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.

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.
1 - background, 2 - background, 3 - method, 4 - method, 5 - background, 6 - finding, 7 - finding

Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). By comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection.
1 - background, 2 - method, 3 - method, 4 - method, 5 - method, 6 - finding, 7 - finding, 8 - finding, 9 - finding, 10 - finding, 11 - finding

Immunization with different EV types allowed to produce a variety of monoclonal antibodies (MAbs) that are either type-specific (i.e., responsible of virus neutralization) or directed to conserved regions of capsid proteins 15-18 . Reactivity of the latter antibodies may be limited to sets of different EV types or may be directed to a wider range of EV types. Though molecular methods are held to be more informative than classical serologic methods for virus identification 19 , "pan-EV antibodies" capable of reacting with all or with the majority of EV types remain desirable reagents for detecting these agents both in the diagnosis of infectious diseases 20-23 and in immunohistochemistry 24 .

1 - finding, 2 - finding, 3 - finding, 4 - finding