Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2103.00677

Modeling and prediction of COVID-19 in the United States considering population behavior and vaccination

Thomas Usherwood·Zachary LaJoie·Vikas Srivastava
View original source ↗Read source document ↗Export metadata ↓

Abstract

COVID-19 has devastated the entire global community. Vaccines present an opportunity to mitigate the pandemic; however, the effect of vaccination coupled with the behavioral response of the population is not well understood. We propose a model that incorporates two important dynamically varying population behaviors: level of caution and sense of safety. Level of caution increases with the number of infectious cases, while an increasing sense of safety with increased vaccination lowers precautionary behaviors. To the best of our knowledge, this is the first model that can effectively reproduce the complete time history of COVID-19 infections for various regions of the United States and provides relatable measures of dynamic changes in the population behavior and disease transmission rates. We propose a parameter d_I as a direct measure of a population's caution against an infectious disease, that can be obtained from the ongoing new infectious cases. The model provides a method for quantitative measure of critical infectious disease attributes for a population including highest disease transmission rate, effective disease transmission rate, and disease related precautionary behavior. We predict future COVID-19 pandemic trends in the United States accounting for vaccine rollout and behavioral response. Although a high rate of vaccination is critical to quickly end the pandemic, we find that a return towards pre-pandemic social behavior due to increased sense of safety during vaccine deployment, can cause an alarming surge in infections. Our results indicate that at the current rate of vaccination, the new infection cases for COVID-19 in the United States will approach zero by the end of August 2021. The model can be used for predicting future epidemic and pandemic dynamics before and during vaccination.

Explore related subjects

q-bio.PEphysics.soc-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thomas Usherwood, Zachary LaJoie, Vikas Srivastava. 2021-03-01. Modeling and prediction of COVID-19 in the United States considering population behavior and vaccination. https://doi.org/10.1038/s41598-021-91514-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Fixation probabilities for multi-allele Moran dynamics with weak selection

Fixation probabilities are essential for characterizing stochastic evolutionary dynamics, but analytical results remain limited mainly to systems with two competing types. We develop a perturbative framework to compute fixation probabilities in multi-allele Moran processes under weak selection. Exploiting the general structure of the backward Fokker-Planck operator in this regime, we show that fixation probabilities admit a systematic expansion around their neutral solution. We first introduce the framework for $M$ competing alleles with multivariate polynomial fitness functions. We then apply it to three biologically motivated examples: a constant-fitness model, which we solve for an arbitrary number of alleles; a three-allele coordination game in which allele fitness increases with its frequency in the population; and a three-allele model of clonal interference between mutualistic alleles. These results extend the analytical understanding of fixation probabilities beyond pairwise interactions, establishing a framework for investigating multi-strategy stochastic evolutionary dynamics.

q-bio.PE↗
arXiv2026

GrassTop: Grassmannian k-mer Topology for Viral Classification and Phylogenetic Analysis

We introduce GrassTop, a genome representation that integrates Grassmann manifolds and algebraic topology for viral classification and phylogenetic analysis. The framework begins by constructing multiscale topological and spectral descriptors of (k)-mer positional patterns. It then extracts a low-rank subspace that summarizes variation across the filtration and compares genomes using a Grassmannian distance. Although the reported implementation uses the chordal distance, the framework is not restricted to this particular choice. We evaluate GrassTop on four families of viral classification datasets, four phylogenetic clustering datasets, and a sequence perturbation experiment. Under the reported 5-nearest-neighbor protocol, GrassTop achieves higher scores than five published alignment-free reference methods across all reported classification metrics and datasets. Its UPGMA (unweighted pair-group method using arithmetic averages) trees achieve an average label purity of 1.0 on every phylogenetic dataset. The perturbation experiment provides a more nuanced result: the subspace representation differs most clearly from direct comparison of the unprojected feature matrices for SARS-CoV-2, whereas the differences are smaller or non-monotonic for the other datasets. Overall, these results support GrassTop as an effective topological-geometric representation for viral classification and phylogenetic analysis.

q-bio.PE↗
arXiv2026

Anomalous First Passage in Evolution: Edge-KPZ Theory

The pace of evolution depends on how rapidly new phenotypes arise. We show that neutral Wright-Fisher evolution exhibits anomalous first passage despite diffusive mutations. The mean time for the first individual to reach a prescribed phenotypic distance scales approximately as $(σ^2)^{-3/2}$ with mutation variance $σ^2$. Two crossovers bound this regime, with inverse-variance scaling on either side. Combining coalescent theory with Kardar-Parisi-Zhang (KPZ) fluctuations at the dilute population edge, we develop an edge-KPZ theory of all three regimes. The anomaly persists under weak selection.

q-bio.PE↗