interventions to reduce pandemic morbidity and mortality: modeling the US winter 2020 COVID-19 wave

Journal article

COVID-19 remains a stark health threat worldwide, in part because of minimal levels of targeted vaccination outside high-income countries and highly transmissible variants causing infection in vaccinated individuals. Decades of theoretical and experimental data suggest that nonspecific effects of non–COVID-19 vaccines may help bolster population immunological resilience to new pathogens. These routine vaccinations can stimulate heterologous cross-protective effects, which modulate nontargeted infections. For example, immunization with Bacillus Calmette–Guérin, inactivated influenza vaccine, oral polio vaccine, and other vaccines have been associated with some protection from SARS-CoV-2 infection and amelioration of COVID-19 disease. If heterologous vaccine interventions (HVIs) are to be seriously considered by policy makers as bridging or boosting interventions in pandemic settings to augment nonpharmaceutical interventions and specific vaccination efforts, evidence is needed to determine their optimal implementation. Using the COVID-19 International Modeling Consortium mathematical model, we show that logistically realistic HVIs with low (5 to 15%) effectiveness could have reduced COVID-19 cases, hospitalization, and mortality in the United States fall/winter 2020 wave. Similar to other mass drug administration campaigns (e.g., for malaria), HVI impact is highly dependent on both age targeting and intervention timing in relation to incidence, with maximal benefit accruing from implementation across the widest age cohort when the pandemic reproduction number is >1.0. Optimal HVI logistics therefore differ from optimal rollout parameters for specific COVID-19 immunizations. These results may be generalizable beyond COVID-19 and the US to indicate how even minimally effective heterologous immunization campaigns could reduce the burden of future viral pandemics.

Authors: Hupert, N; Marín-Hernández, D; Gao, B; Águas, R; Nixon, DF

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: National Academy of Sciences
• Journal: Proceedings of the National Academy of Sciences
• Volume: 119
• Issue: 3
• Article number: e2025448119
• Publication date: 2022-01-10
• Acceptance date: 2021-12-02
• DOI: 10.1073/pnas.2025448119
• EISSN: 1091-6490
• ISSN: 0027-8424
• Language: English
• Keywords: BCG; vaccination; COVID-19; infectious disease modeling; trained immunity; FFR