  Background: Providing appropriate care for people suffering from COVID-19,
the disease caused by the pandemic SARS-CoV-2 virus is a significant global
challenge. Many individuals who become infected have pre-existing conditions
that may interact with COVID-19 to increase symptom severity and mortality
risk. COVID-19 patient comorbidities are likely to be informative about
individual risk of severe illness and mortality. Accurately determining how
comorbidities are associated with severe symptoms and mortality would thus
greatly assist in COVID-19 care planning and provision.
  Methods: To assess the interaction of patient comorbidities with COVID-19
severity and mortality we performed a meta-analysis of the published global
literature, and machine learning predictive analysis using an aggregated
COVID-19 global dataset.
  Results: Our meta-analysis identified chronic obstructive pulmonary disease
(COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2
diabetes, malignancy, and hypertension as most significantly associated with
COVID-19 severity in the current published literature. Machine learning
classification using novel aggregated cohort data similarly found COPD, CVD,
CKD, type 2 diabetes, malignancy and hypertension, as well as asthma, as the
most significant features for classifying those deceased versus those who
survived COVID-19. While age and gender were the most significant predictor of
mortality, in terms of symptom-comorbidity combinations, it was observed that
Pneumonia-Hypertension, Pneumonia-Diabetes and Acute Respiratory Distress
Syndrome (ARDS)-Hypertension showed the most significant effects on COVID-19
mortality.
  Conclusions: These results highlight patient cohorts most at risk of COVID-19
related severe morbidity and mortality which have implications for
prioritization of hospital resources.
