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. 2022 Jun:39:100587.
doi: 10.1016/j.epidem.2022.100587. Epub 2022 Jun 1.

Synchronized spread of COVID-19 in the cities of Bahia, Brazil

Hugo Saba  1 , A S Nascimento Filho  2 , José G V Miranda  3 , Raphael S Rosário  4 , Thiago B Murari  5 , Eduardo M F Jorge  6 , Elaine C B Cambui  7 , Márcia S P L Souza  8 , Ana C F N Silva  9 , Márcio L V Araújo  10
Affiliations

Synchronized spread of COVID-19 in the cities of Bahia, Brazil

Hugo Saba et al. Epidemics. 2022 Jun.

Abstract

The COVID-19 pandemic, caused by the highly transmissible SARS-CoV-2 virus, has overloaded health systems in many contexts Conant and Wolfe (2008). Brazil has experienced more than 345,000 deaths, as of April/2021 Conant and Wolfe (2008), with dire consequences for the country's public and private health systems. This paper aims to estimate the synchronization graph between the cities' contagion waves from public COVID-19 data records. For this purpose, the Motif-Synchronization method Magwire et al. (2011) was applied to publicly available COVID-19 data records to determine the sequential relationship of occurrence of the waves among Bahia's cities. We find synchronization between waves of infection between cities, suggesting diffusion of the disease in Bahia and a potential role for inter-city transportation Saba et al. (2018), Saba et al. (2014), Araújo et al. (2018) in the dynamics of this phenomenon McKee and Stuckler (2020), Chinazzi et al. (2020), Tizzoni et al. (2014). Our main contribution lies in the use of the Motif-Synchronization method applied to COVID-19 data records, with the results revealing a pattern of disease spread that extends beyond city boundaries.

Keywords: COVID-19; Motif–Synchronization; Spread.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The Motif–Synchronization method design - For the time window T1 the method is applied by obtaining a network for the given time instant. By moving the time window along the time series, the process is repeated generating all other networks of the Time Varying Graph (TVG) structure.
Fig. 2
Fig. 2
Correlation between Edges and Incidence of cases in Bahia, the linear adjust (red line) presented R2=0.92 and slope = 32.81. Until incidence of cases equal to 40, the relation between Edges and Incidence of cases is strong. The linear region of the curve represents 27% of the connected nodes in the network (11/41). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 3
Fig. 3
Network of cities with edges with weight greater than or equal to 18 weeks of correlation.
Fig. 4
Fig. 4
Time series of dates of first symptom grouped in weeks in the cities. The weights demonstrate the numbers of times that a significant synchronization occurred between the incidence time series of the same pair of cities.
See this image and copyright information in PMC

References

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