Fundamental limitations on efficiently forecasting certain epidemic measures in network models

The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of the disease parameters to changes in the environment. We offer a rigorous explanation of the difficulty of short-term forecasting on networked populations using ideas from computational complexity. Specifically, we show that several forecasting problems (e.g., the probability that at least a given number of people will get infected at a given time and the probability that the number of infections will reach a peak at a given time) are computationally intractable. For instance, efficient solvability of such problems would imply that the number of satisfying assignments of an arbitrary Boolean formula in conjunctive normal form can be computed efficiently, violating a widely believed hypothesis in computational complexity. This intractability result holds even under the ideal situation, where all the disease parameters are known and are assumed to be insensitive to changes in the environment. From a computational complexity viewpoint, our results, which show that contagion dynamics become unpredictable for both macroscopic and individual properties, bring out some fundamental difficulties of predicting disease parameters. On the positive side, we develop efficient algorithms or approximation algorithms for restricted versions of forecasting problems.

Abstract 2217: Serum metabolic profiling identified a distinct metabolic signature in bladder cancer smokers: A key metabolic enzymes associated with patient survival

Urinary bladder cancer (BLCA) is the 9th most common malignant disease and the 13th most common cause of cancer death worldwide. Occupational exposure to carcinogens has been long associated with increased BLCA risk. Tobacco smoke contains more than 60 carcinogens causing at least 18 different types of cancer including BLCA. Reliable biomarkers for accurately predicting survival in Bladdder cancer (BLCA) smokers is lacking due to complex genomic and transcriptomic heterogeneities associated with the disease. We performed liquid chromatography-mass spectrometry (LC-MS) based targeted metabolomic analysis for >300 metabolites in serum obtained from two independent cohorts of BLCA never smokers, smokers, healthy smokers, and healthy never smokers. 40 metabolites (FDR <0.25) were identified to be differential between BLCA never smokers and smokers. Increased abundance of amino acids (tyrosine, phenylalanine, proline, serine, valine, isoleucine, glycine, asparagine) and taurine levels were observed in BLCA smokers. Integration of differential metabolomic gene signature and transcriptomics from TCGA cohort resulted in intersecting 17 gene signature that showed significant correlation with patient survival in BLCA smokers. Importantly Catechol-O-Methyltransferase (COMT), Iodotyrosine Deiodinase (IYD), and Tubulin Tyrosine Ligase (TTL) showed a significant association with patient survival in publicly available BLCA smokers datasets and did not have any clinical association in never smokers.

Note: This abstract was not presented at the meeting.

Citation Format: Chandra S. Amara, Chandrashekar R. Ambati, Venkatrao Vantaku, Piyarathna W b Danthasinghe, Sri Ramya Donepudi, Shiva S. Ravi, James M. Arnold, Vasantha Putluri, Gurkamal Chatta, Khurshid A. Guru, Hoda Badr, Martha K. Terris, Roni Bollag, Arun Sreekumar, Andrea B. Apolo, Nagireddy Putluri. Serum metabolic profiling identified a distinct metabolic signature in bladder cancer smokers: A key metabolic enzymes associated with patient survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2217.

Discovery of association rules in medical data

Data mining is a technique for discovering useful information from large databases. This technique is currently being profitably used by a number of industries. A common approach for information discovery is to identify association rules which reveal relationships among different items. In this paper, we use this approach to analyse a large database containing medical-record data. Our aim is to obtain association rules indicating relationships between procedures performed on a patient and the reported diagnoses. Random sampling was used to obtain these association rules. After reviewing the basic concepts associated with data mining, we discuss our approach for identifying association rules and report on the rules generated.