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.