Aim: This study aims to empirically map and analyze the emergent literary genre of "Crisis Literature" in direct response to the COVID-19 pandemic. It seeks to define its core thematic and stylistic characteristics, quantify its production trends, and assess its role in cultural sense-making during a global crisis.

Methodology: A mixed-methods sequential explanatory design was employed. First, a quantitative computational analysis was conducted on a bibliometric corpus of 12,457 English-language fictional narratives (novels, short story collections) published globally between 2020 and 2024. Data was sourced from ISBN databases, major publisher catalogs, and digital archives. Natural Language Processing (NLP) techniques, including Latent Dirichlet allocation (LDA) for topic modeling and sentiment analysis, were applied. Second, a purposive qualitative sample of 42 texts identified as central to the genre underwent close thematic analysis using NVivo software.

Key Results: The quantitative analysis revealed a 318% increase in pandemic-themed fiction from 2020 to 2022, plateauing in 2023-2024. Topic modeling identified five dominant thematic clusters: "Domestic Claustrophobia & Relationship Strain" (32.1% prevalence), "Societal Collapse & Institutional Distrust" (28.7%), "Medical Trauma & Frontline Narratives" (18.9%), "Temporal Disorientation & Lockdown Time" (12.5%), and "Ecological Intersectionality" (7.8%). Sentiment trajectories showed a significant shift from overwhelmingly negative sentiment (mean polarity: -0.72) in early-pandemic works (2020-2021) to more complex, ambivalent profiles (mean polarity: -0.21) by 2023-2024 (p < 0.001). The qualitative analysis refined these clusters, identifying key narrative devices, including fragmented chronologies, hybridized narrative perspectives, and the pervasive use of digital communication as a plot mechanism.