Publications by Computational Biology and Bioinformatics

Parag, K., Donnelly, C. and Zarebski, A. (2022) “Quantifying the information in noisy epidemic curves”, Nature Computational Science, 2(9), pp. 584–594.

Dankwa, E. et al. (2022) “Stochastic modelling of African swine fever in wild boar and domestic pigs: epidemic forecasting and comparison of disease management strategies”, Epidemics, 40.

Elliott, P. et al. (2022) “Dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 in England”, Nature Communications, 13(1), p. 4500.

Atchison, C. et al. (2022) “Validity of self-testing at home with rapid severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody detection by lateral flow immunoassay”, Clinical Infectious Diseases, 76(4).

Eales, O. et al. (2022) “Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England”, Nature Communications, 13(1), p. 4375.

Chadeau-Hyam, M. et al. (2022) “Omicron SARS-CoV-2 epidemic in England during February 2022: A series of cross-sectional community surveys”, Lancet Regional Health Europe, 21.

Longini, I. et al. (2022) “A platform trial design for preventive vaccines against Marburg virus and other emerging infectious disease threats”, Clinical Trials, 19(6), pp. 647–654.

Elliott, P. et al. (2022) “Twin peaks: The Omicron SARS-CoV-2 BA.1 and BA.2 epidemics in England”, Science, 376(6600), p. eabq4411.

Pardo-Diaz, J. et al. (2022) “Generating weighted and thresholded gene coexpression networks using signed distance correlation.”, Network Science, 10(2), pp. 131–145.

Chadeau-Hyam, M. et al. (2022) “Breakthrough SARS-CoV-2 infections in double and triple vaccinated adults and single dose vaccine effectiveness among children in Autumn 2021 in England: REACT-1 study”, EClinicalMedicine, 48, p. 101419.