If you don't see copy number variation analysis mentioned in the DESeq / DESeq2 manual, then don't use it for that purpose. The data distribution of your CNV data will not match that expected by DESeq (expects a negative binomial distribution). CNV data is measured as discrete intervals, so, something like a Hidden Markov Model (HMM) is more commonly employed (although it can be measured on a continuous scale too).
Note that the same question was asked in relation to edgeR and CNV on the Bioconductor forum: Question: edgeR for CNV detection
Also, take a look at this other Biostars question: Copy Number Variation from paired end RNA-Seq data
Note, in particular Devon's reply, where he alludes to the "fundamental limitation" of trying to detect CNV from RNA-seq. This limitation relates to the fact that a copy number event does not necessarily alter gene expression levels. A gene could easily be duplicated, for example, but, without the promoter sequence and/or transcription start site (TSS), it will not be expressed (or just expressed at negligible levels).
If you can't afford to whole genome sequence, then the Affymetrix SNP 6.0 array can determine genome-wide CNV profiles, along with genotyping SNPs. I used this in my PhD years ago.
Best of luck,