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.

Example Input: Human bocavirus (HBoV) was recently discovered in children with respiratory distress and/or diarrhea. To our knowledge, no previous study has reported the existence of bocavirus in Saudi Arabia. Swabs samples from 80 children with respiratory tract infections were examined for the presence of HBoV. Real-time polymerase chain reaction was used as a sensitive method to detect the HBoV. Direct gene sequencing was used to determine the genotype of the detected virus isolates. HBoV was detected in 22.5% of the examined patients. The NP1 partial gene sequence from all patients showed that the circulated strains were related to HBoV-1 genotype. Most of HBoV infected patients showed evidence of mixed coinfection with other viral pathogens. The current study clearly demonstrated that genetically conserved HBoV1 circulates in Saudi Arabia. Interestingly, most of the HBoV1 infected cases were associated with high rates of co-infections with other viruses.
Example Output: Detection of Bocavirus in Children Suffering from Acute Respiratory Tract Infections in Saudi Arabia

Example Input: Aims: To determine the seroprevalence of canine respiratory coronavirus (CRCoV) in New Zealand dogs, and to explore associations with age, sex, breed, month, and geographical region of sampling and reported presence of clinical signs suggestive of respiratory disease. Methods: A total of 1,015 canine serum samples were randomly selected from submissions to a diagnostic laboratory between March and December 2014, and were analysed for CRCoV antibodies using a competitive ELISA. Logistic regression analysis was used to determine associations between seroprevalence of CRCoV and breed category, age, sex, sampling month, region, and reported health status of dogs. Results: Overall, 538/1,015 (53.0%) samples were seropositive for CRCoV, with 492/921 (53.4%) positive dogs in the North Island and 46/94 (49%) in the South Island. Age of dog, sampling month, region, and presence of abnormal respiratory signs were included in the initial logistic regression model. Seroprevalence was higher in dogs aged ≥3 compared with ≤2 years (p < 0.01). The lowest seroprevalence was observed in July (30/105; 28.5%) and August (32/100; 32%), and the highest in June (74/100; 74%). Seroprevalence in dogs from Auckland was higher than in dogs from the Hawkes Bay, Manawatu, Marlborough, and Waikato regions (p < 0.05). Abnormal respiratory signs (coughing, nasal discharge, or sneezing) were reported for 28/1,015 (2.8%) dogs sampled. Seroprevalence for CRCoV tended to be higher among dogs with respiratory signs (67.9 (95% CI = 47.6-83.4)%) than dogs with no reported respiratory signs (52.6 (95% CI = 49.5-55.7)%). Conclusions: Serological evidence of infection with CRCoV was present in more than half of the dogs tested from throughout New Zealand. Differences in CRCoV seroprevalence between regions and lack of seasonal pattern indicate that factors other than external temperatures may be important in the epidemiology of CRCoV in New Zealand. Clinical relevance: Our data suggest that CRCoV should be included in investigations of cases of infectious canine tracheobronchitis, particularly if these occur among dogs vaccinated with current vaccines, which do not include CRCoV antigens.
Example Output: A serological survey of canine respiratory coronavirus in New Zealand

Example Input: Background: The Middle East respiratory syndrome (MERS) associated coronavirus has been imported via travelers into multiple countries around the world. In order to support risk assessment practice, the present study aimed to devise a novel statistical model to quantify the country-level risk of experiencing an importation of MERS case. Methods: We analyzed the arrival time of each reported MERS importation around the world, i.e., the date on which imported cases entered a specific country, which was modeled as a dependent variable in our analysis. We also used openly accessible data including the airline transportation network to parameterize a hazard-based risk prediction model. The hazard was assumed to follow an inverse function of the effective distance (i.e., the minimum effective length of a path from origin to destination), which was calculated from the airline transportation data, from Saudi Arabia to each country. Both country-specific religion and the incidence data of MERS in Saudi Arabia were used to improve our model prediction. Results: Our estimates of the risk of MERS importation appeared to be right skewed, which facilitated the visual identification of countries at highest risk of MERS importations in the right tail of the distribution. The simplest model that relied solely on the effective distance yielded the best predictive performance (Area under the curve (AUC) = 0. 943) with 100 % sensitivity and 79.6 % specificity. Out of the 30 countries estimated to be at highest risk of MERS case importation, 17 countries (56.7 %) have already reported at least one importation of MERS. Although model fit measured by Akaike Information Criterion (AIC) was improved by including country-specific religion (i.e. Muslim majority country), the predictive performance as measured by AUC was not improved after accounting for this covariate. Conclusions: Our relatively simple statistical model based on the effective distance derived from the airline transportation network data was found to help predicting the risk of importing MERS at the country level. The successful application of the effective distance model to predict MERS importations, particularly when computationally intensive large-scale transmission models may not be immediately applicable could have been benefited from the particularly low transmissibility of the MERS coronavirus.
Example Output:
Predicting the international spread of Middle East respiratory syndrome (MERS)