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1. Colliding respiratory jets as a mechanism of air exchange and pathogen transport during conversations

   https://doi.org/10.1017/jfm.2021.915  

   Giri, Arghyanir ; Biswas, Neelakash ; Chase, Danielle L. ; Xue, Nan ; Abkarian, Manouk ; Mendez, Simon ; Saha, Sandeep ; Stone, Howard A. ( January 2022 , Journal of Fluid Mechanics)

   Air exchange between people has emerged in the COVID-19 pandemic as the important vector for transmission of the SARS-CoV-2 virus. We study the airflow and exchange between two unmasked individuals conversing face-to-face at short range, which can potentially transfer a high dose of a pathogen, because the dilution is small when compared to long-range airborne transmission. We conduct flow visualization experiments and direct numerical simulations of colliding respiratory jets mimicking the initial phase of a conversation. The evolution and dynamics of the jets are affected by the vertical offset between the mouths of the speakers. At low offsets the head-on collision of jets results in a `blocking effect', temporarily shielding the susceptible speaker from the pathogen carrying jet, although, the lateral spread of the jets is enhanced. Sufficiently large offsets prevent the interaction of the jets. At intermediate offsets (8-10 cm for 1 m separation), jet entrainment and the inhaled breath assist the transport of the pathogen-loaded saliva droplets towards the susceptible speaker's mouth. Air exchange is expected, in spite of the blocking effect arising from the interaction of the respiratory jets from the two speakers. 

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2. Some examples of privacy-preserving sharing of COVID-19 pandemic data with statistical utility evaluation

   https://doi.org/10.1186/s12874-023-01927-3  

   Liu, Fang ; Wang, Dong ; Yan, Tian ( May 2023 , BMC Medical Research Methodology)

   A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns.

   We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply.

   The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ($$\epsilon \approx 1$$$ϵ\approx 1$). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When$$\epsilon <1$$$ϵ<1$ and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints.

   Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

    

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3. Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

   https://doi.org/10.1038/s41598-022-13072-w  

   Agarwal, Khushbu ; Choudhury, Sutanay ; Tipirneni, Sindhu ; Mukherjee, Pritam ; Ham, Colby ; Tamang, Suzanne ; Baker, Matthew ; Tang, Siyi ; Kocaman, Veysel ; Gevaert, Olivier ; et al ( June 2022 , Scientific Reports)

   Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ($${\textsc {TransMED}}$$$TRANS\mathrm{MED}$) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of$${\textsc {TransMED}}$$$TRANS\mathrm{MED}$’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis.$${\textsc {TransMED}}$$$TRANS\mathrm{MED}$’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

    

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4. Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics

   https://doi.org/10.1038/s41467-023-38756-3  

   Gao, Junyi ; Heintz, Joerg ; Mack, Christina ; Glass, Lucas ; Cross, Adam ; Sun, Jimeng ( May 2023 , Nature Communications)

   In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6$${R}^{2}$$$R^2$and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts.

    

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5. Identifying a stochastic clock network with light entrainment for single cells of Neurospora crassa

   https://doi.org/10.1038/s41598-020-72213-1  

   Caranica, C. ; Al-Omari, A. ; Schüttler, H. -B. ; Arnold, J. ( September 2020 , Scientific Reports)

   Stochastic networks for the clock were identified by ensemble methods using genetic algorithms that captured the amplitude and period variation in single cell oscillators ofNeurosporacrassa. The genetic algorithms were at least an order of magnitude faster than ensemble methods using parallel tempering and appeared to provide a globally optimum solution from a random start in the initial guess of model parameters (i.e., rate constants and initial counts of molecules in a cell). The resulting goodness of fit$${x}^{2}$$$x^2$was roughly halved versus solutions produced by ensemble methods using parallel tempering, and the resulting$${x}^{2}$$$x^2$per data point was only$${\chi }^{2}/n$$${\chi }^2/n$= 2,708.05/953 = 2.84. The fitted model ensemble was robust to variation in proxies for “cell size”. The fitted neutral models without cellular communication between single cells isolated by microfluidics provided evidence for onlyoneStochastic Resonance at one common level of stochastic intracellular noise across days from 6 to 36 h of light/dark (L/D) or in a D/D experiment. When the light-driven phase synchronization was strong as measured by the Kuramoto (K), there was degradation in the single cell oscillations away from the stochastic resonance. The rate constants for the stochastic clock network are consistent with those determined on a macroscopic scale of 10⁷cells.

    

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