Hi all
This is an interesting discussion and I think Patrick is pointing to
possibilities to say for instance a given ongoing outbreak will infect x
number of people in n days if nothing is done. This is quite interesting
because telling decision makers the outbreak would claim this number of
deaths in x days if a response is not quickly in place speaks more than
just says saying there is this number of cases or deaths.
Having said that, given that we have so many priorities we need to
prioritise them. But I do believe even if it is not possible to implement
this in dhis2 now the community can learn how to do these predictive
analysis using different tools. In so doing we can gradually learn and see
how to better implement it in DHIS2.
Best
Edem
On 8 Nov 2017 20:36, "jim@dhis2.org" <jim@dhis2.org> wrote:
Hi Patrick,
Thanks, that sounds quite interesting!
I will put this back to the community: Is there work underway or upcoming
that would benefit from this type of predictive modeling? (See the
preceding email from Patrick Saunders-Hastings on this thread.)
Patrick, the reason I ask the community for more feedback is to help set
our implementation priorities. As I said, we are always interested in
learning more about how we can improve DHIS2. But also, as you might
imagine, we generally have more feature requests than time to implement
them. So we tend to be field-driven in setting our priorities. Do you know
of any specific organizations or projects that might want to use this
analysis should it be in DHIS2? To your knowledge is this used by any
countries, or CDC, or NGOs?
Another approach would be to do predictive modeling outside of DHIS2 at
first, but using DHIS2 data. The data could be exported, predictive
modelling applied, and the results imported back into DHIS2. If this were
done for example in a pilot project, we might learn from the results what
kind of integration into DHIS2 would be most beneficial.
Meanwhile, if you have readings to suggest, please post them to this list.
I would like to learn at least some more about this and maybe others would
too. (Maybe start a new email thread with a subject such as "Predictive
Modeling in DHIS2". Feel free to repeat any or all of the information you
just sent.) Thank you!
Cheers,
Jim
On Tue, Nov 7, 2017 at 10:25 AM, Patrick Saunders-Hastings < > psaunders-hastings@gevityinc.com> wrote:
Hi Jim,
Thank you very much for your quick and very helpful response. With regard
to my question about predictive modelling, this arose from one of my own
research interests in emerging disease preparedness and response planning.
My doctoral research involved the construction and implementation of a
mathematical model to chart pandemic influenza transmission against
hospital-resource capacity in Canada. However, one of the big points of
frustration in infectious disease modelling is the knowledge that
assumptions used to inform model inputs will likely not reflect the actual
parameters of the next disease outbreak. This is especially true in
lower-resource environments where such empirical research is more scarce.
This leads to uncertainty in disease estimates and response planning, and
there is currently an unmet need for predictive models that are able to
adapt to data as it becomes available in real time. This is where I see a
potential space for DHIS 2.
Put simply, the case data currently being tracked in DHIS 2 could be used
to develop increasingly accurate estimates of key disease parameters. Dates
of estimated transmission, development of symptoms and alleviation of
symptoms could together calculate latent period (time between exposure and
infection), incubation period (time between exposure and onset of clinical
symptoms), and infectious period (duration of infectiousness – could be
approximated as duration of symptoms). Meanwhile, expanded capability to
track the number of contacts with whom a given case interacts — and the
proportion of these that become infected — would allow estimation of
disease transmissibility and transmission rate.
In terms of what this would mean for predictor calculations, this would
represent a departure from relying on past data counts to detect abnormal
increases and potential outbreaks. Instead, it would allow next-generation
calculations of how incidence and prevalence of a given disease may
increase or decrease over coming days, weeks and months (as well as the
implications of these changes for resource demand). Generator calculations
could be built using estimates of disease attack rate, contact rate,
transmissibility and duration of infection (from case-based data). And most
exciting from my point of view: automated calculation functionality would
support these estimates becoming increasingly accurate and reflective of
actual disease parameters as more cases were recorded. These developments
would also represent an important step towards meeting an unmet need for
predictive modelling in lower-resource environments.
I hope that this has proved helpful, and would be happy to chat or
suggest some background reading if you would be interested in discussing
further.
Cheers,
Patrick
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