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Epidemiology and Infection Risk clusters of COVID-19 transmission in northeastern Brazil: prospective cambridge.org/hyg space–time modelling 1,2 2,3 2,4 2,5 Original Paper D. S. Gomes , L. A. Andrade , C. J. N. Ribeiro , M. V. S. Peixoto , S. V. M. A. Lima2,6 , A. M. Duque2,7 , T. M. Cirilo1 , M. A. O. Góes2,8,9 , Cite this article: Gomes DS et al (2020). Risk 8 10 2,10 2,3,6 clusters of COVID-19 transmission in A. G. C. F. Lima , M. B. Santos , K. C. G. M. Araújo and A. D. Santos northeastern Brazil: prospective space–time modelling. Epidemiology and Infection 148, 1Universidade Federal de Sergipe, Programa de Pós-graduação em Biologia Parasitária, Aracaju, Sergipe, Brazil; – e188, 1 8. https://doi.org/10.1017/ 2Universidade Federal de Sergipe, Núcleo de Investigação em Saúde Coletiva, Aracaju, Sergipe, Brazil; S0950268820001843 3Universidade Federal de Sergipe, Programa de Pós-graduação em Enfermagem, Aracaju, Sergipe, Brazil; 4Instituto Federal de Educação, Ciência e Tecnologia de Sergipe, São Cristóvão, Sergipe, Brazil; 5Departamento de Received: 10 June 2020 6 Revised: 11 August 2020 Fonoaudiologia, Universidade Federal de Sergipe, Aracaju, Sergipe, Brazil; Departamento de Enfermagem, 7 Accepted: 14 August 2020 Universidade Federal de Sergipe, Lagarto, Sergipe, Brazil; Departamento de Terapia Ocupacional, Universidade Federal de Sergipe, Lagarto, Sergipe, Brazil; 8Secretaria Estadual de Saúde de Sergipe, Aracaju, Sergipe, Brazil; Key words: 9Departamento de Medicina, Universidade Federal de Sergipe, Aracaju, Sergipe, Brazil and 10Departamento de COVID-19; disease surveillance; pandemic; Morfologia, Universidade Federal de Sergipe, Aracaju, Sergipe, Brazil space–time cluster; spatial analysis Author for correspondence: Abstract A. D. Santos, E-mail: [email protected] This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and tem- poral trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was iden- tified high risk clusters for COVID-19, especially in the coastal side. Introduction The world has been facing an international public health emergency caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), termed as coronavirus disease 2019 (COVID-19) [1]. The disease was firstly reported in the city of Wuhan (China) at the end of December 2019 and was declared a pandemic by the World Health Organisation in March 2020 [2]. There were more than 17 million cases and over 650 000 deaths by COVID-19 confirmed worldwide [1]. Notably, the pandemic has been challenging for health systems and governments, as the extent of the social and economic impacts of the pandemic is still uncertain [3]. Following the disease’s dynamics and the exponential growth of the number of cases, sev- eral studies have been reported [2–5]. Particularly, those that perform temporal and spatial analyses of COVID-19 have demonstrated the impact of morbidity, mortality and global geo- graphical dissemination of the disease in the world. The use of aggregate spatial data allows to © The Author(s), 2020. Published by map the patterns of the rapid progression of the disease and to support decision-making in the Cambridge University Press. This is an Open allocation of resources for the prevention and control of COVID-19 in priority areas [3–6]. Access article, distributed under the terms of In this context, a study that used spatial analysis techniques, conducted in China, found the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), that SARS-CoV-2 infection was spatially dependent and spread mainly from Hubei province, which permits unrestricted re-use, in Central China, to the surrounding areas [7]. Additionally, the spatial distribution of cases distribution, and reproduction in any medium, and mortality by COVID-19 is heterogeneous across regions, especially in those with socio- provided the original work is properly cited. economic disparities [8, 9]. This uneven geographic distribution has been observed in several regions of the United States. The disease has disproportionately affected populations in situations of social vulnerabil- ity, as observed in poorer communities from Chicago and New York. The inadequate effects of COVID-19 reflect the social inequities that existed prior to the current health crisis [9]. 2 D. S. Gomes et al. Recently, Brazil has become the epicentre of the epidemic in Latin America and ranks second in the world in the total number of cases (behind only the USA) [10]. COVID-19’s first case was confirmed on 26 February 2020 and the country currently has more than half a million cases and about 30 000 deaths. Among Brazilian regions, the Northeast ranks second with the highest number of cases [11]. Additionally, Brazil is still marked by great social and human development inequalities, especially in the Northeast region [12]. This highlights the need for scientific research on the epidemiology and spatial distribution of COVID-19 in the municipalities of this region. Notably, studies of spatial and temporal patterns help to eluci- date the mechanisms of disease spread in the population and to identify factors associated with heterogeneous geographic distri- bution [3, 6]. Similarly, prospective space–time analysis is required to monitor outbreaks, as it allows the detection of active, emerging clusters and the relative risk (RR) for each affected site during the epidemic [13]. Therefore, considering the current situ- ation of COVID-19 in the regions of the country, the study aimed to analyse the trend and spatial–temporal clusters of transmission risk of COVID-19 in northeastern Brazil, defining priority areas for surveillance actions and more effective disease control in the states. Materials and methods Study design We conducted an ecological study with techniques of spatial ana- lysis and temporal trend. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May Fig. 1. Study location. 2020 (divided into 12 epidemiological weeks). The units of ana- lysis were the nine states (federative unit) in the region and its 1794 municipalities. Data were collected daily regarding the (b) Weekly incidence rates for states, metropolitan (MA) and municipality of residence of confirmed cases of COVID-19. We inland areas were calculated per 100 000 inhabitants. For excluded 1519 cases with no data of county location [14]. the calculation, we used the number of confirmed cases of COVID-19 per week in each state, in the metropolitan and Study area description inland areas, as the numerator and the corresponding popu- 2 lations as the denominator; Brazil occupies a territorial area of 8.51 million km , which is (c) COVID-19 incidence rates of municipalities were calculated equivalent to almost 50% of South America territory and has a per 100 000 inhabitants. For the calculation, the number of total population of 210.1 million inhabitants [15]. It is the country accumulated cases of COVID-19 in each municipality was with the fifth largest territorial area on the planet and the sixth used as the numerator and the corresponding current popu- largest population, with a demographic density of 24.47 inhabi- lation as the denominator. tants per km2 [15, 16]. The Northeast region (latitude: 01° 02′30′′N/18°20′07′′S; longitude: 34°47′30′′E/48°45′24′′W) is one of the five Brazilian regions and the one with the largest number of federative units (nine) (Fig. 1). This region has the third largest Time trend analysis 2 territorial area in Brazil (155 291 744 km ) and a population of To assess the weekly time trend of cases by COVID-19, we per- 57 071 654 inhabitants, which corresponds to about 30% of the formed data analysis using the segmented log-linear regression Brazilian population. The highest population density occurs in model. The incidence rates of COVID-19 were considered depend- the coastal strip, where most state capitals are located [16]. ent variables and the epidemiological weeks were the independent The Northeast region of Brazil also presents the lowest human variables. The Monte Carlo permutation test was used to select the development index in Brazil (HDI = 0.663) [17]. best model for inflection points, applying 999 permutations and considering the highest residue determination coefficient (R2). To describe and quantify the time trends, we calculated the weekly Variables and measures percentage increments (adapted from annual percent changes; The variables analysed in this study were: APC) [18] and their respective confidence intervals (95% CI). Once more than one significant inflection was detected during (a) New cases of COVID-19 in the 1794 municipalities of Northeast the study period, and the average annual percentage changes region of Brazil. The calculation was based on subtracting the (AAPCs) were calculated. Time trends were considered statistically ’ previous day scount(nt) from the previous day (nt−1); significant when APCs had a P-value <0.05 and their 95% CI did Epidemiology and Infection 3 not include a zero value.
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