Why Can’t Neural Networks Forecast Pandemics Better

Why can’t neural networks (NN) forecast better? In the major super-forecasting competitions, NN have typically under-performed when compared to traditional statistical methods. When they have performed well, the underlying methods have been ensembles of NN and statistical methods. Forecasting stock markets, medical, infrastructure dynamics, social activity or pandemics each have their own challenges. In this study, we evaluate the strengths of a collection of methods for forecasting pandemics such as Covid-19 using NN, statistical methods as well as parameterized mechanistic models. Forecasts of epidemics can inform public health response and decision making, so accurate forecasting is crucial for general public notification, timing and spatial targeting of intervention. We show that NN typically under-perform in forecasting Covid-19 active cases which can be attributed to the lack of training data which is common for forecasts. Our test data consists of the top ten countries for active Covid-19 cases early in the pandemic and is represented as a Time Series (TS). We found that Statistical methods outperform NN for most cases. Albeit, NN are still good pattern finders and we suggest that there are perhaps more productive ways other than purely data driven models of using NN to help produce better forecasts

Time-Series Causality with Missing Data

Over the past years, researchers have proposed various methods to discover causal relationships among time-series data as well as algorithms to fill in missing entries in time-series data. Little to no work has been done in combining the two strategies for the purpose of learning causal relationships using unevenly sampled multivariate time-series data. In this paper, we examine how the causal parameters learnt from unevenly sampled data (with missing entries) deviates from the parameters learnt using the evenly sampled data (without missing entries). However, to obtain the causal relationship from a given time-series requires evenly sampled data, which suggests filling the missing data values before obtaining the causal parameters. Therefore, the proposed method is based on applying a Gaussian Process Regression (GPR) model for missing data recovery, followed by several pairwise Granger causality equations in Vector Autoregssive form to fit the recovered data and obtain the causal parameters. Experimental results show that the causal parameters generated by using GPR data filling offers much lower RMSE than the dummy model (fill with last seen entry) under all missing values percentage, suggesting that GPR data filling can better preserve the causal relationships when compared with dummy data filling, thus should be considered when dealing with unevenly sampled time-series causality learning

Treatment of COVID-19 with remdesivir in the absence of humoral immunity: a case report

Abstract: The response to the coronavirus disease 2019 (COVID-19) pandemic has been hampered by lack of an effective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral therapy. Here we report the use of remdesivir in a patient with COVID-19 and the prototypic genetic antibody deficiency X-linked agammaglobulinaemia (XLA). Despite evidence of complement activation and a robust T cell response, the patient developed persistent SARS-CoV-2 pneumonitis, without progressing to multi-organ involvement. This unusual clinical course is consistent with a contribution of antibodies to both viral clearance and progression to severe disease. In the absence of these confounders, we take an experimental medicine approach to examine the in vivo utility of remdesivir. Over two independent courses of treatment, we observe a temporally correlated clinical and virological response, leading to clinical resolution and viral clearance, with no evidence of acquired drug resistance. We therefore provide evidence for the antiviral efficacy of remdesivir in vivo, and its potential benefit in selected patients

Treatment of COVID-19 with remdesivir in the absence of humoral immunity: a case report

Abstract: The response to the coronavirus disease 2019 (COVID-19) pandemic has been hampered by lack of an effective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral therapy. Here we report the use of remdesivir in a patient with COVID-19 and the prototypic genetic antibody deficiency X-linked agammaglobulinaemia (XLA). Despite evidence of complement activation and a robust T cell response, the patient developed persistent SARS-CoV-2 pneumonitis, without progressing to multi-organ involvement. This unusual clinical course is consistent with a contribution of antibodies to both viral clearance and progression to severe disease. In the absence of these confounders, we take an experimental medicine approach to examine the in vivo utility of remdesivir. Over two independent courses of treatment, we observe a temporally correlated clinical and virological response, leading to clinical resolution and viral clearance, with no evidence of acquired drug resistance. We therefore provide evidence for the antiviral efficacy of remdesivir in vivo, and its potential benefit in selected patients

Towards Bayesian real-time optical flow

Optical flow is a pre-requisite for computing motion detection, time to collision, structure, focus of expansion as well as object segmentation. Unfortunately, most optical flow techniques do not provide accurate and dense measures that are useful for these types of computations. In addition, most techniques are also computationally slow. Albeit, one method proposed by Camus claims and is able to perform optical flow computations in real-time capitalizing on redundancies in the computation and spatial-temporal sampling trade-offs. It is a simple technique based on simulating various motions and computing the sum–difference of patches. The shortcoming of the Camus algorithm is that the produced field is not accurate and is arbitrary for aperture and blank wall situations. However, we show that the intermediate results from the Camus approach can be used as the factored samples for the likelihood probabilities that can be used in a Bayesian spatial and temporal propagation framework. The maximization/minimization of the likelihood is not able to differentiate arbitrary from zero flow situations. It is interesting to note that the shape of the likelihood pdf clearly identifies aperture and blank wall cases. A simple diffusion of the means of reliable flow vectors produces results that are suitable for motion detection but are inaccurate for flow determination. Similar past efforts (e.g. Singh) for flow propagation produced comparable results with a computationally more complicated propagation process. It is argued that a logic is required that takes the variances into consideration in addition to the means is required for the propagation process: first propagating spatial (to address aperture and blank wall problems) and subsequently temporal information in order to maximize the number of unimodal small variance nodes

Performing Concurrent Robot Path Execution and Computation in Real-Time

Concurrent robot path planning and execution in real-time is difficult to accomplish with existing technology. A computational framework is proposed and implemented that accomplishes this task using an existing network of computational processor resources. The tradeoffs and decisions made in order to execute this computational framework in real-time are also examined. Path planning is performed using a potential field approach. A specific type of potential function - a harmonic function - that has no local minima is used. Steepest gradient descent on this function is guaranteed to find a path to the goal if such a path exists. The implementation is parallel across a network of SPARC and SGI workstations using a message-passing software package called PVM. The computation of the plan is performed independently of and concurrently with the execution of the plan. The path planner is an integral part of a mobile robot control architecture called SPOTT which performs navigational tasks in r..