Dear Colleagues，

Priorities all over the world were changed by the ongoing coronavirus disease 2019 (COVID-2019) pandemic. Health systems thus face financial and facility challenges when managing this condition. Many studies reported the clinical characteristics and outcomes of COVID-19 patients, but few research studies focused on the risk assessment of outcomes [1–5]. Personal risk profiles can help physicians make the correct decision for optimal patient treatment.

Length of stay in the intensive care unit, duration of invasive ventilation, and the probability of death can describe the temporal dynamics of illness severity among hospitalized COVID-19 patients [6]. Statistical methods for analyzing time-dependent data could be applied for hospitalized COVID-19 patients [7]. “Fighting panic with information” [8] implies using proper statistical models to deduce the risk of bias [9,10]. The integration of proper biostatistical models and machine learning algorithms are helpful in health policymakers and risk assessment during current or future pandemics [11]. This Special Issue thus focuses on automatic COVID-19 diagnosis and prognosis, based on rigorous machine learning and biostatistical models, except mechanisms of pathogenesis, which are not within the scope of the Special Issue.

It is necessary to use STARD [12] (or) TRIPOD [13] guidelines (https://www.equator-network.org/) for diagnostic/prognostic studies, to ensure that the submitted papers have the acceptable quality.

Prof. Dr. Martin Wolkewitz
Prof. Dr. Hamid Reza Marateb
Guest Editors

References:

1. Das AK, Mishra S, Saraswathy Gopalan S. Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool. PeerJ. 2020;8:e10083.
2. Marateb HR, von Cube M, Sami R, Haghjooy Javanmard S, Mansourian M, Amra B et al. Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study. BMC Medical Research Methodology. 2021;21(1):146.
3. Kar S, Chawla R, Haranath SP, Ramasubban S, Ramakrishnan N, Vaishya R et al. Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID). Scientific Reports. 2021;11(1):12801.
4. Heldt FS, Vizcaychipi MP, Peacock S, Cinelli M, McLachlan L, Andreotti F et al. Early risk assessment for COVID-19 patients from emergency department data using machine learning. Scientific Reports. 2021;11(1):4200.
5. Yan L, Zhang H-T, Goncalves J, Xiao Y, Wang M, Guo Y et al. An interpretable mortality prediction model for COVID-19 patients. Nature Machine Intelligence. 2020;2(5):283-8.
6. Hazard D, Kaier K, von Cube M, Grodd M, Bugiera L, Lambert J et al. Joint analysis of duration of ventilation, length of intensive care, and mortality of COVID-19 patients: a multistate approach. BMC Medical Research Methodology. 2020;20(1):206.
7. Beyersmann J, Wolkewitz M, Allignol A, Grambauer N, Schumacher M. Application of multistate models in hospital epidemiology: Advances and challenges. Biometrical Journal. 2011;53(2):332-50.
8. The Lancet. COVID-19: fighting panic with information. Lancet (London, England). 2020;395(10224):537.
9. Wolkewitz M, Puljak L. Methodological challenges of analysing COVID-19 data during the pandemic. BMC Medical Research Methodology. 2020;20(1):81.
10. Marateb HR, Mohebbian MR, Shirzadi M, Mirshamsi A, Zamani S, Abrisham chi A et al. Reliability of machine learning methods for diagnosis and prognosis during the COVID-19 pandemic: a comprehensive critical review. 2021. In: High Performance Computing for Intelligent Medical Systems [Internet]. IOP Publishing; [5-1-5-25]. Available from: http://0-dx-doi-org.brum.beds.ac.uk/10.1088/978-0-7503-3815-8ch5.
11. Mansourian M, Marateb HR, Cube Mv, Khademi S, Jordanic M, Mananas MA et al. Reliable Diagnosis and Prognosis of COVID-19. In: Bajaj V, Sinha G, editors. Computer-aided Design and Diagnosis Methods for Biomedical Applications: CRC Press; 2021.
12. Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6(11):e012799.
13. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Medicine. 2015;13(1):1.

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• machine learning
• multi-state models
• risk assessment models
• survival analysis