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

ABSTRACT: 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.

SUBMITTER: Hupert N

PROVIDER: S-EPMC8784160 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn Mexico, the lifetime risk of dying from maternal causes is 1 in 370 compared to 1 in 2,500 in the U.S. Although national efforts have been made to improve maternal services in the last decade, it is unclear if Millennium Development Goal 5--to reduce maternal mortality by three-quarters by 2015--will be met.Methodology/principal findingsWe developed an empirically calibrated model that simulates the natural history of pregnancy and pregnancy-related complications in a cohort of 15-year-old women followed over their lifetime. After synthesizing national and sub-national trends in maternal mortality, the model was calibrated to current intervention-specific coverage levels and validated by comparing model-projected life expectancy, total fertility rate, crude birth rate and maternal mortality ratio with Mexico-specific data. Using both published and primary data, we assessed the comparative health and economic outcomes of alternative strategies to reduce maternal morbidity and mortality. A dual approach that increased coverage of family planning by 15%, and assured access to safe abortion for all women desiring elective termination of pregnancy, reduced mortality by 43% and was cost saving compared to current practice. The most effective strategy added a third component, enhanced access to comprehensive emergency obstetric care for at least 90% of women requiring referral. At a national level, this strategy reduced mortality by 75%, cost less than current practice, and had an incremental cost-effectiveness ratio of $300 per DALY relative to the next best strategy. Analyses conducted at the state level yielded similar results.Conclusions/significanceIncreasing the provision of family planning and assuring access to safe abortion are feasible, complementary and cost-effective strategies that would provide the greatest benefit within a short-time frame. Incremental improvements in access to high-quality intrapartum and emergency obstetric care will further reduce maternal deaths and disability.

Project description:Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region, predicts COVID-19 statistics (the daily number of cases, hospitalizations, and deaths), and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity), different variants of concern, vaccination rate, and mitigation measures. Using Switzerland as a case study, COVIDHunter estimates that we are experiencing a deadly new wave that will peak on 26 January 2022, which is very similar in numbers to the wave we had in February 2020. The policy-makers have only one choice that is to increase the strength of the currently applied mitigation measures for 30 days. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures. We release the source code of the COVIDHunter implementation at https://github.com/CMU-SAFARI/COVIDHunter and show how to flexibly configure our model for any scenario and easily extend it for different measures and conditions than we account for.

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

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