COVID-19 Epidemiological Models Incorporating Effects of Different Variants of SARS-CoV-2 and/or Impact of Vaccines
| Model Type | Location of Data Used | Research Question | Key Variables/Parameters Considered | Reference |
|---|---|---|---|---|
| Two-variant compartmental model | USA | Assess the effect of VOC B.1.1.7 on the US pandemic trajectory in Jan-Apr 2021 |
SARS-CoV-2 immunity from previous infection; time-varying reproduction numbers of current variants and VOC; potential impact of community vaccination | Galloway et al.16 |
| SIR model with compartments stratified by age, vaccine status, and infecting strain |
Washington, USA | Project the timing and intensity of subsequent waves of infection | Time-varying, age-stratified effects of non-pharmaceutical interventions; different case thresholds for triggering and relaxing partial lockdowns; different vaccination rates and vaccine efficacy profiles; different infectivity rates of current variant and VOC B.1.1.7 | Reeves et al.17 |
| An extended θ-SIR model | Italy | Assess the impact of VOC B.1.1.7 and vaccination campaigns on spread of COVID-19 | Different numbers of SARS-CoV-2 variants and vaccines; time-varying reproduction numbers; differential immunity depending on dose of vaccine | Ramos et al.18 |
| An extended SEIR model(“UVA PatchSim model”) | Virginia, USA | Assess the preliminary effect of vaccines and potential effect of new variants on the trajectory of cases | Seasonal effects, including effect from pandemic fatigue; impact of VOC B.1.1.7; impact of community vaccination | University of Virginia Department of Health19 |
|
An extended SEIR model (“Behavioral SEIR”) | USA; UK | Forecast the evolution of the epidemic | Seasonal variation in transmission rate; pandemic fatigue; spread of the new variant (same seasonal pattern but different transmissibility) | Atkeson20 |
| An extended SEIR model | Brazil | Forecast disease transmission behaviour under 2 SARS-CoV-2 dominant strains | Time-varying reproduction numbers of the original SARS-Cov-2 D614 and its dominant G614 variant; different incubation rates | Celaschi21 |
| Renewal equation based semi-mechanistic model | England | Compare reproduction numbers of VOC with non-VOC | Time-varying reproduction numbers of current variants and VOC B.1.1.7; hotspots of infection; true positive rate adjusted SGTF frequencies (proxy for VOC frequencies) | Volz et al.22 |
| Age-structured SEIR model | India | Assess optimal vaccine allocation strategies | Different age-stratified vaccination strategies and rates; different efficacies and types of immunity from vaccines | Foy et al.25 |
Abbreviations: θ, instantaneous ratio of new detected infectious cases per unit time over the total; COVID-19, coronavirus disease; SARS-CoV-2, severe acute respiratory syndrome coronavirus-2; SEIR, susceptible-exposed-infected-recovered; SGTF, S-gene target failures; SIR, susceptible-infected-recovered; VOC, variant of concern.