Effective management of emerging and existing epidemics requires strategic decisions on where, when, and to whom interventions should be applied. However, personalized decision-making in infectious disease applications introduces new and unique statistical challenges. For instance, the individuals at risk of infection are unknown, the true outcome of interest (positive infection status) is often a latent variable, and the presence of complex dependence reduces data to a single observation. In this work, we investigate an adaptive sequential design under latent outcome structures and unspecified dependence through space and time. The statistical problem is addressed within a nonparametric model that respects the unknown dependence structure. I will begin by formalizing a treatment allocation strategy that utilizes up-to-date data to inform who is at risk of infection in real-time, with favorable theoretical properties. The optimal allocation strategy, or optimal policy, maximizes the mean latent outcome under a resource constraint. The proposed estimator learns the optimal policy over time and exploits the double-robust structure of the efficient influence function of the target parameters of interest. In the second part of the talk, I will present the study of data-adaptive inference on the mean under the optimal policy, where the target parameter adapts over time in response to the observed data (state of the epidemic). Lastly, I present a novel paradigm in nonparametric efficient estimation particularly suited for target parameters with complex dependence.
Lucia de Berk, a Dutch nurse, was arrested in 2001, and tried and
convicted of serial murder of patients in her care. At a lower court the
only hard evidence against her was the result of a probability
calculation: the chance that she was present at so many suspicious
deaths and collapses in the hospitals where she had worked was 1 in 342
million. During appeal proceedings at a higher court, the prosecution
shifted gears and gave the impression that there was now hard evidence
that she had killed one baby. Having established that she was a killer
and a liar (she claimed innocence) it was not difficult to pin another 9
deaths and collapses on her. No statistics were needed any more. In 2005
the conviction was confirmed by the supreme court. But at the same time,
some whistleblowers started getting attention from the media. A long
fight for the hearts and minds of the public, and a long fight to have
the case reopened (without any new evidence - only new scientific
interpretation of existing evidence) began and ended in 2010 with
Lucia’s complete exoneration. A number of statisticians played a big
role in that fight. The idea that the conviction was purely based on
objective scientific evidence was actually an illusion. This needed to
be explained to journalists and to the public. And the judiciary needed
to be convinced that something had to be done about it.
Lucy Letby, an English nurse, was arrested in 2020 for murder of a large number of babies at a hospital in Chester, UK, in Jan 2015-June 2016. Her trial started in 2022 and took 10 months. She was convicted and given a whole life sentence in 2023.
In my opinion, the similarities between the two cases are horrific. Again there is statistical evidence: a cluster of unexplained bad events, and Lucy was there every time; there is apparently irrefutable scientific evidence for two babies; and just like with Lucia de Berk, there are some weird personal and private writings which can be construed as a confession. For many reasons, the chances of a fair retrial for Lucy Letby are very thin indeed, but I am convinced she is innocent and that her trial was grossly unfair. I will try to convince you, too.
I predict that it will take between 6 and 12 years before she is exonerated.
You can find CSS next to the Botanical Garden, 5 minutes from Nørreport station.
Meeting room 5.2.46 is the library of the Biostatistics section, located in building 5, 2nd floor, room 46. See the map below for directions inside CSS.