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.
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Question: medRxiv preprint Interpretation Parameters that successfully reproduce the observed number of cases indicate that both R 0 and the prevalence of the virus might be underestimated. This is in concordance with the newest research on undocumented COVID-19 cases. Consequently, the actual mortality rate is putatively lower than estimated. Confirmation of the pandemic characteristic by further refinement of the model and screening tests is crucial for developing an effective strategy for the global epidemiological crisis. 1 Balcan D, Gonçalves B, Hu H, Ramasco JJ, Colizza V, Vespignani A. Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model. J Comput Sci 2010; 1 : 132-45. 2 Van den Broeck W, Gioannini C, Gonçalves B, Quaggiotto M, Colizza V, Vespignani A. The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect Dis 2011; 11 : 37. 3 Tizzoni M, Bajardi P, Poletto C, et al. Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm. BMC Med 2012;

Answer: From a single host to global spread. The global mobility based modelling of the COVID-19 pandemic implies higher infection and lower detection rates than current estimates


Question: 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

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


Question: The interferon-inducible transmembrane proteins (IFITMs) are a family of small transmembrane proteins belonging to the interferon (IFN)-stimulated gene (ISG) superfamily and strongly induced by IFNs. In this paper, we studied the expression profile of IFITMs in 32 organ tissues. The IFITM mRNA expression profile showed that IFITM1, IFITM2 and IFITM3 were expressed in each tissue, especially, in spermatophore, spermaduct, testicle and epididymis. The expression of IFITM1, IFITM2 and IFITM3 showed a trend from high to low. Except for IFITM3 and IFITM6, the others IFITMs were highly expressed in the bone marrow, and the expression level of them was higher in the tibia than that in other parts of the long bones. In liver, the relative expression of IFITM1 and IFITM3 was higher than that of other members. The expression level of IFITM5 was the highest in bone marrow, successively in pancreas, and it was low in skin, smooth muscle and fat. Interestingly, the expression profile of IFITM2 and IFITM7 in tissues was similar to IFITM5. The expression of IFITM2, IFITM5 and IFITM10 were higher in smooth muscle than that in skeletal muscle. IFITM2, IFITM5, IFITM7 and IFITM10 were both highly expressed in esophagus and trachea. In addition, the expression of IFITM6 in eyes was high, and also in pancreas, gallbladder and bone. In the present study, we systematically analyzed the mRNA expression profile of IFITMs in 32 organ tissues, providing the foundation for the study of the function of the IFITMs.

Answer:
Intractable & Rare Diseases Research