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 Q]: C57 strain mice were inoculated intraperitoneally with denatured mouse hepatitis virus strain 3 particles and virus surface projection, membrane and ribonucleoprotein subeomponents, obtained from detergent treated purified virus preparations. All immunised animals developed high levels of serum antibody directed against the respective antigens, detectable by enzyme-linked immunosorbent assay. Mice that had been immunised with denatured virus particles or surface projections were protected against infection with mouse hepatitis virus strain 3, whereas immunisation with virus membrane or ribonucleoprotein subcomponents failed to protect mice against virus challenge.
[EX A]: Archives of Virology Antigenieity of Mouse Hepatitis Virus Strain 3 Subeomponents in C57 Strain Miee

[EX Q]: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.
[EX A]: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

[EX Q]: The global spread of Coronavirus Disease 2019 (COVID-19) is overwhelming many health-care systems. As a result, epidemiological models are being used to inform policy on how to effectively deal with this pandemic. We note that the majority of existing models do not take into account differences in the amount of interactions between individuals (i.e. the underlying human interaction network). Using network science we demonstrate how this network of interactions can be used to predict the spread of the virus and to inform policy on the most successful mitigation and suppression strategies. Although applicable to disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models. We provide commented source code for all our analyses so that they can easily be integrated into current and future epidemiological models.
[EX A]:
Using network science to propose strategies for effectively dealing with pandemics: The COVID-19 example