Human cytomegalovirus can cause a diverse range of diseases in different immunocompromised hosts. The pathogenic mechanisms underlying these diseases have not been fully elucidated, though the maximal viral load during infection is strongly correlated with the disease. However, concentrating on single viral load measures during infection ignores valuable information contained during the entire replication history up to the onset of disease. We use a statistical model that allows all viral load data sampled during infection to be analysed, and have applied it to four immunocompromised groups exhibiting five distinct cytomegalovirus-related diseases. The results show that for all diseases, peaks in viral load contribute less to disease progression than phases of low virus load with equal amount of viral turnover. The model accurately predicted the time of disease onset for fever, gastrointestinal disease and pneumonitis but not for hepatitis and retinitis, implying that other factors may be involved in the pathology of these diseases.