Results
Data simulated for cancer patients are plotted in Figure 3 and Figure 4.
This model is prepared for a oncology hospital setup and simultaneously
having the pressure to deal with COVID-19. The appearance of cancer
cases is natural and the cumulative number is presented by the red line
in Figure 3. This is a non-communicable disease that will report at its
own pace, and the mortality rate without treatment will merge in Figure
3. But an alarming situation is present as the presence of COVID-19 is
added. Probability of cancer patients getting infected by SARS-CoV-2for
the next sixty days is plotted as a red line in Figure 4. Similarly,
their probability of death is plotted as a black line in Figure 4.
Further, we separated them into two groups i.e. group A and group
B(Figure 5). Group-A represents patients undergoing cancer directed
treatment during the pandemic, and group B were those that were
restricted dueto health serveicesnot rendering treatment. The 30 day
mortality rate in our hosptial audit was 0.9% after completion of
treatment.13 However, there is no data to suggest the
probability of death when no treatment is rendered to stage-IV oral
cancer patients. We accumulated the number of patients that join the
pool as untreated(Group B). Even if we consider all these patients
eventually die, the risk of death within the 60 days is lesser than
those that contract SARS-CoV-2 and die. In absence of treatment the
disease will progress and increase mortality, but does not exceed the
mortality of those infected with SARS-CoV-2. The simulated presentation
on group A and B demonstrated different scenarios. We should prefer to
defer cancer treatement in these patients restricting the mortality to
them.
The current patient load in our hospital is between 800-1000. Each day,
150 patients are registered on average. In the past 3 weeks, a total of
4150 should have been registered. The accumulated cancer mortality of
partients not being treated (Group B) will be inclined linearly and we
can expect that it can come close to 50. We assumed that Group B will
see a cumulative increase of 10% in the rate of patients dying. In
contrast, once infected by COVID-19, we expect to see a 50% cumulative
increasein patients dying in group A. The soultion could revoles around
treating the cancer patient efficiently with minimal clinic visits
reducing their chance of infection.