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.

Input: Consider Input: Data are limited regarding 2 new human polyomaviruses, KI polyomavirus (KIPyV) and WU polyomavirus (WUPyV), in immunocompromised patients. We used realtime PCR to test for these and 12 respiratory viruses in 2,732 nasal wash samples collected during the fi rst year after allogeneic hematopoietic cell transplantation from 222 patients. Specimens were collected weekly until day 100; then at least every 3 months. One year after hematopoietic cell transplantation, the cumulative incidence estimate was 26% for KIPyV and 8% for WUPyV. Age <20 years predicted detection of KIPyV (hazard ratio [HR] 4.6) and WUPyV (HR 4.4), and detection of a respiratory virus in the previous 2 weeks predicted KIPyV detection (HR 3.4). Sputum production and wheezing were associated with detection of KIPyV in the past week and WUPyV in the past month. There were no associations with polyomavirus detection and acute graft versus host disease, cytomegalovirus reactivation, neutropenia, lymphopenia, hospitalization, or death.

Output: WU and KI Polyomaviruses in Respiratory Samples from Allogeneic Hematopoietic Cell Transplant Recipients


Input: Consider Input: Objectives: We aimed to develop a prospective prediction tool on CrimeaneCongo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: We used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: We predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: Main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases. Ç. Ak, Clin Microbiol Infect 2020;26:123.e1e123.e7

Output: A prospective prediction tool for understanding CrimeaneCongo haemorrhagic fever dynamics in Turkey


Input: Consider Input: In China, national regulations and standards for health care waste management were implemented in 2003. To investigate the current status of health care waste management at different levels of health care facilities (HCF) after the implementation of these regulations, one tertiary hospital, one secondary hospital, and four primary health care centers from Binzhou District were visited and 145 medical staff members and 24 cleaning personnel were interviewed. Generated medical waste totaled 1.22, 0.77, and 1.17 kg/bed/day in tertiary, secondary, and primary HCF, respectively. The amount of medical waste generated in primary health care centers was much higher than that in secondary hospitals, which may be attributed to general waste being mixed with medical waste. This study found that the level of the HCF, responsibility for medical waste management in departments and wards, educational background and training experience can be factors that determine medical staff members' knowledge of health care waste management policy. Regular training programs and sufficient provision of protective measures are urgently needed to improve occupational safety for cleaning personnel. Financing and administrative monitoring by local authorities is needed to improve handling practices and the implementation of off-site centralized disposal in primary health care centers.


Output: Investigation of health care waste management in Binzhou District, China