Unveiling the paths of COVID-19 in a large city based on public transportation data

Abstract

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased $$\approx 25$$ ≈ 25 % more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant $$\lambda \approx 33$$ λ ≈ 33 days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

Preventable COVID-19 cases and deaths by alternative vaccination and non-pharmacological intervention policies in Brazil

“…This work uses daily historical data from each state for the number of cases (infections), deaths (absolute number of deaths), and number of vaccinated individuals available at https://github.com/wcota/covid19br 31 which aggregates data from at least two main sources 2 : the Ministry of Health 1 and Brasil.IO 32 . Previous works use the same data source in their analysis (see, for example, Araújo et al 33 , Badr et al 34 , Cassão et al 35 , Aragão et al 36 , and Almeida et al 37 ).…”

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

“…Urban mobility encompasses a wide range of transportation options and sources that help people move around cities efficiently. Some studies related to COVID-19 have focused on public transportation, such as urban rail systems [ 2 ], public buses [ 3 ], a combination of them (e.g., public transit, ride-shares, bikes, and walking [ 4 ]), or gendered mobility patterns [ 5 ]. In principle, knowing more about the relationship between increased or decreased mobility and the incidence of COVID-19 will help us to act more effectively in future pandemics.…”

Enhancing dengue time-series forecasting at the neighborhood level based on the intensity of contagion

“…A ocorrência de doenc ¸as infecciosas em cidades varia no espac ¸o e no tempo, pois depende das características socioambientais de cada região da cidade. Construir modelos preditivos nesse contexto requer mapear os impactos dessas diversas características que podem influenciar a transmissibilidade, tais como a populac ¸ão de um bairro ou o deslocamento de pessoas entre bairros [1,2].…”