That is only 0.1% needing hospitalization. That is great news! See below.
It is important to realize that not everyone gets sick all at once. Maybe in a very fast moving virus such as the professor models (based on the Chinese data) the population moves to the 50% herd immunity threshold over two months. So, you can expect a curve of cases requiring hospitalization over two months and a little more (because of the dispersion of average times for individuals to develop symptoms and have the disease run its course). Perhaps it is one month. The same principle applies, the curve is just steeper and shorter.
Also, in this model only 50% of the population gets infected, so for the United States that would be 340mm * 0.5 (herd immunity threshold) * 0.001 (hypothesized hospitalization rate) = 170,000 people.
I have seen estimates based on ICU beds available that hospital overwhelm would occur in the US at 0.3% infection rate, but this was estimated based on static capacity.
No doubt the repurposing, building and shifting of resources has already raised this ceiling, perhaps to 0.5%. At these estimates, then, overwhelm occurs at between 1.02mm (0.3%) - 1.7mm (0.5%) people sick and needing hospitalization. As there is a curve in hospital admissions, obviously that can be handled theoretically with 170,000 hypothesized hospital admissions. Keep in mind that right now according to worldometers we have about 55,000 cases, and obviously most do not go to the hospital.
People still die, of course, under these assumptions but that is inevitable in any disease. In the end we are just flesh and blood creatures with a very imperfect understanding of our own biology.
On any given day in the United States, approximately 150-200 people die of flu/pneumonia alone, which is more than have died on any given day so far from coronavirus (and no doubt some corona deaths are mislabeled flu/pneumonia cases as well - not all are being tested of course).
Another 100 or more (iirc) die each day from emphysema and COPD-related illnesses (again, some portion are no doubt being wrongly attributed to coronavirus).
Time will tell. I hope this analysis cheers you up, and even more I hope the Oxford professor is right.