How do I interpret this QQ plot and residual vs fitted plot?

I have a model in R looking at infectious disease spread on social networks, and I am running into a problem where my data are clearly not normally-distributed when I try to run a linear regression but I'm unsure of how to approach it. My dependent variable/outcomes are maximum prevalence, time of peak infection, length of outbreak, cumulative infection, and maximum Reff. My independent variables are homophily based on SES, homophily based on health behavior, probability of isolation, and length of isolation. The model is simulation based and I end up with 1000 data points. I have some plots attached here using just the dependent variable of maximum prevalence, because the plots for all the outcomes look similar (from top left to right, then to the bottom row): histogram of my outcome, outcome vs residuals, histogram of residuals, fitted values vs residuals, QQ plot, and density of residuals. I have pretty limited statistical knowledge so I'm unsure of the best next step to approach this. HEAVY right skew based on the histograms but... (see point 3) Residuals are dependent on the outcome; I'm thinking I'm not using the right distribution for this analysis (I used a normal distribution for these); is there a way to diagnose what the correct distribution would be? QQ plot looks to me like it's indicating a heavy right skew, but log-transforming the data doesn't make anything look much better, including adjusted model R^2 and the QQ plot (see below) My only guess is that I'm us…