instruction:
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.
question:
Influenza viruses are a major public health problem worldwide. Although influenza has been extensively researched, there are still many aspects that are not fully understood such as the effects of within and between-hosts dynamics and their impact on behavior change. Here, we develop mathematical models with multiple infection stages and estimate parameters based on within-host data to investigate the impact of behavior change on influenza dynamics. We divide the infected population into three and four groups based on the age of the infection, which corresponds to viral load shedding. We consider within-host data on viral shedding to estimate the length and force of infection of the different infectivity stages. Our results show that behavior changes, due to exogenous events (e.g., media coverage) and disease symptoms, are effective in delaying and lowering an epidemic peak. We show that the dynamics of viral shedding and symptoms, during the infection, are key features when considering epidemic prevention strategies. This study improves our understanding of the spread of influenza virus infection in the population and provides information about the impact of emergent behavior and its connection to the within and between-hosts dynamics.
answer:
Connecting within and between-hosts dynamics in the influenza infection-staged epidemiological models with behavior change


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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


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Background: Swine-origin virus infection spreading widely could cause significant economic loss to porcine industry. Novel antiviral agents need to be developed to control this situation. Methods: In this study, we evaluated the activities of five broad-spectrum antimicrobial peptides (AMPs) against several important swine-origin pathogenic viruses by TCID 50 assay. Plaque reduction assay and cell apoptosis assay were also used to test the activity of the peptides. Protection effect of piscidin against pseudorabies virus (PRV) was also examined in mouse model. Results: Piscidin (piscidin 1), caerin (caerin 1.1) and maculatin (maculatin 1.1) could inhibit PRV by direct interaction with the virus particles in a dose-dependent manner and they could also protect the cells from PRV-induced apoptosis. Among the peptides tested, piscidin showed the strongest activity against PRV. Moreover, in vivo assay showed that piscidin can reduce the mortality of mice infected with PRV. Conclusion: In vitro and in vivo experiments indicate that piscidin has antiviral activity against PRV.
answer:

Antiviral activity of Piscidin 1 against pseudorabies virus both in vitro and in vivo