Based on data from the 2018-19 measles epidemic in Manaus, the model showed that understanding the spatial and temporal spread of measles outbreaks provides important information for decision-making and may even mitigate future epidemics.
Researchers from the Institut Pasteur de São Paulo (IPSP) have developed a tool that allows them to monitor the transmission of an infectious disease, helping decision-makers analyze risks and identify the best measures to mitigate future epidemics. By cross-referencing epidemiological data on a measles outbreak in Manaus with the geographic coordinates (zip codes) of infected people, the group created a heat map that allowed them to monitor the spread of the disease in the city.
The study, published in the Einstein Journal, analyzed geographic data from the homes of more than 7,000 people infected with measles in Manaus during the 2018-19 outbreak. Spatial and temporal analyses were performed to characterize various aspects of the outbreak, such as the onset and prevalence of symptoms, demographic data, and vaccination status.
“We had access to microdata from the 2018-19 measles epidemic in Manaus and were able to find out the zip code of the infected people. With the traditional analysis, we identified important information such as the contagion rate and most impacted areas, for example. But we wanted to go beyond that and perform a broader analysis, to create a tool that would make it easy to visualize and understand the problem,” says Helder Nakaya, principal investigator at IPSP and study coordinator.
According to Nakaya, the tool is useful for public administrators, as it allows to quickly estimate the impact of control measures – such as isolation and vaccination – on disease transmission. “The model was based on measles data, as it is rapidly transmitted. However, it allows to create mapping tools for other diseases, which can be very useful for health departments,” he says.
The researcher emphasizes that, besides facilitating the visualization of disease transmission, the tool allows to centralize epidemiological and health data, which are usually scattered across different databases. “This will greatly improve disease monitoring. Public administrators often know the transmission rate data of a given disease. But what does it mean? Generally, they are just tables with numbers they do not necessarily make sense of. That is why it is important to gather a series of data and other information so that public administrators can understand the situation quickly,” he says.
In the work developed with data from Manaus, researchers were also able to measure the impact of vaccination on new measles outbreaks, since approximately 95% of reported cases occurred in individuals who had not been vaccinated.
“One lesson from our work is that vaccination is really important. Our analysis reinforced the fact that vaccination is the best way to combat epidemics. In Manaus, measles vaccination coverage has been low for ten years. Since the tool allows us to visualize where the cases are and perform clustering analyses, it is possible to focus vaccination efforts and health care on the most affected neighborhoods – an important measure to combat the disease,” says Nakaya.
The solution, created by IPSP scientists, also counted with the partnership of Albert Einstein Hospital, Amazonas State University, Heitor Vieira Dourado Tropical Medicine Foundation, University of São Paulo, Georgia Institute of Technology, Amazonas Health Surveillance Foundation, and All for Health Institute (Instituto Todos pela Saúde – ITpS).
The open-source tool for monitoring measles cases is available online at https://carlosprete.shinyapps.io/Measles/. Based on this study, the researchers have developed another tool that can be adapted for any epidemic or outbreak, available at: https://outbreak.sysbio.tools/.