In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Ex Input:
The recent outbreak of the human Zaire ebolavirus (EBOV) epidemic is spiraling out of control in West Africa. Human EBOV hemorrhagic fever has a case fatality rate of up to 90%. The EBOV is classified as a biosafety level 4 pathogen and is considered a category A agent of bioterrorism by Centers for Disease Control and Prevention, with no approved therapies and vaccines available for its treatment apart from supportive care. Although several promising therapeutic agents and vaccines against EBOV are undergoing the Phase I human trial, the current epidemic might be outpacing the speed at which drugs and vaccines can be produced. Like all viruses, the EBOV largely relies on host cell factors and physiological processes for its entry, replication, and egress. We have reviewed currently available therapeutic agents that have been shown to be effective in suppressing the proliferation of the EBOV in cell cultures or animal studies. Most of the therapeutic agents in this review are directed against non-mutable targets of the host, which is independent of viral mutation. These medications are approved by the Food and Drug Administration (FDA) for the treatment of other diseases. They are available and stockpileable for immediate use. They may also have a complementary role to those therapeutic agents under development that are directed against the mutable targets of the EBOV.

Ex Output:
Human Ebola virus infection in West Africa: a review of available therapeutic agents that target different steps of the life cycle of Ebola virus-mutable host cell therapeutic targets for Ebola virus, Cocktail therapeutic intervention for RNA virus Multilingual abstract


Ex Input:
The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naïve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th , 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.

Ex Output:
An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)


Ex Input:
Early warning systems for outbreaks of infectious diseases are an important application of the ecological theory of epidemics. A key variable predicted by early warning systems is the final outbreak size. However, for directly transmitted diseases, the stochastic contact process by which outbreaks develop entails fundamental limits to the precision with which the final size can be predicted. I studied how the expected final outbreak size and the coefficient of variation in the final size of outbreaks scale with control effectiveness and the rate of infectious contacts in the simple stochastic epidemic. As examples, I parameterized this model with data on observed ranges for the basic reproductive ratio (R 0 ) of nine directly transmitted diseases. I also present results from a new model, the simple stochastic epidemic with delayed-onset intervention, in which an initially supercritical outbreak (R 0 . 1) is brought under control after a delay. The coefficient of variation of final outbreak size in the subcritical case (R 0 , 1) will be greater than one for any outbreak in which the removal rate is less than approximately 2.41 times the rate of infectious contacts, implying that for many transmissible diseases precise forecasts of the final outbreak size will be unattainable. In the delayed-onset model, the coefficient of variation (CV) was generally large (CV . 1) and increased with the delay between the start of the epidemic and intervention, and with the average outbreak size. These results suggest that early warning systems for infectious diseases should not focus exclusively on predicting outbreak size but should consider other characteristics of outbreaks such as the timing of disease emergence.

Ex Output:

Limits to Forecasting Precision for Outbreaks of Directly Transmitted Diseases