Dengue fever is a virus that is transmitted from person to person by mosquitos, globally causing an estimated 390 million cases each year. With 2.5 billion people at risk of infection and the virus expanding into new regions, dengue has become a major global health concern. Nevertheless, it is still classified as a neglected tropical disease – a group of illnesses that primarily affect low-income countries and have historically received less funding for prevention and research. Because of this underinvestment, it is essential to make intervention strategies as effective and efficient as possible, especially in countries with limited resources like Cuba. Many different dengue control methods exist, but in order to develop the most efficient intervention strategy, the best time, place and method of application needs to be determined. With the use of mathematical models, it is possible to predict and evaluate the effects of an intervention based on specified parameters that can influence the spread of the disease. These models often include climatic conditions and human/mosquito behaviour. While the important influence of human mobility on the spread of the disease has been acknowledged, it has not been included into a dengue model yet. This lead to wrong predictions during the Covid-19 mobility restrictions, where lower mobility resulted in a reduced number of cases. Before, it was believed that transmission of dengue mostly happened in or near the home, but now it is suggested that people might get infected in other commonly visited public areas like workplaces or schools. However, we still don’t fully understand where dengue transmission actually occurs. To answer that question, we need a better insight into how much time people spend at home versus outside, and how their daily travel patterns might contribute to the spread of the virus. By studying locations of cases, we will be able to examine patterns in the spread of the disease over space and time and explore the role of mobility by assessing overlap with commuting flows. With this intention, we analysed dengue case data from Cienfuegos, Cuba, during different epidemic phases to see which areas were involved at the beginning, peak, end or in between the epidemics. We also used a mobility questionnaire – asking people where they went during the day – to understand who moved where and when. Based on this, new ways to estimate each person’s exposure risk based on their mobility patterns were created. At last, we explored whether it was possible to discover overlap between areas with many visits and areas with many cases. The use of case data in combination with a mobility questionnaire proved suitable and demonstrated some early findings. For example, some areas consistently showed higher numbers of cases at specific epidemic phases. Men were found to visit more locations per week than woman and were more likely to go to parks. Older adults visited shops and markets more often than younger people and used bicycles and public transport less frequently. People were less likely to work on Sundays and more likely to visit friends or family, while Saturday was the typical day to spend time outdoors. In terms of overlap, two regions stood out as both highly visited and heavily affected by dengue. These first findings should be treated with caution because they were based on a small, local sample from which no definitive results or conclusions could be drawn. However, they demonstrated that combining case data with a mobility questionnaire can distinguish useful mobility and spread patterns. The approach shows promise for larger-scale studies that could validate and expand these findings. To further obtain a comprehensive understanding of human mobility, qualitative research is recommended to complement the mobility questionnaire, as well as studies in different regions for validation. This information could then be used to update dengue models and help design more targeted, cost-effective and efficient public health intervention strategies, saving lives and reducing health inequalities in countries with limited resources.