At the beginning, the rate at which the number of deaths was growing was 26% per day, or doubling approximately every 3 days. This means that in the two weeks that it takes for the average person that is going to die of covid to die of covid, the number of people infected has grown by a factor of 2^4 to 2^5. So by the time that 30 people have died, It is reasonable to suspect that that the number of infections had grown by an order of magnitude since those people were infected, and those people are 1.5% of the people who had been infected two weeks ago. (This back of the envelope calculation is very sensitive to changes in the time to death distribution for people who have contracted covid, particularly to number of people that die fast.)

Furthermore, your infection fatality ratio is entirely wrong. My 1.5% was very optimistic. South Korea has the most exhaustively tested population on earth, and their case fatality rate is 2%, and it's worse among cases that have reached an endpoint. The virus could have mutated and attenuated since then, but other evidence suggests that the New York strain was more lethal than the SK strain, not less.

The Sciencemag paper that you have linked relies on a "seroprevalence of 3%", despite the parenthetical statement right next to their assumption that the confidence interval on that seroprevalence is between 0 and 3 percent. So not only have they chosen the maximum value for seroprevalence in that interval as their assumption, but the interval actually includes zero. Antibody testing cannot say with 95% confidence that any of its positive results were not false positives. That's a pretty bad test.