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.

Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of its observed cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimising p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimises p so that R and N estimates properly adapt to the available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems. Our approach requires some knowledge of the parameter space and exposes the similarities between renewal and skyline models.
Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models

Recent years have seen several advances in our understanding of immunity to virus infection of the lower respiratory tract, including to influenza virus infection. Here, we review the cellular targets of viruses and the features of the host immune response that are unique to the lungs. We describe the interplay between innate and adaptive immune cells in the induction, expression and control of antiviral immunity, and discuss the impact of the infected lung milieu on moulding the response of antiviral effector T cells. Recent findings on the mechanisms that underlie the increased frequency of severe pulmonary bacterial infections following respiratory virus infection are also discussed. Viruses are highly evolved and ubiquitous pathogens. Although infection with many viruses results in a mild, self-limited disease, other viruses can produce severe and frequently fatal infections. The respiratory tract is a major portal through which viruses enter to initiate infection. Whereas some viruses can initiate infection in the respiratory tract and then disseminate to other sites in the body, other viruses typically remain localized to the respiratory tract, where they replicate and induce tissue injury (see Supplementary information S1 (table)). Various defence mechanisms have evolved in the respiratory tract to prevent and control infection by viruses and other pathogens. The respiratory tract can be divided anatomically into the upper respiratory tract (including the nose, mouth and pharynx) and the lower respiratory tract (which consists of the trachea, bronchi and lungs), with the lymphoid tissue of Waldeyer's ring (including the pharyngeal and palatine tonsils) representing the line of demarcation. Because of the crucial role of the lower respiratory tract in respiration, we limit the focus of this Review to recent advances in our understanding of the host immune response to virus infection at this site.
Regulating the adaptive immune response to respiratory virus infection NIH Public Access

The clinical presentation of severe acute respiratory syndrome (SARS) resembles that of other etiologies of community-acquired pneumonia, making diagnosis difficult. Hematological and biochemical abnormalities, particularly lymphopenia, are common in patients with SARS. Methods. With the use of 2 databases, we compared the ability of the absolute lymphocyte count, absolute neutrophil count, lactate dehydrogenase level, creatine kinase level, alanine aminotransferase level, and serum calcium level at hospital admission to discriminate between cases of SARS and cases of community-acquired pneumonia. The SARS database contained data for 144 patients with SARS from the 2003 Toronto SARS outbreak. The community-acquired pneumonia database contained data for 8044 patients with community-acquired pneumonia from Edmonton, Canada. Patients from the SARS database were matched to patients from the communityacquired pneumonia database according to age, and receiver operating characteristic curves were constructed for each laboratory variable. Results. The areas under the receiver operating characteristic curves (AUCs) demonstrated fair to poor discriminatory ability for all laboratory variables tested except absolute neutrophil count, which had an AUC of 0.80, indicating good discriminatory ability (although there was no cutoff value of the absolute neutrophil count at which reasonable sensitivity or specificity could be obtained). Combinations of any 2 tests did not perform significantly better than did the absolute neutrophil count alone. Conclusions. Routine laboratory tests, including determination of absolute lymphocyte count, should not be used in the diagnosis of SARS or incorporated into current case definitions of SARS. The role of the absolute neutrophil count in SARS diagnosis is likely limited, but it should be assessed further.
Can Routine Laboratory Tests Discriminate between Severe Acute Respiratory Syndrome and Other Causes of Community-Acquired Pneumonia?