Let's say $X_i$ is the number of incidents on day $i$, and $Y$ is the total number of incidents in $30$ days, so $Y=\sum_{i=1}^{30} X_i$. We assume that the $X_i$'s are independent and all have the stated distribution.
By calculation from the probabilities provided, the mean of $X_i$ is $\mu_X=0.65$, and the variance is $\sigma^2_X =0.7075$. So the mean of $Y$ is $\mu_Y = 30 \mu_X = 19.5$, and the variance is $\sigma^2_Y = 30 \sigma^2_X = 21.225$.
It seems reasonable to approximate $Y$ with a Normal distribution with mean $\mu_Y$ and variance $\sigma^2_Y$. Then $Z= (Y-\mu_y)/\sigma_Y$ has a Normal(0,1) distribution, and we can find using tables of the normal distribution or by use of software that $P(Y < 20.5) \approx 0.586$, so $P(Y > 20.5) = 1 - P(Y < 20.5) \approx \boxed{0.414}$.
It is also possible to find an exact probability that $Y > 20$ using a probability generating function and a computer algebra system, such as Mathematica. It turns out that the exact probability is $0.400276$ (to six digits), so the Normal approximation is pretty good. For those interested in such things, the probability generating function of $Y$ is
$$f(x) = \left(0.55\, +0.28 x+0.15 x^2+0.01 x^3+0.01 x^4\right)^{30}$$
The probability that $Y<=20$ is the sum of the coefficients of $x^n$ for $0 \le n \le 20$ when $f(x)$ is expanded.
If you are interested in learning about generating functions in general (including probability generating functions), a number of resources can be found in the answers to this question: How Can I Learn About Generating Functions?
"x"
by italicized variable names$x$
. On the content: As Mick says, it's a large sum for which you'll probably need a computer; but for parameters like $20$ and $30$ in your example, you can get a good approximation by approximating the distribution by a Gaussian distribution with the same mean and variance. – joriki Mar 01 '20 at 09:12