Any explanation for negative counts?

[cpsievert]

Great job pulling this data together, and many thanks for making it public!

From what I gather, most of these counts are cumulative, and if you difference them, you get a surprising number of negative values. Anyone know why? It looks like someone is aware of this issue, and censoring them to 0 --

zika/code/Plot_cases_by_country/Plot_all.R

Lines 83 to 87 in 03365a5

	if (any(confirmed_codes != "")){
	confirmed[2:length(confirmed)][confirmed[2:length(confirmed)] < 0] <- 0 }
	if (any(suspected_codes != "")){
	suspected[2:length(suspected)][suspected[2:length(suspected)] < 0] <- 0 }

BTW, I'm developing an R package that provides some more tools for exploring this data -- https://github.com/cpsievert/zikar

[dmrodz]

These counts are reported as they are from each country and they change
quite a lot over time as cases get confirmed according to data field. So
we'll always see these events.

On Aug 19, 2016 11:20 AM, "Carson" notifications@github.com wrote:

From what I gather, most of these counts are cumulative, and if you
difference them, you get a surprising number of negative values. Anyone
know why? It looks like someone is aware of this issue, and censoring them
to 0 -- https://github.com/cdcepi/zika/blob/03365a5f6649bc115a62b04c2569b9
bdd948123f/code/Plot_cases_by_country/Plot_all.R#L83-L87

BTW, I'm developing an R package that provides some more tools for
exploring this data (give it a few days before trying the explore()
function).

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[cpsievert]

Right, but I don't understand how a cumulative number of confirmed infections could be 100 one week, then 80 the next week (I'm seeing this happen in multiple countries). Could most, if not all, of these instances be explained by mis-diagnosis?

[dmrodz]

I see what you mean. Unfortunately, we can't answer that. The goal here is to accumulate the data as reported by each country.