Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data.

Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden.

Model for Interpreting Discordant SARS-CoV-2 Diagnostic Test Results.

We devised a model to interpret discordant SARS-CoV-2 test results. We estimate that, during March 2020-May 2022, a patient in the United States who received a positive rapid antigen test result followed by a negative nucleic acid test result had only a 15.4% (95% CI 0.6%-56.7%) chance of being infected.

Real time monitoring of COVID-19 intervention effectiveness through contact tracing data.

Communities worldwide have used vaccines and facemasks to mitigate the COVID-19 pandemic. When an individual opts to vaccinate or wear a mask, they may lower their own risk of becoming infected as well as the risk that they pose to others while infected. The first benefit-reducing susceptibility-has been established across multiple studies, while the second-reducing infectivity-is less well understood. Using a new statistical method, we estimate the efficacy of vaccines and facemasks at reducing both types of risks from contact tracing data collected in an urban setting. We find that vaccination reduced the risk of onward transmission by 40.7% [95% CI 25.8-53.2%] during the Delta wave and 31.0% [95% CI 19.4-40.9%] during the Omicron wave and that mask wearing reduced the risk of infection by 64.2% [95% CI 5.8-77.3%] during the Omicron wave. By harnessing commonly-collected contact tracing data, the approach can broadly provide timely and actionable estimates of intervention efficacy against a rapidly evolving pathogen.

Disproportionate impacts of COVID-19 in a large US city.

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

Thermal transport in kinked nanowires through simulation.

The thermal conductance of nanowires is an oft-explored quantity, but its dependence on the nanowire shape is not completely understood. The behaviour of the conductance is examined as kinks of varying angular intensity are included into nanowires. The effects on thermal transport are evaluated through molecular dynamics simulations, phonon Monte Carlo simulations and classical solutions of the Fourier equation. A detailed look is taken at the nature of heat flux within said systems. The effects of the kink angle are found to be complex, influenced by multiple factors including crystal orientation, details of transport modelling, and the ratio of mean free path to characteristic system lengths. The effect of varying phonon reflection specularity on the heat flux is also examined. It is found that, in general, the flow of heat through systems simulated through phonon Monte Carlo methods is concentrated into a channel smaller than the wire dimensions, while this is not the case in the classical solutions of the Fourier model.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

Thymosin beta 4 prevents systemic lipopolysaccharide-induced plaque load in middle-age APP/PS1 mice.

Lipopolysaccharide (LPS) produced by the gut during systemic infections and inflammation is thought to contribute to Alzheimer's disease (AD) progression. Since thymosin beta 4 (Tß4) effectively reduces LPS-induced inflammation in sepsis, we tested its potential to alleviate the impact of LPS in the brain of the APPswePS1dE9 mouse model of AD (APP/PS1) and wildtype (WT) mice. 12.5-month-old male APP/PS1 mice (n = 30) and their WT littermates (n = 29) were tested for baseline food burrowing performance, spatial working memory and exploratory drive in the spontaneous alternation and open-field tests, prior to being challenged with LPS (100ug/kg, i.v.) or its vehicle phosphate buffered saline (PBS). Tß4 (5 mg/kg, i.v.) or PBS, was administered immediately following and at 2 and 4 h after the PBS or LPS challenge, and then once daily for 6 days (n = 7-8). LPS-induced sickness was assessed though monitoring of changes in body weight and behaviour over a 7-day period. Brains were collected for the determination of amyloid plaque load and reactive gliosis in the hippocampus and cortex. Treatment with Tß4 alleviated sickness symptoms to a greater extent in APP/PS1 than in WT mice by limiting LPS-induced weight loss and inhibition of food burrowing behaviour. It prevented LPS-induced amyloid burden in APP/PS1 mice but increased astrocytic and microglial proliferation in the hippocampus of LPS-treated WT mice. These data show that Tß4 can alleviate the adverse effects of systemic LPS in the brain by preventing exacerbation of amyloid deposition in AD mice and by inducing reactive microgliosis in aging WT mice.

The risk of SARS-CoV-2 Omicron variant emergence in low and middle-income countries (LMICs).

We estimated the probability of undetected emergence of the SARS-CoV-2 Omicron variant in 25 low and middle-income countries (LMICs) prior to December 5, 2021. In nine countries, the risk exceeds 50 %; in Turkey, Pakistan and the Philippines, it exceeds 99 %. Risks are generally lower in the Americas than Europe or Asia.

Modular light sources for microscopy and beyond (ModLight).

Modular light (ModLight) sources can be integrated into complex systems for microscopy, medical imaging, remote sensing, and many more. Motivated by the need for affordable and open-access alternatives that are globally relevant, we have designed and presented light devices that use simple, off-the-shelf components. Red, green, blue, white and near-infrared LEDs are combined using mirrors and X-Cube prisms in novel devices. This modular nature allows portability and mounting flexibility. The ModLight suite can be used with any optical system that requires single- or multi-wavelength illumination such as bright-field and epifluorescence microscopes.

Computational version of the correlation light-field camera.

Light-field cameras allow the acquisition of both the spatial and angular components of the light-field. The conventional way to perform such acquisitions leads to a strong spatio-angular resolution limitation but correlation-enabled plenoptic cameras have been introduced recently that relax this constraint. Here we use a computational version of this concept to acquire realistic light-fields images using a commercial DSLR Camera lens as an imaging system. By placing the image sensor in the focal plane of a lens, within the camera we ensure the acquisition of pure angular components together with the spatial information. We perform an acquisition presenting a high spatio-angular rays resolution obtained through a trade off of the temporal resolution. The acquisition reported is photo-realistic and the acquisition of diffraction limited features is observed with the setup. Finally, we demonstrate the refocusing abilities of the camera.

Simulated assessment of light transport through ischaemic skin flaps.

Currently, free flaps and pedicled flaps are assessed for reperfusion in postoperative care using colour, capillary refill, temperature, texture, and Doppler signal (if available). While these techniques are effective, they are prone to error due to their qualitative nature. In this research, different wavelengths of light were used to quantify the response of ischaemic tissue. The assessment provides indicators that are key to developing a point-of-care diagnostic device that is capable of observing reduced perfusion quantitatively. Detailed optical models of the layers of the skin were set up and appropriate optical properties assigned, with due consideration of melanin and haemoglobin concentration. A total of 24 models of healthy, perfused and perfusion-deprived tissue were used to assess the responses when illuminated with visible and near-infrared wavelengths of light. In addition to detailed fluence maps of photon propagation, a simple mathematical model is proposed to assess the differential propagation of photons in tissue; the optical reperfusion factor (ORF). The results show clear advantages of using light at longer wavelengths (red, near-infrared) and the inferences drawn from the simulations hold significant clinical relevance. The simulated scenarios and results consolidate the belief in a multi-wavelength, point-of-care diagnostic device, and inform its design to quantify blood flow in transplanted tissue. The modelling approach is applicable beyond the current research and can be used to investigate other medical conditions in the skin that can be mathematically represented. Through these, additional inferences and approaches to other point-of-care devices can be realised.

i-RheoFT: Fourier transforming sampled functions without artefacts.

In this article we present a new open-access code named "i-RheoFT" that implements the analytical method first introduced in [PRE, 80, 012501 (2009)] and then enhanced in [New J Phys 14, 115032 (2012)], which allows to evaluate the Fourier transform of any generic time-dependent function that vanishes for negative times, sampled at a finite set of data points that extend over a finite range, and need not be equally spaced. I-RheoFT has been employed here to investigate three important experimental factors: (i) the 'density of initial experimental points' describing the sampled function, (ii) the interpolation function used to perform the "virtual oversampling" procedure introduced in [New J Phys 14, 115032 (2012)], and (iii) the detrimental effect of noises on the expected outcomes. We demonstrate that, at relatively high signal-to-noise ratios and density of initial experimental points, all three built-in MATLAB interpolation functions employed in this work (i.e., Spline, Makima and PCHIP) perform well in recovering the information embedded within the original sampled function; with the Spline function performing best. Whereas, by reducing either the number of initial data points or the signal-to-noise ratio, there exists a threshold below which all three functions perform poorly; with the worst performance given by the Spline function in both the cases and the least worst by the PCHIP function at low density of initial data points and by the Makima function at relatively low signal-to-noise ratios. We envisage that i-RheoFT will be of particular interest and use to all those studies where sampled or time-averaged functions, often defined by a discrete set of data points within a finite time-window, are exploited to gain new insights on the systems' dynamics.

Aggregating predictions from experts: a review of statistical methods, experiments, and applications.

Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts-models that combine expert-generated predictions into a single forecast-can make predictions when training data is limited by relying on human intuition. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model. This review surveyed recent literature on aggregating expert-elicited predictions. We gathered common terminology, aggregation methods, and forecasting performance metrics, and offer guidance to strengthen future work that is growing at an accelerated pace.

Improving probabilistic infectious disease forecasting through coherence.

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system's geographical hierarchy.

Realizing new designs of multiplexed electrode chips by 3-D printed masks.

Creating small and portable analytical methods is a fast-growing field of research. Devices capable of performing bio-analytical detection are especially desirable with the onset of the global pandemic. Lab-on-a-chip (LOC) technologies, including rapid point-of-care (POC) devices such as glucose sensors, are attractive for applications in resource-poor settings. There are many challenges in creating such devices, from sensitive molecular designs to stable conditions for storing the sensor chips. In this study we have explored using three-dimensional (3D) printing to create shadow masks as a low-cost method to produce multiplexed electrodes by physical vapour deposition. Although the dimensional resolution of the electrodes produced by using 3D printed masks is inferior to those made through photolithography-based techniques, their dimensions can be readily tailored ranging from 1 mm to 3 mm. Multiple mask materials were tested, such as polylactic acid and polyethylene terephthalate glycol, with acrylonitrile butadiene styrene shown to be the best. Simple strategies in making chip holders by 3D printing and controlling working electrode surface area with epoxy glue were also investigated. The prepared chips were tested by performing surface chemistry with thiol-containing molecules and monitoring the signals electrochemically.

Single-pixel imaging 12 years on: a review.

Modern cameras typically use an array of millions of detector pixels to capture images. By contrast, single-pixel cameras use a sequence of mask patterns to filter the scene along with the corresponding measurements of the transmitted intensity which is recorded using a single-pixel detector. This review considers the development of single-pixel cameras from the seminal work of Duarte et al. up to the present state of the art. We cover the variety of hardware configurations, design of mask patterns and the associated reconstruction algorithms, many of which relate to the field of compressed sensing and, more recently, machine learning. Overall, single-pixel cameras lend themselves to imaging at non-visible wavelengths and with precise timing or depth resolution. We discuss the suitability of single-pixel cameras for different application areas, including infrared imaging and 3D situation awareness for autonomous vehicles.

Developing a portable gas imaging camera using highly tunable active-illumination and computer vision.

We have developed a portable gas imaging camera for identifying methane leaks in real-time. The camera uses active illumination from distributed feedback InGaAs laser diodes tuned to the 1653 nm methane absorption band. An InGaAs focal plane sensor array images the active illumination. The lasers are driven off resonance every alternate frame so that computer vision can extract the gas data. A colour image is captured simultaneously and the data is superimposed to guide the operator. Image stabilisation has been employed to allow detection with a moving camera, successfully imaging leaks from mains pressure gas supplies at a range of up to 3 m and flow rates as low as 0.05 L min-1.

What Caging Force Cells Feel in 3D Hydrogels: A Rheological Perspective.

It has been established that the mechanical properties of hydrogels control the fate of (stem) cells. However, despite its importance, a one-to-one correspondence between gels' stiffness and cell behavior is still missing from literature. In this work, the viscoelastic properties of poly(ethylene-glycol) (PEG)-based hydrogels are investigated by means of rheological measurements performed at different length scales. The outcomes of this work reveal that PEG-based hydrogels show significant stiffening when subjected to a compressional deformation, implying that conventional bulk rheology measurements may overestimate the stiffness of hydrogels by up to an order of magnitude. It is hypothesized that this apparent stiffening is caused by an induced "tensional state" of the gel network, due to the application of a compressional normal force during sample loading. Moreover, it is shown that the actual stiffness of the hydrogels is instead accurately determined by means of both passive-video-particle-tracking (PVPT) microrheology and nanoindentation measurements, which are inherently performed at the cell's length scale and in absence of any externally applied force in the case of PVPT. These results underpin a methodology for measuring hydrogels' linear viscoelastic properties that are representative of the mechanical constraints perceived by cells in 3D hydrogel cultures.

Dual-band single-pixel telescope.

Single-pixel imaging systems can obtain images from a wide range of wavelengths at low-cost compared to those using conventional multi-pixel, focal-plane array sensors, especially at wavelengths outside the visible spectrum. The ability to sense short-wave infrared radiation with single-pixel techniques extends imaging capability to adverse weather conditions and environments, such as fog, haze, or night time. In this work, we demonstrate a dual-band single-pixel telescope for imaging at both visible (VIS) and short-wave infrared (SWIR) spectral regions simultaneously under some of these outdoor weather conditions. At 64 × 64 pixel-resolution, our system has achieved continuous VIS and SWIR imaging of various objects at a frame rate up to 2.4 Hz. Visual and contrast comparison between the reconstructed VIS and SWIR images emphasizes the significant contribution of infrared observation using the single-pixel technique. The single-pixel telescope provides an alternative cost-effective imaging solution for synchronized dual-waveband optical applications.

Open sessile droplet viscometer with low sample consumption.

This paper reports a portable viscometer that requires less than 10 µL of sample for a measurement. Using a two-droplet Laplace-induced pumping system on an open microfluidic substrate, the device measures the viscosity of a liquid by determining the time required for one droplet to completely pump into a second droplet. The pumping behaviour follows the Hagen-Poiseuille and Laplace relations where the flow rate, Q, is proportional to the liquid's kinematic viscosity, µ. The progress of pumping is measured by tracking the change in curvature of one of the droplets using a laser that is positioned perpendicular to the microfluidic chip and directed at the "tail" of the shrinking droplet. The angle of incidence and degree of refraction changes depending on the size of the droplet, which is tracked by a linear diode array placed beneath the microfluidic chip. Droplet reservoirs and connecting channels were defined by precise patterning of a glass substrate coated with a commercially available omniphobic coating (Ultra Ever Dry®) using laser micromachining. A 500 µm wide and 20 mm long channel with circular reservoirs (d = 1.5 mm) enabled the measurement of dynamic viscosities in the range of η = 1.0-2.87 mPa s. The materials cost for the entire viscometer (fluidics and electronics, etc.) is <15 USD.