Impact of CoronaVac on Covid-19 outcomes of elderly adults in a large and socially unequal Brazilian city: A target trial emulation study.

BACKGROUND: Although CoronaVac was the only Covid-19 vaccine adopted in the first months of the Brazilian vaccination campaign, randomized clinical trials to evaluate its efficacy in elderly adults were limited. In this study, we use routinely collected surveillance and SARS-CoV-2 vaccination and testing data comprising the population of the fifth largest city of Brazil to evaluate the effectiveness of CoronaVac in adults 60+ years old against severe outcomes. METHODS: Using large observational databases on vaccination and surveillance data from the city of Fortaleza, Brazil, we defined a retrospective cohort including 324,302 eligible adults aged ≥60 years to evaluate the effectiveness of the CoronaVac vaccine. The cohort included individuals vaccinated between January 21, 2021, and August 31, 2021, who were matched with unvaccinated persons at the time of rollout following a 1:1 ratio according to baseline covariates of age, sex, and Human Development Index of the neighborhood of residence. Only Covid-19-related severe outcomes were included in the analysis: hospitalization, ICU admission, and death. Vaccine effectiveness for each outcome was calculated by using the risk ratio between the two groups, with the risk obtained by the Kaplan-Meier estimator. RESULTS: We obtained 62,643 matched pairs for assessing the effectiveness of the two-dose regimen of CoronaVac. The demographic profile of the matched population was statistically representative of the population of Fortaleza. Using the cumulative incidence as the risk associated with each group, starting at day 14 since the receipt of the second dose, we found an 82.3 % (95 % CI 66.3-93.9) effectiveness against Covid-19-related death, 68.4 % (95 % CI 42.3-86.4) against ICU admission, and 55.8 % (95 % CI 42.7-68.3) against hospital admission. CONCLUSIONS: Our results show that, despite critical delays in vaccine delivery and limited evidence in efficacy trial estimates, CoronaVac contributed to preventing deaths and severe morbidity due to Covid-19 in elderly adults.

Unveiling the paths of COVID-19 in a large city based on public transportation data.

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased [Formula: see text]% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant [Formula: see text] days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization.

The spread of COVID-19 caused by the SARS-CoV-2 virus has become a worldwide problem with devastating consequences. Here, we implement a comprehensive contact tracing and network analysis to find an optimized quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints to monitor the evolution of the contact network of disease transmission before and after mass quarantines. As a consequence of the lockdowns, people's mobility decreases by 53%, which results in a drastic disintegration of the transmission network by 90%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, an optimized strategy to break the chain of transmission is to quarantine a minimal number of 'weak links' with high betweenness centrality connecting the large k-cores.

Decomposition of socioeconomic inequalities in arboviral diseases in Brazil and Colombia (2007-2017).

BACKGROUND: We used surveillance data from Brazil and Colombia during 2007-2017 to assess the presence of socioeconomic inequalities on dengue, chikungunya and Zika at the neighborhood level in two Latin American cities. METHODS: To quantify the inequality, we estimated and decomposed the relative concentration index of inequality (RCI) accounting for the spatiotemporal distribution of the diseases. RESULTS: There were 281 426 arboviral cases notified in Fortaleza, Brazil, and 40 889 in Medellin, Colombia. The RCI indicated greater concentration of dengue cases among people living in low socioeconomic settings in both sites. The RCIs for chikungunya in Fortaleza covered the line of equality during their introduction in 2014, while the RCIs for Zika and chikungunya in Medellin indicated the presence of a small inequality. The RCI decomposition showed that year of notification and age were the main contributors to this inequality. In Medellin, the RCI decomposition showed that age and access to waste management accounted for 75.5%, 72.2% and 54.5% of the overall inequality towards the poor for dengue, chikungunya and Zika, respectively. CONCLUSIONS: Our study presents estimates of the socioeconomic inequality of arboviruses and its decomposition in two Latin American cities. We corroborate the concentration of arboviral diseases in low socioeconomic neighborhoods and identify that year of occurrence, age, presence of healthcare facilities and waste management are key determinants of the heterogenous distribution of endemic arboviruses across the socioeconomic spectrum.

Spatiotemporal distribution and socioeconomic disparities of dengue, chikungunya and Zika in two Latin American cities from 2007 to 2017.

OBJECTIVE: To assess the presence, pattern and magnitude of socioeconomic inequalities on dengue, chikungunya and Zika in Latin America, accounting for their spatiotemporal distribution. METHODS: Using longitudinal surveillance data (reported arboviruses) from Fortaleza, Brazil and Medellin, Colombia (2007-2017), we fit Bayesian hierarchical models with structured random effects to estimate: (i) spatiotemporally adjusted incidence rates; (ii) Relative Concentration Index and Absolute Concentration Index of inequality; (iii) temporal trends in RCIs; and (iv) socioeconomic-specific estimates of disease distribution. The spatial analysis was conducted at the neighbourhood level (urban settings). The socioeconomic measures were the median monthly household income (MMHI) for Brazil and the Socio-Economic Strata index (SES) in Colombia. RESULTS: There were 281 426 notified arboviral cases in Fortaleza and 40 887 in Medellin. We observed greater concentration of dengue among residents of low socioeconomic neighbourhoods in both cities: Relative Concentration Index = -0.12 (95% CI = -0.13, -0.10) in Fortaleza and Relative Concentration Index = -0.04 (95% CI = -0.05, -0.03) in Medellin. The magnitude of inequalities varied over time across sites and was larger during outbreaks. We identified a non-monotonic association between disease rates and socioeconomic measures, especially for chikungunya, that changed over time. The Relative Concentration Index and Absolute Concentration Index showed few if any inequalities for Zika. The socioeconomic-specific model showed increased disease rates at MMHI below US$400 in Brazil and at SES-index below level four, in Colombia. CONCLUSIONS: We provide robust quantitative estimates of socioeconomic inequalities in arboviruses for two Latin American cities. Our findings could inform policymaking by identifying spatial hotspots for arboviruses and targeting strategies to decrease disparities at the local level.

Spatiotemporal transmission dynamics of co-circulating dengue, Zika, and chikungunya viruses in Fortaleza, Brazil: 2011-2017.

The mosquito-borne viruses dengue (DENV), Zika (ZIKV), and chikungunya (CHIKV), now co-endemic in the Americas, pose growing threats to health worldwide. However, it remains unclear whether there exist interactions between these viruses that could shape their epidemiology. This study advances knowledge by assessing the transmission dynamics of co-circulating DENV, ZIKV, and CHIKV in the city of Fortaleza, Brazil. Spatiotemporal transmission dynamics of DENV, ZIKV, and CHIKV were analyzed using georeferenced data on over 210,000 reported cases from 2011 to 2017 in Fortaleza, Brazil. Local spatial clustering tests and space-time scan statistics were used to compare transmission dynamics across all years. The transmission of co-circulating viruses in 2016 and 2017 was evaluated at fine spatial and temporal scales using a measure of spatiotemporal dependence, the τ-statistic. Results revealed differences in the diffusion of CHIKV compared to previous DENV epidemics and spatially distinct transmission of DENV/ZIKV and CHIKV during the period of their co-circulation. Significant spatial clustering of viruses of the same type was observed within 14-day time intervals at distances of up to 6.8 km (p<0.05). These results suggest that arbovirus risk is not uniformly distributed within cities during co-circulation. Findings may guide outbreak preparedness and response efforts by highlighting the clustered nature of transmission of co-circulating arboviruses at the neighborhood level. The potential for competitive interactions between the arboviruses should be further investigated.

Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas.

Dengue is a vector-borne disease transmitted by the Aedes genus mosquito. It causes financial burdens on public health systems and considerable morbidity and mortality. Tropical regions in the Americas and Asia are the areas most affected by the virus. Fortaleza is a city with approximately 2.6 million inhabitants in northeastern Brazil that, during the recent decades, has been suffering from endemic dengue transmission, interspersed with larger epidemics. The objective of this paper is to study the impact of human mobility in urban areas on the spread of the dengue virus, and to test whether human mobility data can be used to improve predictions of dengue virus transmission at the neighbourhood level. We present two distinct forecasting systems for dengue transmission in Fortaleza: the first using artificial neural network methods and the second developed using a mechanistic model of disease transmission. We then present enhanced versions of the two forecasting systems that incorporate bus transportation data cataloguing movement among 119 neighbourhoods in Fortaleza. Each forecasting system was used to perform retrospective forecasts for historical dengue outbreaks from 2007 to 2015. Results show that both artificial neural networks and mechanistic models can accurately forecast dengue cases, and that the inclusion of human mobility data substantially improves the performance of both forecasting systems. While the mechanistic models perform better in capturing seasons with large-scale outbreaks, the neural networks more accurately forecast outbreak peak timing, peak intensity and annual dengue time series. These results have two practical implications: they support the creation of public policies from the use of the models created here to combat the disease and help to understand the impact of urban mobility on the epidemic in large cities.

Mapping disparities in homicide trends across Brazil: 2000-2014.

BACKGROUND: Homicides are a major problem in Brazil. Drugs and arms trafficking, and land conflicts are three of the many factors driving homicide rates in Brazil. Understanding long-term spatiotemporal trends and social structural factors associated with homicides in Brazil would be useful for designing policies aimed at reducing homicide rates. METHODS: We obtained data from 2000 to 2014 from the Brazil Ministry of Health (MOH) Mortality Information System and sociodemographic data from the Brazil Institute of Geography and Statistics (IBGE). First, we quantified the rate of change in homicides at the municipality and state levels. Second, we used principal component regression and k-medoids clustering to examine differences in temporal trends across municipalities. Lastly, we used Bayesian hierarchical space-time models to describe spatio-temporal patterns and to assess the contribution of structural factors. RESULTS: There were significant variations in homicide rates across states and municipalities. We noted the largest decrease in homicide rates in the western and southeastern states of Sao Paulo, Rio de Janeiro and Espirito Santo, which coincided with an increase in homicide rates in the northeastern states of Ceará, Alagoas, Paraiba, Rio Grande Norte, Sergipe and Bahia during the fifteen-year period. The decrease in homicides in municipalities with populations of at least 250,000 coincided with an increase in municipalities with 25,000 people or less. Structural factors that predicted municipality-level homicide rates included crude domestic product, urbanization, border with neighboring countries and proportion of population aged fifteen to twenty-nine. CONCLUSIONS: Our findings support both a dissemination hypothesis and an interiorization hypothesis. These findings should be considered when designing interventions to curb homicide rates.

Infestation measured by Aedes aegypti larval surveys as an indication of future dengue epidemics: an evaluation for Brazil.

BACKGROUND: Aedes aegypti rapid larval surveys are mandatory in Brazil. Here, we retrospectively examined whether the house index estimated by larval surveys served as a useful tool in anticipating epidemics within Brazilian municipalities from 2009-2015. METHODS: We used correlation indices and classification analysis stratified by year, region, population size and time after the national larval survey. RESULTS: We found no association between the house index and the proportion of municipalities experiencing an epidemic. The sensitivity of a high score house index in predicting an epidemic was 7.20% (95% CI 6.22 to 8.33%) for all years combined. The positive predictive value of a high score house index to predict a 'true epidemic' was 38.96%, lower than the negative predictive values of a low score house index for predicting 'no epidemic' (56.96%). The highest overall sensitivity was observed in the North region (20.15%; 95% CI 17.14 to 23.53%). The sensitivity of a high score house index demonstrated a monotonic decrease with increasing time from larval collection. CONCLUSIONS: Larval surveys are surveillance tools with the potential to risk-stratify and guide dengue control programs towards judicious resource allocation. However, the national rapid larval survey performed in Brazil, in its present form, consistently underpredicts dengue epidemics.

Evaluation of the usefulness of Aedes aegypti rapid larval surveys to anticipate seasonal dengue transmission between 2012-2015 in Fortaleza, Brazil.

Rapid larval surveys have been mandated in nearly every urban Brazilian municipality and promoted by the Pan American Health Organization. These surveys purport to classify arbovirus transmission risk as a basis to triage local surveillance and vector control operations, yet no previous analyses have determined relative risk associated with marginal changes in infestation at administrative and temporal scales relevant to vector control. We estimated associations between entomological indices from six larval surveys and daily incidence rates of confirmed dengue cases in Fortaleza, Brazil using models adjusted for rainfall, and indicators of spatial association. Poor correspondence between infestation and incidence indicates that these surveys may systematically mislead vector control activities and treatment strategies in Fortaleza and in similar cities throughout Latin America. The co-circulation of multiple arboviruses enhances the importance of determining the true informational value of these surveys, and of identifying complementary tools to discern local and inter-annual transmission risk.

Epidemiological characteristics and determinants of dengue transmission during epidemic and non-epidemic years in Fortaleza, Brazil: 2011-2015.

BACKGROUND: After being eliminated during the 1950s, dengue reemerged in Brazil in the 1980s. Since then, incidence of the disease has increased, as serotypes move within and between cities. The co-circulation of multiple serotypes contributes to cycles of epidemic and interepidemic years, and a seasonal pattern of transmission is observed annually. Little is known regarding possible differences in the epidemiology of dengue under epidemic and interepidemic scenarios. This study addresses this gap and aims to assess the epidemiological characteristics and determinants of epidemic and interepidemic dengue transmission, utilizing data from the 5th largest city in Brazil (Fortaleza), at fine spatial and temporal scales. METHODS/PRINCIPAL FINDINGS: Longitudinal models of monthly rates of confirmed dengue cases were used to estimate the differential contribution of contextual factors to dengue transmission in Fortaleza between 2011 and 2015. Models were stratified by annual climatological schedules and periods of interepidemic and epidemic transmission, controlling for social, economic, structural, entomological, and environmental factors. Results revealed distinct seasonal patterns between interepidemic and epidemic years, with persistent transmission after June in interepidemic years. Dengue was strongly associated with violence across strata, and with poverty and irregular garbage collection during periods of low transmission, but not with other indicators of public service provision or structural deprivation. Scrapyards and sites associated with tire storage were linked to incidence differentially between seasons, with the strongest associations during transitional precipitation periods. Hierarchical clustering analysis suggests that the dengue burden concentrates in the southern periphery of the city, particularly during periods of minimal transmission. CONCLUSIONS/SIGNIFICANCE: Our findings have direct programmatic implications. Vector control operations must be sustained after June even in non-epidemic years. More specifically, scrapyards and sites associated with tires (strongly associated with incidence during periods of minimal transmission), require sustained entomological surveillance, particularly during interepidemic intervals and in the urban periphery. Intersectoral collaborations that address urban violence are critical for facilitating the regular activities of vector control agents.

Determinants of tuberculosis transmission and treatment abandonment in Fortaleza, Brazil.

BACKGROUND: Tuberculosis (TB) remains a public health problem, despite recent achievements in reducing incidence and mortality rates. In Brazil, these achievements were above the worldwide average, but marked by large regional heterogeneities. In Fortaleza (5th largest city in Brazil), the tuberculosis cure rate has been declining and treatment abandonment has been increasing in the past decade, despite a reduction in incidence and an increase in directly observed therapy (DOT). These trends put efforts to eliminate tuberculosis at risk. We therefore sought to determine social and programmatic determinants of tuberculosis incidence and treatment abandonment in Fortaleza. METHODS: We analyzed sociodemographic and clinical data for all new tuberculosis cases notified in the Notifiable Diseases Information System (SINAN) from Fortaleza between 2007 and 2014. We calculated incidence rates for 117 neighborhoods in Fortaleza, assessed their spatial clustering, and used spatial regression models to quantify associations between neighborhood-level covariates and incidence rates. We used hierarchical logistic regression models to evaluate how individual- and neighborhood-level covariates predicted tuberculosis treatment abandonment. RESULTS: There were 12,338 new cases reported during the study period. Case rates across neighborhoods were significantly positively clustered in two low-income areas close to the city center. In an adjusted model, tuberculosis rates were significantly higher in neighborhoods with lower literacy, higher sewerage access and homicide rates, and a greater proportion of self-reported black residents. Treatment was abandoned in 1901 cases (15.4%), a rate that rose by 71% between 2007 and 2014. Abandonment was significantly associated with many individual sociodemographic and clinical factors. Notably, being recommended for DOT was protective for those who completed DOT, but associated with abandonment for those who did not. CONCLUSION: Low socioeconomic status areas have higher tuberculosis rates, and low socioeconomic individuals have higher risk of treatment abandonment, in Fortaleza. Treatment abandonment rates are growing despite the advent of universal DOT recommendations in Brazil. Proactive social policies, and active contact tracing to find missed cases, may help reduce the tuberculosis burden in this setting.