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.

Q: The spread of infectious disease epidemics is mediated by human travel. Yet human mobility patterns vary substantially between countries and regions. Quantifying the frequency of travel and length of journeys in well-defined population is therefore critical for predicting the likely speed and pattern of spread of emerging infectious diseases, such as a new influenza pandemic. Here we present the results of a large population survey undertaken in 2007 in two areas of China: Shenzhen city in Guangdong province, and Huangshan city in Anhui province. In each area, 10,000 randomly selected individuals were interviewed, and data on regular and occasional journeys collected. Travel behaviour was examined as a function of age, sex, economic status and home location. Women and children were generally found to travel shorter distances than men. Travel patterns in the economically developed Shenzhen region are shown to resemble those in developed and economically advanced middle income countries with a significant fraction of the population commuting over distances in excess of 50 km. Conversely, in the less developed rural region of Anhui, travel was much more local, with very few journeys over 30 km. Travel patterns in both populations were well-fitted by a gravity model with a lognormal kernel function. The results provide the first quantitative information on human travel patterns in modern China, and suggest that a pandemic emerging in a less developed area of rural China might spread geographically sufficiently slowly for containment to be feasible, while spatial spread in the more economically developed areas might be expected to be much more rapid, making containment more difficult.

A: Travel Patterns in China
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Q: Background Uganda is currently implementing the International Health Regulations (IHR[2005]) within the context of Integrated Disease Surveillance and Response (IDSR). The IHR(2005) require countries to assess the ability of their national structures, capacities, and resources to meet the minimum requirements for surveillance and response. This report describes the results of the assessment undertaken in Uganda. We conducted a descriptive cross-sectional assessment using the protocol developed by the World Health Organisation (WHO). The data collection tools were adapted locally and administered to a convenience sample of IHR(2005) stakeholders, and frequency analyses were performed. Results Ugandan national laws relevant to the IHR(2005) existed, but they did not adequately support the full implementation of the IHR(2005). Correspondingly, there was a designated IHR National Focal Point (NFP), but surveillance activities and operational communications were limited to the health sector. All the districts (13/13) had designated disease surveillance offi ces, most had IDSR technical guidelines (92%, or 12/13), and all (13/13) had case defi nitions for infectious and zoonotic diseases surveillance. Surveillance guidelines were available at 57% (35/61) of the health facilities, while case defi nitions were available at 66% (40/61) of the health facilities. The priority diseases list, surveillance guidelines, case defi nitions and reporting tools were based on the IDSR strategy and hence lacked information on the IHR(2005). The rapid response teams at national and district levels lacked food safety, chemical and radio-nuclear experts. Similarly, there were no guidelines on the outbreak response to food, chemical and radionuclear hazards. Comprehensive preparedness plans incorporating IHR(2005) were lacking at national and district levels. A national laboratory policy existed and the strategic plan was being drafted. However, there were critical gaps hampering the effi cient functioning of the national laboratory network. Finally, the points of entry for IHR(2005) implementation had not been designated. The assessment highlighted critical gaps to guide the IHR(2005) planning process. The IHR(2005) action plan should therefore be developed to foster national and international public health security.

A: S9 Uganda. 2 World Health Organisation Country Offi ce, Plot 60, Prince Charles Drive, P.O. Box 24578, Kampala. 3 World Health Organisation -Regional Offi ce
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Q: Background: For disease surveillance, manual data collection using paper-based questionnaires can be time consuming and prone to errors. We introduced smartphone data collection to replace paper-based data collection for an influenza sentinel surveillance system in four hospitals in Kenya. We compared the quality, cost and timeliness of data collection between the smartphone data collection system and the paper-based system. Methods: Since 2006, the Kenya Ministry of Health (MoH) with technical support from the Kenya Medical Research Institute/Centers for Disease Control and Prevention (KEMRI/CDC) conducted hospital-based sentinel surveillance for influenza in Kenya. In May 2011, the MOH replaced paper-based collection with an electronic data collection system using Field Adapted Survey Toolkit (FAST) on HTC Touch Pro2 smartphones at four sentinel sites. We compared 880 paper-based questionnaires dated Jan 2010-Jun 2011 and 880 smartphone questionnaires dated May 2011-Jun 2012 from the four surveillance sites. For each site, we compared the quality, cost and timeliness of each data collection system. Results: Incomplete records were more likely seen in data collected using pen-and-paper compared to data collected using smartphones (adjusted incidence rate ratio (aIRR) 7, 95% CI: 4.4-10.3). Errors and inconsistent answers were also more likely to be seen in data collected using pen-and-paper compared to data collected using smartphones (aIRR: 25, 95% CI: 12.5-51.8). Smartphone data was uploaded into the database in a median time of 7 days while paper-based data took a median of 21 days to be entered (p < 0.01). It cost USD 1,501 (9.4%) more to establish the smartphone data collection system ($17,500) than the pen-and-paper system (USD $15,999). During two years, however, the smartphone data collection system was $3,801 (7%) less expensive to operate ($50,200) when compared to pen-and-paper system ($54,001). Conclusions: Compared to paper-based data collection, an electronic data collection system produced fewer incomplete data, fewer errors and inconsistent responses and delivered data faster. Although start-up costs were higher, the overall costs of establishing and running the electronic data collection system were lower compared to paper-based data collection system. Electronic data collection using smartphones has potential to improve timeliness, data integrity and reduce costs.

A:
A comparison of smartphones to paper-based questionnaires for routine influenza sentinel surveillance, Kenya, 2011-2012
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