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How Can Econometrics Help Fight The Covid'19 Pandemic?, Kevin Alvarez, Vladik Kreinovich
How Can Econometrics Help Fight The Covid'19 Pandemic?, Kevin Alvarez, Vladik Kreinovich
Departmental Technical Reports (CS)
The current pandemic is difficult to model -- and thus, difficult to control. In contrast to the previous epidemics whose dynamics was smooth and well described by the existing models, the statistics of the current pandemic is highly oscillating. In this paper, we show that these oscillations can be explained if we take into account the disease's long incubation period -- as a result of which our control measures are determined by outdated data, showing number of infected people two weeks ago. To better control the pandemic, we propose to use the experience of economics, where also the effect of …
Covid-19 Peak Immunity Values Seem To Follow Lognormal Distribution, Julio Urenda, Olga Kosheleva, Vladik Kreinovich, Tonghui Wang
Covid-19 Peak Immunity Values Seem To Follow Lognormal Distribution, Julio Urenda, Olga Kosheleva, Vladik Kreinovich, Tonghui Wang
Departmental Technical Reports (CS)
For the current pandemic, an important open problem is immunity: do people who had this disease become immune against further infections? In the immunity study, it is important to know how frequent are different levels of immunity, i.e., what is the probability distribution of the immunity levels. Different people have different rates of immunity dynamics: for some, immunity gets to the level faster, for others the immunity effect is slower. Similarly, in some patients, immunity stays longer, it others, it decreases faster. In view of this, an important characteristic is peak immunity. A recent study provides some statistics on the …
Scale-Invariance-Based Pre-Processing Drastically Improves Neural Network Learning: Case Study Of Diagnosing Lung Dysfunction In Children, Nancy Avila, Julio Urenda, Nelly Gordillo, Vladik Kreinovich
Scale-Invariance-Based Pre-Processing Drastically Improves Neural Network Learning: Case Study Of Diagnosing Lung Dysfunction In Children, Nancy Avila, Julio Urenda, Nelly Gordillo, Vladik Kreinovich
Departmental Technical Reports (CS)
To adequately treat different types of lung dysfunctions in children, it is important to properly diagnose the corresponding dysfunction, and this is not an easy task. Neural networks have been trained to perform this diagnosis, but they are not perfect in diagnostics: their success rate is 60%. In this paper, we show that by selecting an appropriate invariance-based pre-processing, we can drastically improve the diagnostic success, to 100% for diagnosing the presence of a lung dysfunction.
Hifocap: An Android App For Wearable Health Devices, Yoonsik Cheon, Rodrigo A. Romero
Hifocap: An Android App For Wearable Health Devices, Yoonsik Cheon, Rodrigo A. Romero
Departmental Technical Reports (CS)
Android is becoming a platform for mobile health-care devices and apps. However, there are many challenges in developing soft real-time, health-care apps for non-dedicated mobile devices such as smartphones and tablets. In this paper we share our experiences in developing the HifoCap app, a mobile app for receiving electroencephalogram (EEG) wave samples from a wearable device, visualizing the received EEG samples, and transmitting them to a cloud storage server. The app is network and data-intensive. We describe the challenges we faced while developing the HifoCap app---e.g., ensuring the soft real-time requirement in the presence of uncertainty on the Android platform---along …