TASK DEFINITION: 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.
PROBLEM: A novel coronavirus, termed the severe acute respiratory syndrome coronavirus (SARS-CoV), infected humans in Guangdong, China, in November 2002 and the subsequent efficient human-to-human transmissions of this virus caused profound disturbances in over 30 countries worldwide in 2003. Eventually, this epidemic was controlled by isolation and there has been no human infection reported since January 2004. However, research on different aspects of the SARS-CoV is not waning, as it is not known if this virus will re-emerge, especially since its origins and potential reservoir(s) are unresolved. The SARS-CoV genome is nearly 30 kb in length and contains 14 potential open reading frames (ORFs). Some of these ORFs encode for genes that are homologous to proteins found in all known coronaviruses, namely the replicase genes (ORFs 1a and 1b) and the four structural proteins: nucleocapsid, spike, membrane and envelope, and these proteins are expected to be essential for the replication of the virus. The remaining eight ORFs encodes for accessory proteins, varying in length from 39 to 274 amino acids, which are unique to SARS-CoV. This review will summarize the expeditious research on these accessory viral proteins in three major areas: (i) the detection of antibodies against accessory proteins in the serum of infected patients, (ii) the expression, processing and cellular localization of the accessory proteins, and (iii) the effects of the accessory proteins on cellular functions. These in-depth molecular and biochemical characterizations of the SARS-CoV accessory proteins, which have no homologues in other coronaviruses, may offer clues as to why the SARS-CoV causes such a severe and rapid attack in humans, while other coronaviruses that infect humans seem to be more forgiving.

SOLUTION: Understanding the accessory viral proteins unique to the severe acute respiratory syndrome (SARS) coronavirus

PROBLEM: Community-ARI and inpatient-ARI was also combined with mild and severe cases of influenza from a historical prospective study as mild-ARI and severe-ARI respectively to evaluate the performance of clinical case definitions. We analysed 130 community-ARI and 140 inpatient-ARI episodes (5 inpatient-ARI excluded because multiple pathogens were detected), involving 138 and 207 samples respectively. Detection by PCR declined with days post-onset for influenza virus; decrease was faster for community-ARI than for inpatient-ARI. No such patterns were observed for noninfluenza respiratory virus infections. PathChip added substantially to viruses detected for community-ARI only. Clinical case definitions discriminated influenza from other mild-ARI but performed poorly for severe-ARI and for older participants. Rational strategies for diagnosis and surveillance of influenza and other respiratory virus must acknowledge the differences between ARIs presenting in community and hospital settings.

SOLUTION: Detection of viral respiratory pathogens in mild and severe acute respiratory infections in Singapore To investigate the performance of laboratory methods and clinical case definitions in detecting the viral pathogens for acute respiratory infections (ARIs) from a prospective community cohort and hospital inpatients, nasopharyngeal swabs from cohort members reporting ARIs (community-ARI) and inpatients admitted with ARIs (inpatient-ARI) were tested by Singleplex Real Time-Polymerase Chain Reaction (SRT-PCR), multiplex RT-PCR (MRT-PCR) and pathogen-chip system (PathChip) between OPEN

PROBLEM: Background: Mathematical and statistical models are used to project the future time course of infectious disease epidemics and the expected future burden on health care systems and economies. Influenza is a particularly important disease in this context because it causes annual epidemics and occasional pandemics. In order to forecast health care utilization during epidemics-and the effects of hospitalizations and deaths on the contact network and, in turn, on transmission dynamics-modellers must make assumptions about the lengths of time between infection, visiting a physician, being admitted to hospital, leaving hospital, and death. More reliable forecasts could be be made if the distributions of times between these types of events ("delay distributions") were known. Methods: We estimated delay distributions in the province of Ontario, Canada, between 2006 and 2010. To do so, we used encrypted health insurance numbers to link 1.34 billion health care billing records to 4.27 million hospital inpatient stays. Because the four year period we studied included three typical influenza seasons and the 2009 influenza pandemic, we were able to compare the delay distributions in non-pandemic and pandemic settings. We also estimated conditional probabilities such as the probability of hospitalization within the year if diagnosed with influenza. Results: In non-pandemic [pandemic] years, delay distribution medians (inter-quartile ranges) were: Service to Admission 6.3 days (0.1-17.6 days) [2.4 days (-0.3-13.6 days)], Admission to Discharge 3 days (1.4-5.9 days) [2.6 days (1.2-5.1 days)], Admission to Death 5.3 days (2.1-11 days) [6 days (2.6-13.1 days)]. (Service date is defined as the date, within the year, of the first health care billing that included a diagnostic code for influenza-like-illness.) Among individuals diagnosed with either pneumonia or influenza in a given year, 19% [16%] were hospitalized within the year and 3% [2%] died in hospital. Among all individuals who were hospitalized, 10% [12%] were diagnosed with pneumonia or influenza during the year and 5% [5%] died in hospital. Conclusion: Our empirical delay distributions and conditional probabilities should help facilitate more accurate forecasts in the future, including improved predictions of hospital bed demands during influenza outbreaks, and the expected effects of hospitalizations on epidemic dynamics.

SOLUTION:
Patterns of seasonal and pandemic influenza-associated health care and mortality in Ontario, Canada