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  1. 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. 
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    Abstract Dr. Deborah Birx, the White House Coronavirus Task Force coordinator, told NBC News on “Meet the Press” that “[T]he U.S. needs a ‘breakthrough’ in coronavirus testing to help screen Americans and get a more accurate picture of the virus’ spread.” We have been involved with biopathogen detection since the 2001 anthrax attacks and were the first to detect anthrax in real-time. A variation on the laser spectroscopic techniques we developed for the rapid detection of anthrax can be applied to detect the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2 virus). In addition to detecting a single virus, this technique allows us to read its surface protein structure. In particular, we have been conducting research based on a variety of quantum optical approaches aimed at improving our ability to detect Corona Virus Disease-2019 (COVID-19) viral infection. Indeed, the detection of a small concentration of antibodies, after an infection has passed, is a challenging problem. Likewise, the early detection of disease, even before a detectible antibody population has been established, is very important. Our team is researching both aspects of this problem. The paper is written to stimulate the interest of both physical and biological scientists in this important problem. It is thus written as a combination of tutorial (review) and future work (preview). We join Prof. Federico Capasso and Editor Dennis Couwenberg in expressing our appreciation to all those working so heroically on all aspects of the COVID-19 problem. And we thank Drs. Capasso and Couwenberg for their invitation to write this paper. 
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