COVID-19 epidemiological studies

This study compares within-host SARS-CoV-2 infection models using RT-qPCR data and finds that simpler models are favored despite potential biological complexity.

This paper introduces a physics-informed machine learning framework for infectious disease forecasting, showing improved accuracy in predicting case and hospitalization counts using a hybrid model of differential equations and machine learning.

This study investigates how temperature and humidity affect the spread of the Omicron variant of SARS-CoV-2 using global data and finds negative correlations between incidence and both variables, especially in mid-latitudes during colder months.

This paper develops an agent-based model to simulate and predict infectious outbreaks in confined environments, showing it can accurately predict infection numbers and suggest improved containment protocols.

The paper quantifies how respiratory pathogens return to pre-pandemic dynamics after interventions, finding that susceptible population replenishment rates determine their resilience to perturbations.

The paper develops a modular mathematical model of the immune response to SARS-CoV-2 infection to better understand disease progression and immune system behavior.

This paper develops a hybrid, multiscale, individual-based model to study SARS-CoV-2 infection spread and immune response, focusing on macrophages and their interactions with viral dynamics and cytokines.

The paper analyzes the spread of COVID-19 using complex systems methods to identify patterns in infection progression and public health response.

This paper analyzes SARS-CoV-2 dynamics using spatio-temporal patterns and entropy to detect phase transitions and cluster formations in global infection data.

This study analyzes epidemic models under social distancing to find optimal timing and duration for minimizing infected population peaks while reducing social distancing time.

The paper models the optimal testing frequency and number of individuals to test in congregate settings to detect and control COVID-19 outbreaks.

The paper clusters U.S. counties by their COVID-19 case patterns over time and identifies demographic, socioeconomic, and political factors associated with these patterns.

This study analyzes how global and regional public health interventions affected SARS-CoV-2 transmission clusters using genetic sequence data and phylogenetic analysis.

This paper uses an SEIR model to estimate the impact of lockdown measures on COVID-19 spread in Malaysia, showing reduced Rt with strict measures and increased Rt with gradual lifting.

The paper uses system dynamics modeling to explore SARS-CoV-2 infection dynamics across different patient groups and immune responses.

January 2020 cases in Germany and Washington State were contained, while separate later introductions from China seeded the first sustained SARS-CoV-2 transmission networks in Italy and Washington State.

The paper develops a transmission network model to simulate the spread of 2019-nCoV from bats to humans, focusing on the reservoir-to-people transmission pathway and calculates the basic reproduction number.

Nationwide data to 7 February 2020 give R0 4.5 and 2.4-day doubling, faster on both counts than SARS, with Rt falling from 8.8 to 1.59 after mid-January.