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.

Example Input: The prevalence of self-reported preventive behaviors in response to an anticipated local human-to-human H5N1 transmission outbreak and factors associated with such behaviors have not been examined. A random, anonymous, cross-sectional telephone survey of 503 Hong Kong Chinese adults. The public in Hong Kong is likely to adopt self-protective behaviors (e.g., wearing face mask in public venues (73.8%), increasing the frequency of handwashing (86.7%)) and behaviors that protect others (e.g., wearing face masks when experiencing influenza-like illness (ILI, 92.4%), immediately seeking medical consultation (94.2%), making declarations when crossing the border with ILI (87.1%), complying to quarantine policies (88.3%)). Multivariate analyses indicated that factors related to age, full-time employment, perceived susceptibility, perceived efficacy of preventive measures, perceived higher fatality as compared to SARS, perceived chance of a major local outbreak, and being worried about self/family members contracting the virus were significantly associated with the inclination to adopt self-protective measures. Similar analyses showed that education level, variables related to perceived efficacy, perceived major local outbreak and such were significantly associated with various behaviors directed towards protecting others. In the event of a human-to-human H5N1 outbreak, the public in Hong Kong is likely to adopt preventive measures that may help contain the spread of the virus in the community.
Example Output: BMC Infectious Diseases Anticipated and current preventive behaviors in response to an anticipated human-to-human H5N1 epidemic in the Hong Kong Chinese general population

Example Input: SARS-CoV-2, a member of the coronavirus family, is responsible for the current COVID-19 pandemic. Based on our analysis of hepatitis C virus and coronavirus replication, and the molecular structures and activities of viral inhibitors, we previously demonstrated that three nucleotide analogues inhibit the SARS-CoV RNA-dependent RNA polymerase (RdRp). Here, using polymerase extension experiments, we have demonstrated that the active triphosphate form of Sofosbuvir (a key component of the FDA approved hepatitis C drug EPCLUSA), is incorporated by SARS-CoV-2 RdRp, and blocks further incorporation. Using the same molecular insight, we selected the active triphosphate forms of three other anti-viral agents, Alovudine, AZT (an FDA approved HIV/AIDS drug) and Tenofovir alafenamide (TAF, an FDA approved drug for HIV and hepatitis B) for evaluation as inhibitors of SARS-CoV-2 RdRp. We demonstrated the ability of these three viral polymerase inhibitors, 3'-fluoro-3'-deoxythymidine triphosphate, 3'-azido-3'deoxythymidine triphosphate and Tenofovir diphosphate (the active triphosphate forms of Alovudine, AZT and TAF, respectively) to be incorporated by SARS-CoV-2 RdRp, where they also terminate further polymerase extension. These results offer a strong molecular basis for these nucleotide analogues to be evaluated as potential therapeutics for COVID-19.
Example Output: Nucleotide Analogues as Inhibitors of SARS-CoV-2 Polymerase

Example Input: Newly available data sets present an exciting opportunity to investigate how human population movement contributes to the spread of infectious diseases across large geographical distances. It is now possible to construct realistic models of infectious disease dynamics for the purposes of understanding global-scale epidemics. Nevertheless, a remaining unanswered question is how best to leverage the new data to parameterize models of movement, and whether one's choice of movement model impacts modeled disease outcomes. We adapt three well-studied models of infectious disease dynamics, the SIR model; the SIS model; and the Ross-Macdonald model, to incorporate either of two candidate movement models. We describe the effect that the choice of movement model has on each disease model's results, finding that in all cases there are parameter regimes where choosing one movement model instead of another has a profound impact on epidemiological outcomes. We further demonstrate the importance of choosing an appropriate movement model using the applied case of malaria transmission and importation on Bioko Island, Equatorial Guinea, finding that one model produces intelligible predictions of R 0 while the other produces nonsensical results. Mathematical modeling | Infectious disease dynamics | Human population movement | Malaria DT Citron developed the project and carried out the analysis. DL Smith provided guidance and context for the project. CA Guerra provided the data for building the Bioko Island model. SL Wu, JM Henry, HM Sánchez C, and D Dolgert discussed and reviewed the analysis.
Example Output:
Comparing Metapopulation Dynamics of Infectious Diseases under Di erent Models of Human Movement