The impact of COVID-19 mobility restrictions on dengue transmission in urban areas

During the COVID-19 pandemic, governments have been forced to implement mobility restrictions to slow down the spread of SARS-CoV-2. These restrictions have also played a significant role in controlling the spread of other diseases, including those that do not require direct contact between individuals for transmission, such as dengue. In this study, we investigate the impact of human mobility on the dynamics of dengue transmission in a large metropolis. We compare data on the spread of the disease over a nine-year period with data from 2020 when strict mobility restrictions were in place. This comparison enables us to accurately assess how mobility restrictions have influenced the rate of dengue propagation and their potential for preventing an epidemic year. We observed a delay in the onset of the disease in some neighborhoods and a decrease in cases in the initially infected areas. Using a predictive model based on neural networks capable of estimating the potential spread of the disease in the absence of mobility restrictions for each neighborhood, we quantified the change in the number of cases associated with social distancing measures. Our findings with this model indicate a substantial reduction of approximately 72% in dengue cases in the city of Fortaleza throughout the year 2020. Additionally, using an Interrupted Time Series (ITS) model, we obtained results showing a strong correlation between the prevention of dengue and low human mobility, corresponding to a reduction of approximately 45% of cases. Despite the differences, both models point in the same direction, suggesting that urban mobility is a factor strongly associated with the pattern of dengue spread.Author summary: In our study, we discovered a surprising connection between mobility restrictions during the COVID-19 pandemic and the reduction in the spread of dengue, a mosquito-borne disease. We analyzed data from a large city over several years, including the year 2020, when these restrictions were rigorously in place. Our focus was to understand how the limitation of people’s movements influenced the dynamics of dengue transmission. The results were revealing. We observed that, with fewer people moving around, there was a delay in the emergence of dengue in some areas and a decrease in cases in initially affected regions. We utilized both neural network and Interrupted Time Series (ITS) models to predict potential outcomes in the absence of mobility restrictions. Our analysis with the neural network model estimated that approximately 20,000 cases of dengue were prevented, while the ITS model indicated a reduction of about 7,000 cases during this period. This study expands our understanding of dengue, highlighting the importance of human mobility patterns in the spread of this disease. These findings are especially significant for urban areas and have important implications for public health strategies in similar contexts.