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:
Sato, K., Inaba, Y., Kurogi, H., Takahashi, E., Satoda, K., Omori, T. and Matumoto, M.t 1977. Hemagglutination by calf diarrhea coronavirus. Vet. Microbiol., 2: 83--87. The virus was grown in BEK-1 cells, a stable cell line from bovine embryo kidney, and tested for hemagglutinafion (HA) with erythrocytes of a variety of species at 4°C, room temperature and 37°C. HA was observed at all temperatures with chicken, mouse, rat, and hamste~ erythrocytes but not with erythrocytes of human (O), cattle, horses, ~heep, guinea pigs, geese, duck~, pigeons and l-day-old chicks. Chickens showed a~ individual variation in agglutinability of their erythrocytes, requiring selection of birds to obtain erythrocytes for HA. HA reaction was inhibited by specific antiserum. Some factors involved in HA and HA inhibition (HI) were investigated and standard HA and HI tests were worked out.
answer:
Short Communication HEMAGGLUTINATION BY CALF DIARRHEA CORONAVIRUS


question:
Background: Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) are two coronaviruses with demonstrated potential to generate significant nosocomial outbreaks. In particular, MERS continues to pose a significant threat in the Middle East since 2012. Currently, no licensed vaccine or drug treatment is available to treat patients infected with either coronavirus. However, there are some MERS vaccines in the preclinical stage of development. We sought to evaluate the potential impact of targeted vaccination strategies for mitigating SARS and MERS outbreaks in healthcare settings using simple mathematical models and detailed historic transmission trees describing the progression of past nosocomial outbreaks of SARS and MERS. Results: Our findings suggest that vaccination strategies targeting patients and healthcare workers, which have been disproportionately affected during past outbreaks, and assuming two vaccination coverage levels at 50 and 75% have the potential to avert nearly 50% or more of MERS or SARS cases. Conclusion: Our modeling results informed by historic outbreak data for SARS and MERS suggest that vaccination strategies targeting patients could be an effective measure to mitigate and prevent outbreaks in the healthcare setting.
answer:
Evaluating the potential impact of targeted vaccination strategies against severe acute respiratory syndrome coronavirus (SARS- CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) outbreaks in the healthcare setting


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h i g h l i g h t s • A modified community network model with heterogeneity among communities is proposed. • A mathematical epidemic model for each community is presented based on this network. • Study the effect of the location of initial infection node on epidemic spreading. • Study the impact of the heterogeneity among communities on epidemic spreading. a b s t r a c t A large number of real world networks exhibit community structure, and different communities may often possess heterogeneity. In this paper, considering the heterogeneity among communities, we construct a new community network model in which the communities show significant differences in average degree. Based on this heterogeneous community network, we propose a novel mathematical epidemic model for each community and study the epidemic dynamics in this network model. We find that the location of the initial infection node only affects the spreading velocity and barely influences the epidemic prevalence. And the epidemic threshold of entire network decreases with the increase of heterogeneity among communities. Moreover, the epidemic prevalence increases with the increase of heterogeneity around the epidemic threshold, while the converse situation holds when the infection rate is much greater than the epidemic threshold. (B. Song). example, the severe acute respiratory syndrome (SARS) in 2003, the H1N1 influenza A virus in 2009, the H7N9 avian influenza virus in 2013, the Ebola virus in 2014 and the Middle East respiratory syndrome coronavirus (MERS-CoV) in 2015. The problem of epidemic spreading has gained great attention over the years and people want to predict the epidemic spread trend and take effective public health measures with limited vaccine supply. As the topology properties of networks have a profound impact on the dynamics of epidemic spreading, it is necessary to consider the effect of community structure on epidemic spreading. So far, a lot of results on epidemic dynamics in community networks have been obtained [12, 14, 16, 29, 30] . To simulate the real network, many different kinds of community network models were constructed based on classical networks, and some individual behavior characteristics in real networks (such as random walk, long-range jump and awareness) were also taken into account. Liu and Hu studied the SIS (susceptible-infected-susceptible) dynamics on a random community network model with probability p (q) of intra-(inter-)
answer:

Modeling and analysis of epidemic spreading on community networks with heterogeneity