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".

[Q]: Paragraph: Viruses of the Ebolavirus genus cause sporadic epidemics of severe and systemic febrile disease that are fueled by human-to-human transmission. Despite the notoriety of ebolaviruses, particularly Ebola virus (EBOV), as prominent viral hemorrhagic fever agents, and the international concern regarding Ebola virus disease (EVD) outbreaks, very little is known about the pathophysiology of EVD in humans and, in particular, about the human immune correlates of survival and immune memory. This lack of basic knowledge about physiological characteristics of EVD is probably attributable to the dearth of clinical and laboratory data gathered from past outbreaks. The unprecedented magnitude of the EVD epidemic that occurred in West Africa from 2013 to 2016 has allowed, for the first time, evaluation of clinical, epidemiological, and immunological parameters in a significant number of patients using state-of-the-art laboratory equipment. This review will summarize the data from the literature regarding human pathophysiologic and immunologic responses to filoviral infection. 
 Title: Ebola Virus Disease in Humans: Pathophysiology and Immunity
[A]: False


[Q]: Paragraph: Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems. ½S i ðt 2 Þ þ S i ðt 6 Þ=2 and hS i ðt 2 þ 1Þi ¼ ½S i ðt 12 Þ þ S i ðt 14 Þ=2 based on the remaining strings marked in different colours (see Methods for more details). CST can be used to reconstruct the neighbouring vector X of node 2 from Y and U from Y ¼ U . X. 
 Title: ARTICLE Reconstructing propagation networks with natural diversity and identifying hidden sources
[A]: False


[Q]: Paragraph: A B S T R A C T Viral diseases exhibit spatial and temporal variation, and there are many factors that can affect their occurrence. The identification of these factors is critical in the efforts to predict and lessen viral disease burden. Because viral infection is able to spread to humans from the environment, animals, and other humans, the One-Health framework can be used to investigate the critical pathways through which viruses are transported and transmitted. A holistic approach, incorporating publicly available clinical data for human, livestock, and wildlife disease occurrence, together with environmental data reported in federal and state databases such as parameters related to land use, environmental quality, and weather, can enhance the understanding of variations in disease patterns, leading to the design and implementation of surveillance systems. An example analysis approach is presented for Michigan, United States, which is a state with large urban centers as well as a sizeable rural and agricultural population. Analysis of publicly available data from 2017 indicates that gastrointestinal (GI) and influenza-associated illnesses in Michigan may have been related with agricultural land use to a higher extent than with developed land use during that year. Meanwhile, hepatitis A virus appears to be most closely related with developed land use in dense population areas. GI illnesses may be related to precipitation, and this relationship is strongest in the springtime, although GI illnesses are most common in the winter months. Integration of human-related clinical data, animal disease data, and environmental data can ultimately be used for prioritization of the most critical locations and times for viral outbreaks in both urban and rural environments. 
 Title: Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach
[A]:
True