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# Beneficiary calculation for multiple years

This question was posted the Management of wasting/acute malnutrition forum area and has 3 replies.

### Mark Myatt

Frequent user

19 Aug 2014, 11:01

I think you could tackle this on a phase-by-phase basis in a spreadsheet. That way you can specify phase specific changes in population, coverage, and prevalence using a system of simple equations which can then be summed over years and over the program duration. The process will be a lot like working out an expected program budget or a business plan. This should give you what you want. Once you get a working model you should check the maths and the assumptions and then do a sensitivity analysis so you get some idea of the uncertainty of the results (e.g. what would happen if my coverage changed at a different rate). I usually take a sampling-base approach to sensitivity analysis this but there are other methods. I hope this is of some use.

### NDONITA Zola

Normal user

21 Jun 2019, 12:44

Hello Mark I work in an NGO and I am faced with the same problem as that expressed by my predecessor on the calculation of caseload of children from 6 to 59 months for a project of a duration of 5 taking into account the reduction of cases of acute malnutrition, the rate of growth of the population. Could you enlighten me more? I did not understand your answer, or rather it seems complicated to me. An example?

### Mark Myatt

Frequent user

24 Jun 2019, 10:26

Zola,

`    case-load = NPCK`

where N is the population, P is prevalence, C is coverage, and K is a fudge factor. We can implement this in a simple spreadsheet.

I have put an example together here.

We know that population will change over time by growth (i.e. deaths - births) and migration (exits - entries) in excess of normal growth. This needs to be accounted for. This is done in columns, B, C, and D of the example spreadsheet.

Prevalence will change. You can make informed guesses based on past results of (e.g.) SMART surveys and the example expected trend. This is in column F of the example spreadsheet.

Coverage may start low and increase over time. I have done this in G of the example spreadsheet.

This is a simple model but it allows you to do what-if type simulations. For example, you may want to see what might happen if the expected decrease in prevalence did not occur.

It is usually a good idea to create (at least) best case, expected case, and worst case scenarios.

I hope this is of some use.