Fig 1.
Probability distribution of the delay in the notified cases.
The distributions of delay and number of cases estimated from the data notified by Opendata SUS platform and the Coronavirus Panel before and after adjustments of the lag between symptom onset and official notification in Fortaleza/CE (Brazil). From right to left, we have: empirical delay distribution (with μ = 25.72 and σ = 31.85), estimated delay distribution(with μ = 10.85 and σ = 9.61), cases distribution before fits, cases distribution after fits. In addition, μ is the mean of the distribution and σ is the standard deviation and the vertical lines on the first and second plots represent the mean of the delay value in each analyzed distribution.
Fig 2.
Schematic for the SENUR model used in this work.
Fig 3.
Overview of the methodology used for the evaluation of the mobility effects on transmission and control of COVID-19.
Table 1.
Periods used to estimate the mobility parameter qt for different cities.
The table presents the periods used in our model to estimate the different values of the mobility parameter qt alongside the qt sample mean. We collected these periods from news provided by the government of each city in 2020. The description describes the main events related to the period.
Fig 4.
Mobility parameters estimated by our model for Fortaleza.
Estimated values of mobility parameters q1 and q2 via MCMC and their resulting distributions. (a) q1 (From 03/15 to 03/20). (b) q2 (From 03/20 to 05/05).
Fig 5.
Simulation results for coarse-grained data.
Number of infected people estimated by our model for the cities of Belo Horizonte, Porto Alegre, São Paulo, Rio de Janeiro, and Fortaleza when analyzed in the context of Waze mobility indexes. The shaded areas represent the 95% confidence region provided by the model; the black line represents the average model prediction and the points the official values released. (a) Belo Horizonte. (b) Porto Alegre. (c) São Paulo. (d) Rio de Janeiro. (e) Fortaleza.
Fig 6.
Simulation results for daily infected cases reported.
Number of infected people estimated by our model for the cities of (a) Belo Horizonte and (b) São Paulo. The shaded areas represent the 95% confidence region provided by the model and the points the official daily values released.
Fig 7.
Results of hypothetical scenarios for coarse-grained data.
The model response when simulating two hypothetical scenarios: I) the population and neither the government prioritized measures to restrict mobility and II) after the first case notified in the city, the government decreed the closure of trade. Here we analyze the ratio between the number of infected individuals in the analyzed scenarios and the actual number of infected individuals to observe the curve trend over time.
Fig 8.
Social analysis of the pandemic spread to the city of Fortaleza.
Results of the social analysis of the spread of the virus under the context of HDI in the neighborhoods of the city of Fortaleza. As we can see, we found that the higher the HDI, the higher the percentage of infected people in the region, as well as the higher recovery, which we believe is directly associated with the greater number of patients with access to quality private hospital treatment. On the other hand, in regions with the lowest HDI rates, we observed the highest percentage of treatments performed independently, which can be explained by the lack of access to public service.
Table 2.
Descriptions of the regions and their respective neighborhoods used in the proposed model.
Fig 9.
Cross-correlation between mobility data and disease spread.
Representation of the cross-correlation between R(t) is the cars’ flow obtained by DETRAN-CE. We observe that, the correlation is maximum (in module) for lag = −1.
Fig 10.
Aggregate flow of vehicles and the reproduction number between the dates 03/20/2020 and 05/04/2020.
Plots in the left show mobility indexes extracted from the Google report, and plots in the right depict the R(t) estimated from DETRAN-CE’s data. We can see similarities between trends in DETRAN-CE’s data and the mobility indexes extracted from the Google report. The highest cross-correlation results are in places labeled as retail and recreation, grocery and pharmacy, and parks.
Fig 11.
Our model applied to data from the city of Fortaleza. The shaded areas represent the 95% confidence region provided by the model; the black line represents the average model prediction and the points the official values released.
Fig 12.
R(t) estimate for all 16 regions of the city of Fortaleza.
We use the number of infected cases estimated by our model for each of the regions. (a) BARROSO. (b) CAVALCANTE. (c) MOURA. (d) PINZON. (e) RE1. (f) RE2. (g) RE3. (h) RE4. (i) RE5. (J) RE6. (k) RE7. (l) RE8. (m) RE9. (n) RE10. (o) RE11. (p)RE12.
Fig 13.
Results of hypothetical scenarios for fine-grained data.
Model’s response when simulating hypothetical scenarios in Fortaleza when we apply fine-grain spatial mobility data.
Fig 14.
Comparison between our mobility quantifier with some real mobility data used in our work.