<div dir="ltr"><div><br></div>Briefly , some researchers are trying to make the distinction between prediction and forecasting. I think this is a good point related to modeling, credibility and reproducibility. <div><br><div><br></div><div><br clear="all"><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr">***************************************************************************</div><div dir="ltr">Gilberto C. Gonzalez-Parra, <span style="color:rgb(0,0,0);font-family:"Helvetica Neue",Helvetica,Arial,"Lucida Grande",sans-serif,serif,EmojiFont;font-size:13px">Ph.D in Applied Mathematics.</span></div><div dir="ltr"><font color="#000000" face="Helvetica Neue, Helvetica, Arial, Lucida Grande, sans-serif, serif, EmojiFont"><span style="font-size:13px">Faculty of the </span></font>Mathematics Department<div><div>New Mexico Tech, NM, USA.</div></div><div>****************************************************************************</div></div></div></div></div></div></div></div></div></div><br></div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, May 17, 2021 at 4:33 AM Jacob Barhak <<a href="mailto:jacob.barhak@gmail.com">jacob.barhak@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto">Well William, <div dir="auto"><br></div><div dir="auto"><div dir="auto">Jonathan and you criticize some work done. However, in a larger perspective, let us remember that although disease models existed for about a century, this is still emerging technology. </div><div dir="auto"><br></div><div dir="auto">I have been working in the field for over a decade and I am a great critic of our current state and believe we can do better. </div><div dir="auto"><br></div><div dir="auto">Our technologies are still not good enough for prediction. We really cannot predict, moreover we still cannot fully explain the phenomena we see computationally. So every technique will have difficulties in forecasting. </div><div dir="auto"><br></div><div dir="auto">And when I write we, I also include myself. I really wish we can do better. </div><div dir="auto"><br></div><div dir="auto">This does not mean people should stop trying. So instead of trying to dismiss other methods, perhaps we should try to suggest how to learn from mistakes and improve things for the future. </div><div dir="auto"><br></div><div dir="auto">We have the collective responsibility of preparing tools for the future. And I think a positive tone in our message of what to do to improve things will do better than pointing fingers in this situation. </div><div dir="auto"><br></div><div dir="auto">I think we were able to do it ok in the paper so far, for each deficiency we were able to show a potential solution. Perhaps we should keep that concept. </div><div dir="auto"><br></div><div dir="auto">The credibility section in the paper attempts to address the issue you discuss in a subtle way. It hints that we should do better as modelers so that regulators will trust us. I think it serves the purpose you are both aiming for. Yet if you think a stronger message is needed, the paper is now open for revisions and for discussions and you are welcome to make those. </div><div dir="auto"><br></div><div dir="auto"> Jacob</div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><br></div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, May 17, 2021, 03:13 William Waites <<a href="mailto:wwaites@ieee.org" target="_blank">wwaites@ieee.org</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">> Also, to motivate the focus on credibility, it could be helpful to cite an instance where the pandemic models were wrong or where lack of credibility (trust) inhibited use of a model. For the former, one example that comes to mind is the widely cited IHME model which was substantially off in the Spring. <br>
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Another good example is Friston’s dynamic causal model which is interesting in itself and apparently a useful technique in neuroscience but does badly for infectious disease, famously leading to the assertion that the reason the model results’ divergence from reality must be do to mysterious “epidemiological dark matter”. The debunking of this sucked up a lot of time from people who know better…<br>
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