key: cord-0796706-dddrzkf6 authors: Romero García, Carolina; Iftimi, Adina; Briz-Redón, Álvaro; Zanin, Massimiliano; Otero, Maria; Ballester, Mayte; de Andrés, José; Landoni, Giovanni; de las Marinas, Dolores; Catalá Bauset, Juan Carlos; Mandingorra, Jesus; Conca, José; Correcher, Juan; Ferrer, Carolina; Lozano, Manuel title: Trends in Incidence and Transmission Patterns of COVID-19 in Valencia, Spain date: 2021-06-18 journal: JAMA Netw Open DOI: 10.1001/jamanetworkopen.2021.13818 sha: 15c64e53f5327d1d008f7ada6a2de3fdcb408c23 doc_id: 796706 cord_uid: dddrzkf6 IMPORTANCE: Limited information on the transmission and dynamics of SARS-CoV-2 at the city scale is available. OBJECTIVE: To describe the local spread of SARS-CoV-2 in Valencia, Spain. DESIGN, SETTING, AND PARTICIPANTS: This single-center epidemiological cohort study of patients with SARS-CoV-2 was performed at University General Hospital in Valencia (population in the hospital catchment area, 364 000), a tertiary hospital. The study included all consecutive patients with COVID-19 isolated at home from the start of the COVID-19 pandemic on February 19 until August 31, 2020. EXPOSURES: Cases of SARS-CoV-2 infection confirmed by the presence of IgM antibodies or a positive polymerase chain reaction test result on a nasopharyngeal swab were included. Cases in which patients with negative laboratory results met diagnostic and clinical criteria were also included. MAIN OUTCOMES AND MEASURES: The primary outcome was the characterization of dissemination patterns and connections among the 20 neighborhoods of Valencia during the outbreak. To recreate the transmission network, the inbound and outbound connections were studied for each region, and the relative risk of infection was estimated. RESULTS: In total, 2646 patients were included in the analysis. The mean (SD) age was 45.3 (22.5) years; 1203 (46%) were male and 1442 (54%) were female (data were missing for 1); and the overall mortality was 3.7%. The incidence of SARS-CoV-2 cases was higher in neighborhoods with higher household income (β(2) [for mean income per household] = 0.197; 95% CI, 0.057-0.351) and greater population density (β(1) [inhabitants per km(2)] = 0.228; 95% CI, 0.085-0.387). Correlations with meteorological variables were not statistically significant. Neighborhood 3, where the hospital and testing facility were located, had the most outbound connections (14). A large residential complex close to the city (neighborhood 20) had the fewest connections (0 outbound and 2 inbound). Five geographically unconnected neighborhoods were of strategic importance in disrupting the transmission network. CONCLUSIONS AND RELEVANCE: This study of local dissemination of SARS-COV-2 revealed nonevident transmission patterns between geographically unconnected areas. The results suggest that tailor-made containment measures could reduce transmission and that hospitals, including testing facilities, play a crucial role in disease transmission. Consequently, the local dynamics of SARS-CoV-2 spread might inform the strategic lockdown of specific neighborhoods to stop the contagion and avoid a citywide lockdown. In the Besag 1 model the risk associated with a region is modelled as the sum of a heterogeneity and a clustering effect. For the temporal effect we want a smooth and flexible evolution, thus we consider a structured random effect in order to make sure that periods close in time are expected to be similar and also allowing for flexible forms for the temporal evolution curves. For this study, we assumed O ij to be the number of observed cases and E ij the expected number of cases for the ith neighborhood and jth day within the period. Hence, the number of observed cases was modelled as O ij ∼ Poisson (r ij E ij ), where r ij is the underlying relative risk for COVID-19 infection. The relative risk represents the ratio between the number of estimated cases provided by the model and the expected number of cases in ith neighborhood on day jth. E ij was calculated as the total number of cases observed on day j multiplied by the fraction of the population that neighborhood i represents. The following equation shows the general definition of the additive spatiotemporal model: where is the global intercept of the model; ′ represent covariate ( ) effects; and are two spatial effects adopting the standard model 2 with structured and unstructured components, respectively; and and represent the structured and distribution was chosen to model the spatially-structured random effect. The usual contiguity matrix which considers that two areas are neighbors if they share a geographical border was considered to define this prior. An independent zero-mean normal prior was used for the temporal effect . In contrast, the parameter displays a temporal structure. We considered a second-order random walk (RW2) for , with a prior in which effects for neighboring time points tend to be alike. The parameter represents space-time interaction. The spatio-temporal interaction term was modeled through a Gaussian prior, which corresponds to the type I interaction in the context of the Knorr-Held models. Specific constraints on the random effects have been considered to avoid identifiability issues. 3 The following covariates were studied: population density ( 1 , in inhabitants/km 2 ); average income per household ( 2 , in euro); and three meteorological variables, namely average temperature ( 3 , in °C), average wind speed ( 4 , in km/h), and number of sunlight hours ( 5 , number of hours in which solar irradiance is >120 W/m 2 ). Population density and income data were obtained from the National Statistics Institute. The effect of the meteorological covariates was considered on a daily basis and with a 7-day lag. An ordinary kriging model 4 was used to estimate the covariate values. Daily measurements were collected from the meteorological stations of the National Weather Service (AEMET). Propagation Assessment The Granger´s causality test 5 is based on two intuitive concepts: causes must precede the corresponding effects and the prediction of the caused time series should be improved when using information from the causes. It has been extensively applied to economic 6 and biomedical data. 7, 8 If the propagation of COVID-19 occurs from neighborhood B to neighborhood A, a forecast of the number of cases in A should be more precise when information from the past of B is also included. Reversing the argument, such forecast can then be used to infer the presence of a propagation process, without the need of a priori information as mobility data, but only relying on the local evolution of the pandemics. present. Before applying it, time series have been checked for stationarity, i.e. for the absence of linear and periodic trends; note that the presence of such trends is known to lead to spurious results in a Granger test. Stationarity has been assessed using an augmented Dickey-Fuller unit root test. 9 While half of them, the presence of a unit root could not be discarded (for = 0.01), an autocorrelation analysis showed that this was mainly due to a periodicity of 7 days. This, nevertheless, did not affect the results, as most of the causality relationships were found for shorter time lags (Fig. 1a) . The associations detected by the Granger test were on a shorter time scale than the trends, thus the impact of the latter ones is negligible. The forecast of the time series was then performed through an autoregressive-moving-average model, 10 in which the time series of the causing element was shifted a number of time steps back in time. An F-test was finally applied to assess the statistical significance of this inequality of Eq (2) and to obtain a corresponding p-value. The network', in which the dynamics of nodes is expected to be a function of the connectivity; the latter (the unknown part) is then reconstructed through the former (the known data). 13 We further calculated a weight matrix , encoding how strong the propagation was between pairs of neighborhoods. For each pair ( , ) with , = 1, , was defined as − 10 , , where , is the p-value obtained by the Granger test for neighborhoods and . Therefore, the larger the value , , the clearer the propagation process from a Granger point of view. Note that the adjacency matrix and the weight matrix represent complementary views of the same information. Each element ( , ) in them is derived from the same Granger test, and specifically from the corresponding p-value. , yields a binarized view of the p-value, i.e. whether a statistically significant association exists; on the other hand, , indicates the strength of such association. This dual view of the connectivity structure is customary in network science, and each matrix is used to characterize different aspects of it. The resulting network was analyzed in terms of the following metrics 14 : • Out-degree: number of outbound links from a given node, or number of neighborhoods the vector is propagating. This value corresponds to the row-sum of the adjacency matrix . • In-degree: number of inbound links at a given node, or number of neighborhoods propagating to it. • Total degree: sum of inbound and outbound links of a node. © 2021 Romero García C et al. JAMA Network Open. • Betweenness centrality: how instrumental or strategical a neighborhood was in propagating the disease throughout the whole network, defined the sum of the fractions of all-pair shortest paths that pass through that node. 15 In other words, this centrality assesses how many times a neighborhood has mediated the propagation between two other regions. The distance between pairs of nodes, which corresponds to the dissimilarity between the corresponding neighborhoods, is defined as the inverse of , . The resulting values were normalized such that the most central node has a betweenness centrality of 1. 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