In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Paragraph: Background. Precision public health is a state-of-the-art concept in public health research and its application in health care. Application of information technology in field of epidemiology paves the way to its transformation to digital epidemiology. A geospatial epidemiological model was simulated to estimate the spread of Ebola virus disease after a hypothetical outbreak in India. Methods. It was a simulation study based on SEIR (Susceptible-Exposed-Infectious-Recovered) compartmental model. Simulation was done in Spatiotemporal Epidemiological Modeler (STEM). Epidemiological profile of Ebola virus, that transmitted throughout the Sierra Leon in 2014-2016, was fitted into the SEIR deterministic compartment model designed for India. 
 Title: A Simulation Study on Hypothetical Ebola Virus Transmission in India Using Spatiotemporal Epidemiological Modeler (STEM): A Way towards Precision Public Health
False

Paragraph: 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. 
 Title: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol
True

Paragraph: Background: Severe community-acquired pneumonia (CAP) due to human adenoviruses (HAdVs) in immunocompetent adults has raised concerns. Objective: To describe the clinical, laboratorial, and radiological characteristics of adenovirus pneumonia and detect the type and diversity of human adenoviruses that caused acute respiratory distress syndrome (ARDS) in Beijing. Study design: An etiological study of adult community-acquired pneumonia was carried out prospectively at Beijing Chao-Yang Hospital. A total of 18 cases with laboratory-confirmed adenovirus infections in 487 cases with CAP were observed clinically. The viral type and phylogenetic analysis were tested by polymerase chain reaction (PCR). Results: Patients with adenovirus pneumonia typically reported flu-like symptoms. Some of them developed shortness of breath or severe dyspnea on 6 days after disease onset. The patients with ARDS usually present dyspnea, higher level of serum muscle enzymes and bilateral, mutilobal consolidation and patchy/ground-glass opacities. HAdVs type was detected in 17 samples and all of them belonged to species B (HAdV-11, 7, 3 and 14) . Among them, HAdV-11 was most frequently (10/17), followed by HAdV-7 (5/17). Phylogenetic analysis of the partial penton nucleotide confirmed a close relationship with stains circulating in the Beijing region. Conclusions: Our identification of severe respiratory illness due to adenovirus, especially type 11 may highlight the need for rapid diagnosis and improved surveillance, which may assist with targeted development of antiviral agents or type-specific vaccines. 
 Title: Severe community-acquired pneumonia caused by adenovirus type 11 in immunocompetent adults in Beijing

False