instruction:
In this task, you are given a paragraph (from a research paper) and your task is to classify each sentence of the paragraph (assume n sentence) into the following categories: Background, Purpose, Method, Finding/Contribution, or Other. Return the output in this format: 1 - category of sentence 1, 2 - category of sentence 2, ..., n - category of sentence n; where each number indicates the order of the sentence. The categories can be identified using the following questions: 
 Background: - Why is this problem important? - What relevant works have been done before? - What did the previous works miss? - What are the high-level research questions? - How might this help other researchers?
 Purpose: - What specific things do the researchers want to do? - What specific knowledge do the researchers want to know? - What specific hypothesis do the researchers want to test?
 Method: - How did the researchers do it or find it out? - What are the procedures and steps of this research?
 Finding/Contribution: - What did the researchers find out? Did the proposed methods work? - Did things behave as the researchers expected?
Other: put every text fragment that does not fit into any of the categories above here. Put a sentence here if its not in English, is not part of the article, contains numbers and dates, is a caption of a figure or a table, is a formatting error, or you are not sure about it.
question:
The global spread of Coronavirus Disease 2019 (COVID-19) is overwhelming many health-care systems. As a result, epidemiological models are being used to inform policy on how to effectively deal with this pandemic. We note that the majority of existing models do not take into account differences in the amount of interactions between individuals (i.e. the underlying human interaction network). Using network science we demonstrate how this network of interactions can be used to predict the spread of the virus and to inform policy on the most successful mitigation and suppression strategies. Although applicable to disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models. We provide commented source code for all our analyses so that they can easily be integrated into current and future epidemiological models.
answer:
1 - background, 2 - background, 3 - method, 4 - method, 5 - background, 6 - finding, 7 - finding


question:
Citation Chan JM, Rabadan R. 2013. Quantifying pathogen surveillance using temporal genomic data. mBio 4(1):e00524-12.
answer:
1 - other, 2 - other, 3 - other


question:
MERS-coronavirus infection is currently responsible for considerable morbidity and mortality in Saudi Arabia. Understanding its burden, as an emerging infectious disease, is vital for devising appropriate control strategies. In this study, the burden of MERS-CoV was estimated over 31 months period from June 6, 2012 to January 5, 2015. The total number of patients was 835; 528 (63.2%) patients were male, 771 (92.3%) patients were ≥25 years of age, and 210 (25.1%) patients were healthcare workers. A total of 751 (89.9%) patients required hospitalization. The median duration between onset of illness and hospitalization was 2 days (interquartile range, 0-5). The median length of hospital stay was 14 days (IQR, 6-27). The overall case fatality rate was 43.1%. Basic reproductive number was 0.9. Being Saudi, non-healthcare workers, and age ≥65 years were significantly associated with higher mortality. In conclusion, MERS-CoV infection caused a substantial health burden in Saudi Arabia.
answer:

1 - background, 2 - background, 3 - background, 4 - finding, 5 - finding, 6 - finding, 7 - finding, 8 - finding, 9 - finding, 10 - finding, 11 - finding, 12 - finding, 13 - finding, 14 - finding, 15 - finding, 16 - finding