Recording and reporting diseases within the community is still a high burden and in a country such as Mozambique, with low capacity for risk management of emerging and re-emerging diseases specially in remote areas, early detection or capture has been considered crucial. Using mobile devices to capture disease cases have enhanced timely reporting and prompt response of disease events, particularly those cases that do not report to the health facilities, preventing the health surveillance system from being able to assemble and include the information in their database for future decision-making purposes. Nevertheless, these same captured events can be used as a tool to promote early detection of cases, through the prediction of likely disease conditions based on the signs and symptoms reported, thus contributing for the reduction of disease spread and the occurrence of pandemics and at the same time promoting and enhancing local capacity for case management. Health technicians from health facilities will be traced and asked to participate in a questionnaire-based interview, where data on their experience with clinical manifestations of emerging and re-emerging disease, as well as endemic disease cases will be compiled. This data will then be compared to a preset clinical manifestation scores map, which will then be weighed and associated with a particular disease through a logistic regression model which is able to obtain the probability of occurrence of a particular event, as well as the influence of each independent variable on the event studied. By the end of this study, a combined set of clinical manifestations will be used to capture and determine the probability of the occurrence of a correspondent set of diseases in a determined place, in order to enhance the local capacity to address health related issues, whilst also timely reporting to the national and subnational level.