We often have some idea of how the prevalence of SAM varies throughout the year. Sometimes we have a picture (e.g. from a surveillance system) and sometimes we have to piece together a picture from multiple data sources (e.g SMART surveys, CMAM admission records, disease / food availability calendars, clinic workload returns, GMP program returns, interviews with TBAs/CHWs/THPs, &c.). This will give us a seasonal pattern and we can usually make some sort of estimate of expected prevalence for different months of the year. Here is a narrative example ... It is late February and you are about to start on a SQUEAC likelihood survey. You have built the prior and find that you need a sample size of n = 34 SAM cases for the likelihood survey. You know that the SAM prevalence peaks in July / August at about 2.4% and expect prevalence to be between about one third and one half of this in late February (i.e. you expect prevalence to be between 0.8% and 1.2%). In order to be likely to get the required sample size you should use the lower figure (p = 0.8%). You might even want to go a bit lower that this to compensate for less then 100% case-finding sensitivity. The sample size calculation is now about the number of villages you will need to sample. To find this you also need to know average village population (VPOP), the proportion of the population aged between 6 and 59 months (U5POP). If we assume that these are 760 and 18.1% respectively then the sample size calculation is: villages to sample = n / (VPOP * U5POP * p) = 34 / (760 * 0.181 * 0.008) = 30.89 Which we'd usually round up to 31 or 32 ... with 32 we might use sixteen quadrats and take two villages closest to the centre of each quadrat. In Sierra Leone you might take a systematic sample of 31 villages from a list of villages sorted by chiefdom. A good rule of thumb is that it is better to underestimate prevalence than to overestimate prevalence. I hope this helps.