Over- and under-estimation of vaccine effectiveness
Abstract Background The effectiveness of SARS-CoV-2 vaccines against infection has been a subject of debate, with varying results reported in different studies, ranging from 60–95% vaccine effectiveness (VE). This range is striking when comparing two studies conducted in Israel at the same time, as...
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BMC
2025-07-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-025-02611-4 |
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| author | Hilla De-Leon Dvir Aran |
| author_facet | Hilla De-Leon Dvir Aran |
| author_sort | Hilla De-Leon |
| collection | DOAJ |
| description | Abstract Background The effectiveness of SARS-CoV-2 vaccines against infection has been a subject of debate, with varying results reported in different studies, ranging from 60–95% vaccine effectiveness (VE). This range is striking when comparing two studies conducted in Israel at the same time, as one study reported VE of 90–95%, while the other study reported only ~ 80%. We argue that this variability is due to inadequate accounting for indirect protection provided by vaccines, which can block further transmission of the virus. Materials and methods We developed a novel analytic heterogenous infection model and extended our agent-based model of disease spread to allow for heterogenous interactions between vaccinated and unvaccinated across close-contacts and regions. We applied these models on real-world regional data from Israel from early 2021 to estimate VE using two common study designs: population-based and secondary infections. Results Our results show that the estimated VE of a vaccine with efficacy of 85% can range from 70–95% depending on the interactions between vaccinated and unvaccinated individuals. Since different study designs capture different levels of interactions, we suggest that this interference explains the variability across studies. Finally, we propose a methodology for more accurate estimation without knowledge of interactions. Discussions and conclusions Our study highlights the importance of considering indirect protection when estimating vaccine effectiveness, explains how different study designs may report biased estimations, and propose a method to overcome this bias. We hope that our models will lead to more accurate understanding of the impact of vaccinations and inform public health policy. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-61690e7b9d3e4b64848e7f91f5ba3a79 |
| institution | Kabale University |
| issn | 1471-2288 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj-art-61690e7b9d3e4b64848e7f91f5ba3a792025-08-20T04:01:35ZengBMCBMC Medical Research Methodology1471-22882025-07-0125111010.1186/s12874-025-02611-4Over- and under-estimation of vaccine effectivenessHilla De-Leon0Dvir Aran1Faculty of Biology, Technion-Israel Institute of Technology, Technion-Israel Institute of TechnologyFaculty of Biology, Technion-Israel Institute of Technology, Technion-Israel Institute of TechnologyAbstract Background The effectiveness of SARS-CoV-2 vaccines against infection has been a subject of debate, with varying results reported in different studies, ranging from 60–95% vaccine effectiveness (VE). This range is striking when comparing two studies conducted in Israel at the same time, as one study reported VE of 90–95%, while the other study reported only ~ 80%. We argue that this variability is due to inadequate accounting for indirect protection provided by vaccines, which can block further transmission of the virus. Materials and methods We developed a novel analytic heterogenous infection model and extended our agent-based model of disease spread to allow for heterogenous interactions between vaccinated and unvaccinated across close-contacts and regions. We applied these models on real-world regional data from Israel from early 2021 to estimate VE using two common study designs: population-based and secondary infections. Results Our results show that the estimated VE of a vaccine with efficacy of 85% can range from 70–95% depending on the interactions between vaccinated and unvaccinated individuals. Since different study designs capture different levels of interactions, we suggest that this interference explains the variability across studies. Finally, we propose a methodology for more accurate estimation without knowledge of interactions. Discussions and conclusions Our study highlights the importance of considering indirect protection when estimating vaccine effectiveness, explains how different study designs may report biased estimations, and propose a method to overcome this bias. We hope that our models will lead to more accurate understanding of the impact of vaccinations and inform public health policy. Clinical trial number Not applicable.https://doi.org/10.1186/s12874-025-02611-4Vaccine effectivenessCOVID-19InterferencePopulation-based studiesAgent-based modelingIndirect protection |
| spellingShingle | Hilla De-Leon Dvir Aran Over- and under-estimation of vaccine effectiveness BMC Medical Research Methodology Vaccine effectiveness COVID-19 Interference Population-based studies Agent-based modeling Indirect protection |
| title | Over- and under-estimation of vaccine effectiveness |
| title_full | Over- and under-estimation of vaccine effectiveness |
| title_fullStr | Over- and under-estimation of vaccine effectiveness |
| title_full_unstemmed | Over- and under-estimation of vaccine effectiveness |
| title_short | Over- and under-estimation of vaccine effectiveness |
| title_sort | over and under estimation of vaccine effectiveness |
| topic | Vaccine effectiveness COVID-19 Interference Population-based studies Agent-based modeling Indirect protection |
| url | https://doi.org/10.1186/s12874-025-02611-4 |
| work_keys_str_mv | AT hilladeleon overandunderestimationofvaccineeffectiveness AT dviraran overandunderestimationofvaccineeffectiveness |