Spatial dynamics of the COVID-19 pandemic in Brazil

High-risk spatiotemporal patterns of leprosy in the southeastern region of Yunnan province from 2010 to 2022: an analysis at the township level

BACKGROUND

Despite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022.

METHODS

Geographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran's I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters.

RESULTS

Between 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013.

CONCLUSIONS

In this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy.

Correlates of prolonged grief, posttraumatic stress and depression symptoms in Brazilian COVID-19 bereaved adults

The Coronavirus pandemic has hit Brazil exceptionally hard, with more than 700.000 confirmed deaths due to COVID-19, corresponding to an estimated 6.3 million bereaved people. Yet, the mental health consequences among COVID-19 bereaved Brazilians, and the associated loss-related variables have been largely unexplored. Therefore, we aimed to clarify the associations of loss-related characteristics and circumstances with prolonged grief, posttraumatic stress, and depression symptoms experienced by COVID-19-bereaved Brazilian adults. A sample of 371 Brazilian COVID-19 bereaved adults (90% women) completed an online survey. The loss of a partner or first-degree relative, a positive assessment of the healthcare received by the deceased, and the perceived helpfulness of hospital visits in the grief process significantly correlated with prolonged grief and posttraumatic stress symptoms. The findings suggest that farewell ceremonies and positive hospital care experiences may mitigate distress among COVID-19-bereaved Brazilian adults.

The effect of public health interventions on COVID-19 incidence in Queensland, Australia: a spatial cluster analysis

BACKGROUND

Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements.

METHODS

Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases.

RESULTS

Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001.

CONCLUSIONS

Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.

A Bayesian spatio-temporal model of COVID-19 spread in England

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005-1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024-1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129-1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009-1.0036], percentage of adults aged 45-64 years old [RR = 1.0031, 95% CI 1.0024-1.0039], and particulate matter ( PM 2.5 ) concentrations [RR = 1.0126, 95% CI 1.0083-1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University

The impact of the COVID-19 pandemic on University students has been a topic of fiery debate and of public health research. This study demonstrates the use of a combination of spatiotemporal epidemiological models to describe the trends in COVID-19 positive cases on spatial, temporal and spatiotemporal scales. In addition, this study proposes new epidemiological metrics to describe the connectivity between observed positivity; an analogous metric to the R number in conventional epidemiology. The proposed indices, Rspatial, Rspatiotemporal and Rscaling will aim to improve the characterisation of the spread of infectious disease beyond that of the COVID-19 framework and as a result inform relevant public health policy. Apart from demonstrating the application of the novel epidemiological indices, the key findings in this study are: firstly, there were some Intermediate Zones in Edinburgh with noticeably high levels of COVID-19 positivity, and that the first outbreak during the study period was observed in Dalry and Fountainbridge. Secondly, the estimation of the distance over which the COVID-19 counts at the halls of residence are spatially correlated (or related to each other) was found to be 0.19km (0.13km to 0.27km) and is denoted by the index, Rspatial. This estimate is useful for public health policy in this setting, especially with contact tracing. Thirdly, the study indicates that the association between the surrounding community level of COVID-19 positivity (Intermediate Zones in Edinburgh) and that of the University of Edinburgh's halls of residence was not statistically significant. Fourthly, this study reveals that relatively high levels of COVID-19 positivity were observed for halls for which higher COVID-19 fines were issued (Spearman's correlation coefficient = 0.34), and separately, for halls which were non-ensuite relatively to those which were not (Spearman's correlation coefficient = 0.16). Finally, Intermediate Zones with the highest positivity were associated with student residences that experienced relatively high COVID-19 positivity (Spearman's correlation coefficient = 0.27).

Insights into parents' perceived worry before and during the COVID-19 pandemic in Australia: inequality and heterogeneity of influences

BACKGROUND

Excessive worry is an invisible disruptive force that has adverse health outcomes and may advance to other forms of disorder, such as anxiety or depression. Addressing worry and its influences is challenging yet crucial for informing public health policy.

METHODS

We examined parents' worries, influences, and variability before and during COVID-19 pandemic and across geography. Parents (n = 340) and their primary school-aged children from five Australian states completed an anonymous online survey in mid-2020. After literature review, we conceptualised the influences and performed a series of regression analyses.

RESULTS

Worry levels and the variables contributing to parents' worry varied before to during the pandemic. The proportion of parents who were "very worried all the time" increased by 14.6% in the early days of the pandemic. During the pandemic, ethnic background modified parents' worry and parents' history of daily distress symptoms was a significant contributor (p < 0.05). Excessive exposure to news remained significant both before and during the pandemic. The primary predictor of parents' worry before COVID-19 was perceived neighbourhood safety, while the main predictor during COVID-19 was financial risk due to income change. Some variable such as neighbourhood safety and financial risk varied in their contribution to worry across geographical regions. The proportion of worried children was higher among distraught parents.

CONCLUSION

Parents' worry during the health pandemic was not triggered by the health risks factors but by the financial risk due to income change. The study depicts inequality in the impact of COVID-19 by ethnic background. Different policies and reported virus case numbers across states may have modified the behaviour of variables contributing to the geography of parents' worry. Exposure to stressors before the COVID-19 pandemic may have helped parents develop coping strategies during stressful events. Parents are encouraged to limit their exposure to stressful news. We advocate for parents-specific tailored policies and emphasise the need for access to appropriate mental health resources for those in need. Advancing research in geographical modelling for mental health may aid in devising much-needed location-targeted interventions and prioritising resources in future events.

Resources availability and COVID-19 mortality among US counties

The COVID-19 was declared a pandemic by WHO on 03/2020 has claimed millions of lives worldwide. The US leads all countries in COVID-19-related deaths. Individual level (preexisting conditions and demographics) and county-level (availability of resources) factors have been attributed to increased risk of COVID-19-related deaths. This study builds on previous studies to assess the relationship between county-level resources and COVID-19 mortality among 2,438 US counties. We merged 2019 data from AHA, AHRF, and USA FACTS. The dependent variable was the total number of COVID-19-related deaths. Independent variables included county-level resources: (1) hospital staffing levels (FTE RNs, hospitalists, and intensivists) per 10,000 population; (2) hospital capacity (occupancy rate, proportion of teaching hospitals, and number of airborne infection control rooms per 10,000 population); and (3) macroeconomic resources [per capita income and location (urban/rural)]. We controlled for population 65+, racial/ethnic minority, and COVID-19 deaths per 1,000 population. A negative binomial regression was used. Hospital staffing per 10,000 population {FTE RN [IRR = 0.997; CI (0.995-0.999)], FTE hospitalists [IRR = 0.936; CI (0.897-0.978)], and FTE intensivists [IRR = 0.606; CI (0.516-0.712)]} was associated with lower COVID-19-related deaths. Hospital occupancy rate, proportion of teaching hospitals, and total number of airborne infection control rooms per 10,000 population were positively associated with COVID-19-related deaths. Per capita income and being in an urban county were positively associated with COVID-19-related deaths. Finally, the proportion of 65+, racial/ethnic minorities, and the number of cases were positively associated with COVID-19-related deaths. Our findings suggest that focusing on maintaining adequate hospital staffing could improve COVID-19 mortality.

Can the Content of Social Networks Explain Epidemic Outbreaks?

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior.

Associations Between Built Environment Factors and SARS-CoV-2 Infections at the Neighbourhood Level in a Metropolitan Area in Germany

COVID-19-related health outcomes displayed distinct geographical patterns within countries. The transmission of SARS-CoV-2 requires close spatial proximity of people, which can be influenced by the built environment. Only few studies have analysed SARS-CoV-2 infections related to the built environment within urban areas at a high spatial resolution. This study examined the association between built environment factors and SARS-CoV-2 infections in a metropolitan area in Germany. Polymerase chain reaction (PCR)-confirmed SARS-CoV-2 infections of 7866 citizens of Essen between March 2020 and May 2021 were analysed, aggregated at the neighbourhood level. We performed spatial regression analyses to investigate associations between the cumulative number of SARS-CoV-2 infections per 1000 inhabitants (cum. SARS-CoV-2 infections) up to 31.05.2021 and built environment factors. The cum. SARS-CoV-2 infections in neighbourhoods (median: 11.5, IQR: 8.1-16.9) followed a marked socially determined north-south gradient. The effect estimates of the adjusted spatial regression models showed negative associations with urban greenness, i.e. normalized difference vegetation index (NDVI) (adjusted β =  - 35.36, 95% CI: - 57.68; - 13.04), rooms per person (- 10.40, - 13.79; - 7.01), living space per person (- 0.51, - 0.66; - 0.36), and residential (- 0.07, 0.16; 0.01) and commercial areas (- 0.15, - 0.25; - 0.05). Residential areas with multi-storey buildings (- 0.03, - 0.12; 0.06) and green space (0.03, - 0.05; 0.11) did not show a substantial association. Our results suggest that the built environment matters for the spread of SARS-CoV-2 infections, such as more spacious apartments or higher levels of urban greenness are associated with lower infection rates at the neighbourhood level. The unequal intra-urban distribution of these factors emphasizes prevailing environmental health inequalities regarding the COVID-19 pandemic.

Social vulnerability and COVID-19 in Maringá, Brazil

This research explores the relationship between COVID-19 and social vulnerability on an intra-urban scale. For this, two composite indicators of social vulnerability have been constructed. The composite indicator constructed by the Benefit-of-the-Doubt considers spatial heterogeneity. It weakly captures the conceptually most significant individual indicator of social vulnerability (R =-0.39), as it overestimates the above-average performance sub-indicators. The composite indicator constructed by the Principal Component Analysis considers that the sub-indicators have the same weights in different census tracts, resulting in a highly consistent composite indicator as a multidimensional phenomenon concept (R =-0.93). These findings allow reaching four conclusions. First, the direction and strength of correlations associated with COVID-19 are sensitive to the method employed to construct the composite indicator and not just the geographic scale and space. Second, Medium and High social vulnerability census tracts concentrate 97% of the population but only 93% of COVID-19 cases and deaths. Third, people living in census tracts of None and Low social vulnerability are 3.87 and 2.13 times more likely to be infected or die from COVID-19. Fourth, policies to combat COVID-19 in the study area should prioritize older populations regardless of their social conditions.

Community vulnerability to the COVID-19 pandemic: A narrative synthesis from an ecological perspective

Background

We aimed to conduct a narrative synthesis of components and indicators of community vulnerability to a pandemic and discuss their interrelationships from an ecological perspective.

Methods

We searched from PubMed, Embase, Web of Science, PsycINFO, and Scopus (updated to November 2021) for studies focusing on community vulnerability to a pandemic caused by novel respiratory viruses on a geographic unit basis . Studies that reported the associations of community vulnerability levels with at least one disease morbidity or mortality outcome were included.

Results

Forty-one studies were included. All were about the COVID-19 pandemic. Suitable temperature and humidity environments, advanced social and human development (including high population density and human mobility, connectivity, and occupations), and settings that intensified physical interactions are important indicators of vulnerability to viral exposure. However, the eventual pandemic health impacts are predominant in communities that faced environmental pollution, higher proportions of socioeconomically deprived people, health deprivation, higher proportions of poor-condition households, limited access to preventive health care and urban infrastructure, uneven social and human development, and racism. More stringent social distancing policies were associated with lower COVID-19 morbidity and mortality only in the early pandemic phases. Prolonged social distancing policies can disproportionately burden the socially disadvantaged and racially/ethnically marginalized groups.

Conclusions

Community vulnerability to a pandemic is foremost the vulnerability of the ecological systems shaped by complex interactions between the human and environmental systems.

Registration

PROSPERO (CRD42021266186).

The correlation between attack rates and urban health indicators during the third wave of the COVID-19 outbreak in Turkey

This study aims to analyze the inter-provincial variation in the increase of attack rates in the third wave of the COVID-19 outbreak in Turkey and to determine their relationship with potential urban health indicators. In this ecological study, dependent variables were selected as the COVID-19 attack rates of provinces before the third wave and during the third peak and the attack rate increase ratio. Urban health indicators that can function as determinants of health were calculated for each province under five headings: demographic, health capacity, economic, environmental, and socio-cultural. The epidemiologic maps were produced to show the spatial distribution of COVID-19 attack rates pre- and during the third wave. The associations with urban indicators were conducted using bivariate analysis, including Pearson or Spearman correlation analysis. A multiple linear regression model was run with variables significantly associated with increased attack rates. The results of our study show significant regional variations in COVID-19 attack rates both at the beginning and during the third wave of the COVID-19 pandemic in Turkey. Among the provinces, the attack rate increase ratio has only shown significant correlations to education level and some economic indicators, such as income, employment, industrial activity measured by electric consumption, and economic activity in the manufacturing industry. The multivariate analysis determined that the indicator of economic activity in the manufacturing industry is related to the increase of the attack rate in the third wave. Our results show that the COVID-19 cases are higher in more developed cities with more manufacturing sector activity. It makes us think that it is mainly related to inequalities arising from access to health institutions and testing. It can be determined that the partly lockdown strategy, which excluded the industrial activity in the country, concluded the higher increase in the attack rates in highly industrialized provinces.

Influence of Population Size, the Human Development Index and the Gross Domestic Product on Mortality by COVID-19 in the Southeast Region of Brazil

We evaluated the influence of population size (POP), HDI (Human Development Index) and GDP (gross domestic product) on the COVID-19 pandemic in the Southeast region of Brazil, between February 2020 and May 2021.

METHODS

Cases, deaths, incidence coefficient, mortality rate and lethality rate were compared among states. The cities were divided into strata according to POP, GDP, and HDI. Data were compared by Welch's ANOVA, nonlinear polynomial regression, and Spearman's correlation test (rS).

RESULTS

The highest incidence coefficient (p < 0.0001) and mortality rate (p < 0.05) were observed in the states of Espírito Santo and Rio de Janeiro, respectively. Until the 45th week, the higher the POP, the higher the mortality rate (p < 0.01), with no differences in the remaining period (p > 0.05). There was a strong positive correlation between POP size and the number of cases (rS = 0.92, p < 0.0001) and deaths (rS = 0.88, p < 0.0001). The incidence coefficient and mortality rate were lower (p < 0.0001) for low GDP cities. Both coefficients were higher in high- and very high HDI cities (p < 0.0001). The lethality rate was higher in the state of Rio de Janeiro (p < 0.0001), in large cities (p < 0.0001), in cities with medium GDP (p < 0.0001), and in those with high HDI (p < 0.05).

CONCLUSIONS

Both incidence and mortality were affected by time, with minimal influence of POP, GDP and HDI.

COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities

COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.

COVID-19 spatialization by empirical Bayesian model in São Paulo, Brazil

The new Acute Respiratory Syndrome, COVID-19, has affected the health and the economy worldwide. Therefore, scientists have been looking for ways to understand this disease. In this context, the main objective of this study was the spatialization of COVID-19, thinking in distinguishing areas with high transmissibility yet, verifying if these areas were associated with the elderly population occurrence. The work was delineated, supposing that spatialization could support the decision-making to combat the outbreak and that the same method could be used for spatialization and prevent other diseases. The study area was a municipality near Sao Paulo Metropolis, one of Brazil's main disease epicenters. Using official data and an empirical Bayesian model, we spatialized people infected by region, including older people, obtaining reasonable adjustment. The results showed a weak correlation between regions infected and older adults. Thus, we define a robust model that can support the definition of actions aiming to control the COVID-19 spread.

Structural factors associated with SARS-CoV-2 infection risk in an urban slum setting in Salvador, Brazil: A cross-sectional survey

BACKGROUND

The structural environment of urban slums, including physical, demographic, and socioeconomic attributes, renders inhabitants more vulnerable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Yet, little is known about the specific determinants that contribute to high transmission within these communities. We therefore aimed to investigate SARS-CoV-2 seroprevalence in an urban slum in Brazil.

METHODS AND FINDINGS

We performed a cross-sectional serosurvey of an established cohort of 2,041 urban slum residents from the city of Salvador, Brazil between November 2020 and February 2021, following the first Coronavirus Disease 2019 (COVID-19) pandemic wave in the country and during the onset of the second wave. The median age in this population was 29 years (interquartile range [IQR] 16 to 44); most participants reported their ethnicity as Black (51.5%) or Brown (41.7%), and 58.5% were female. The median size of participating households was 3 (IQR 2 to 4), with a median daily per capita income of 2.32 (IQR 0.33-5.15) US Dollars. The main outcome measure was presence of IgG against the SARS-CoV-2 spike protein. We implemented multilevel models with random intercepts for each household to estimate seroprevalence and associated risk factors, adjusting for the sensitivity and specificity of the assay, and the age and gender distribution of our study population. We identified high seroprevalence (47.9%, 95% confidence interval [CI] 44.2% to 52.1%), particularly among female residents (50.3% [95% CI 46.3% to 54.8%] versus 44.6% [95% CI 40.1% to 49.4%] among male residents, p < 0.01) and among children (54.4% [95% CI 49.6% to 59.3%] versus 45.4% [95% CI 41.5% to 49.7%] among adults, p < 0.01). Adults residing in households with children were more likely to be seropositive (48.6% [95% CI 44.8% to 52.3%] versus 40.7% [95% CI 37.2% to 44.3%], p < 0.01). Women who were unemployed and living below the poverty threshold (daily per capita household income <$1.25) were more likely to be seropositive compared to men with the same employment and income status (53.9% [95% CI 47.0% to 60.6%] versus 32.9% [95% CI 23.2% to 44.3%], p < 0.01). Participation in the study was voluntary, which may limit the generalizability of our findings.

CONCLUSIONS

Prior to the peak of the second wave of the COVID-19 pandemic, cumulative incidence as assessed by serology approached 50% in a Brazilian urban slum population. In contrast to observations from industrialized countries, SARS-CoV-2 incidence was highest among children, as well as women living in extreme poverty. These findings emphasize the need for targeted interventions that provide safe environments for children and mitigate the structural risks posed by crowding and poverty for the most vulnerable residents of urban slum communities.

What Makes Urban Communities More Resilient to COVID-19? A Systematic Review of Current Evidence

It has been more than two years since the outbreak of the COVID-19 epidemic at the end of 2019. Many scholars have introduced the "resilience" concept into COVID-19 prevention and control to make up for the deficiencies in traditional community governance. This study analyzed the progress in research on social resilience, which is an important component of community resilience, focusing on the current literature on the impact of social resilience on COVID-19, and proposed a generalized dimension to integrated previous relevant literature. Then, VOSviewer was used to visualize and analyze the current progress of research on social resilience. The PRISMA method was used to collate studies on social resilience to the pandemic. The result showed that many current policies are effective in controlling COVID-19, but some key factors, such as vulnerable groups, social assistance, and socioeconomics, affect proper social functioning. Some scholars have proposed effective solutions to improve social resilience, such as establishing an assessment framework, identifying priority inoculation groups, and improving access to technology and cultural communication. Social resilience to COVID-19 can be enhanced by both external interventions and internal regulation. Social resilience requires these two aspects to be coordinated to strengthen community and urban pandemic resilience.

COVID-19 no Brasil: tendências, desafios e perspectivas após 18 meses de pandemia

Objetivo.

Analisar as tendências de incidência e mortalidade por COVID-19 no Brasil, nas unidades da federação e nas capitais.

Método.

Realizou-se um estudo ecológico com dados de incidência e de mortalidade por COVID-19 referentes ao período de 25 de fevereiro de 2020 (primeiro caso notificado no Brasil) a 31 de julho de 2021. Os dados foram agrupados por mês para cálculo das taxas brutas (por 100 000 habitantes) e avaliação das tendências temporais das unidades da federação e de suas capitais. As modificações significativas nas tendências temporais foram analisadas pelo método de regressão por joinpoint.

Resultados.

Foram identificadas duas ondas de novos casos e óbitos. As unidades da federação com as maiores taxas de incidência foram Amapá, Rio Grande do Norte, Rondônia e Roraima; Amazonas e Rondônia tiveram as maiores taxas de mortalidade. Em geral, as taxas de incidência e mortalidade foram piores na segunda onda. Na primeira onda, a média de meses até o início de uma redução de casos novos foi maior nas capitais, enquanto na segunda onda, o início da redução demorou mais nos estados. Quanto aos óbitos, as capitais necessitaram de menos tempo para apresentar redução tanto na primeira quanto na segunda onda.

Conclusão.

A heterogeneidade regional detectada reforça a ideia de que a incidência e a mortalidade por COVID-19 estão associadas a fatores políticos, geográficos, culturais, sociais e econômicos.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Spatiotemporal pattern of Covid-19 outbreak in Turkey

The earliest case of Covid-19 was documented in Wuhan city of China and since then the virus has been spreading throughout the globe. The aim of this study is to evaluate the clusters of Covid-19 among the provinces in Turkey and to examine whether the clustering pattern has changed after the country's lockdown strategy. The spatial dependence of Covid-19 in 81 provinces of Turkey was examined by spatial analysis between February 8 and June 28, 2021. Global and Local Moran's I and Gi* were employed to measure the global and local spatial autocorrelation degrees. The geographical distribution of Covid-19 in the provinces of Turkey showed a strong spatial autocorrelation while the spatial structure of the clusters varied by weeks. The findings of the study show that the complete lockdown carried out in Turkey has been quite effective in mitigating Covid-19. The importance of spatial relations in preventing the spread of the disease in Turkey has also been demonstrated in this context.

Genomic Epidemiology of SARS-CoV-2 in Tocantins State and the Diffusion of P.1.7 and AY.99.2 Lineages in Brazil

Tocantins is a state in the cross-section between the Central-West, North and Northeast regions of Brazilian territory; it is a gathering point for travelers and transportation from the whole country. In this study, 9493 genome sequences, including 241 local SARS-CoV-2 samples (collected from 21 December 2020, to 16 December 2021, and sequenced in the MinION platform) were analyzed with the following aims: (i) identify the relative prevalence of SARS-CoV-2 lineages in the state of Tocantins; (ii) analyze them phylogenetically against global SARS-CoV-2 sequences; and (iii) hypothesize the viral dispersal routes of the two most abundant lineages found in our study using phylogenetic and phylogeographic approaches. The performed analysis demonstrated that the majority of the strains sequenced during the period belong to the Gamma P.1.7 (32.4%) lineage, followed by Delta AY.99.2 (27.8%), with the first detection of VOC Omicron. As expected, there was mainly a dispersion of P.1.7 from the state of São Paulo to Tocantins, with evidence of secondary spreads from Tocantins to Goiás, Mato Grosso, Amapá, and Pará. Rio de Janeiro was found to be the source of AY.99.2 and from then, multiple cluster transmission was observed across Brazilian states, especially São Paulo, Paraiba, Federal District, and Tocantins. These data show the importance of trade routes as pathways for the transportation of the virus from Southeast to Northern Brazil.

Structural factors contributing to SARS-CoV-2 infection risk in the urban slum setting

BACKGROUND

The structural environment of urban slums, including physical, demographic and socioeconomic attributes, renders inhabitants more vulnerable to SARS-CoV-2 infection. Yet, little is known about the specific determinants that contribute to high transmission within these communities.

METHODS AND FINDINGS

We performed a serosurvey of an established cohort of 2,035 urban slum residents from the city of Salvador, Brazil between November 2020 and February 2021, following the first COVID-19 pandemic wave in the country. We identified high SARS-CoV-2 seroprevalence (46.4%, 95% confidence interval [CI] 44.3-48.6%), particularly among female residents (48.7% [95% CI 45.9-51.6%] vs. 43.2% [95% CI 39.8-46.6%] among male residents), and among children (56.5% [95% CI 52.3-60.5%] vs. 42.4% [95% CI 39.9-45.0%] among adults). In multivariable models that accounted for household-level clustering, the odds ratio for SARS-CoV-2 seropositivity among children was 1.96 (95% CI 1.42-2.72) compared to adults aged 30-44 years. Adults residing in households with children were more likely to be seropositive; this effect was particularly prominent among individuals with age 30-44 and 60 years or more. Women living below the poverty threshold (daily per capita household income <$1.25) and those who were unemployed were more likely to be seropositive.

CONCLUSIONS

During a single wave of the COVID-19 pandemic, cumulative incidence as assessed by serology approached 50% in a Brazilian urban slum population. In contrast to observations from industrialized countries, SARS-CoV-2 incidence was highest among children, as well as women living in extreme poverty. These findings emphasize the need for targeted interventions that provide safe environments for children and mitigate the structural risks posed by crowding and poverty for the most vulnerable residents of urban slum communities.

Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia

As COVID-19 dispersion occurs at different levels of gradients across geographies, the application of spatiotemporal science via computational methods can provide valuable insights to direct available resources and targeted interventions for transmission control. This ecological-correlation study evaluates the spatial dispersion of COVID-19 and its temporal relationships with crucial demographic and socioeconomic determinants in Malaysia, utilizing secondary data sources from public domains. By aggregating 51,476 real-time active COVID-19 case-data between 22 January 2021 and 4 February 2021 to district-level administrative units, the incidence, global and local Moran indexes were calculated. Spatial autoregressive models (SAR) complemented with geographical weighted regression (GWR) analyses were executed to determine potential demographic and socioeconomic indicators for COVID-19 spread in Malaysia. Highest active case counts were based in the Central, Southern and parts of East Malaysia regions of Malaysia. Countrywide global Moran index was 0.431 (p = 0.001), indicated a positive spatial autocorrelation of high standards within districts. The local Moran index identified spatial clusters of the main high–high patterns in the Central and Southern regions, and the main low–low clusters in the East Coast and East Malaysia regions. The GWR model, the best fit model, affirmed that COVID-19 spread in Malaysia was likely to be caused by population density (β coefficient weights = 0.269), followed by average household income per capita (β coefficient weights = 0.254) and GINI coefficient (β coefficient weights = 0.207). The current study concluded that the spread of COVID-19 was concentrated mostly in the Central and Southern regions of Malaysia. Population’s average household income per capita, GINI coefficient and population density were important indicators likely to cause the spread amongst communities.

Spatiotemporal Analysis of Covid-19 in Turkey

The Covid-19 pandemic continues to threaten public health around the world. Understanding the spatial dimension of this impact is very important in terms of controlling and reducing the spread of the pandemic. This study measures the spatial association of the Covid-19 outbreak in Turkey between February 8 and May 28, 2021 and reveals its spatiotemporal pattern. In this context, global and local spatial autocorrelation was used to determine whether there is a spatial association of Covid-19 infections, while the spatial regression model was employed to reveal the geographical relationship of the potential factors affecting the number of Covid-19 cases. As a result of the analyzes made in this context, it has been observed that there are spatial associations and distinct spatial clusters in Covid-19 cases at the provincial level in Turkey. The results of the spatial regression model showed that population density and elderly dependency ratio are very important in explaining the model of Covid-19 case numbers. Additionally, it has been revealed that Covid-19 is affected by the Covid-19 numbers of neighboring provinces, apart from the said explanatory variables. The findings of the study revealed that spatial analysis is helpful in understanding the spread of the pandemic in Turkey. It has been determined that geographical location is an important factor to be considered in the investigation of the factors affecting Covid-19.

Geographical variation in demand, utilization, and outcomes of hospital services for COVID-19 in Brazil: A descriptive serial cross-sectional study

Objective

To analyze the geographical variation in the provision of health services, namely in demand, patterns of utilization, and effectiveness in the Brazilian Health Regions in four different periods of the COVID-19 pandemic, from February 2020 to March 2021.

Methods

Descriptive serial cross-sectional study based on secondary data on COVID-19 hospitalizations from SIVEP-Gripe, a public and open-access database of Severe Acute Respiratory Illness records collected by the Brazilian Ministry of Health, and COVID-19 case notification data from Brasil.io, a repository of public data. Fifty-six epidemiological weeks were split into four periods. The following variables were considered for each Brazilian Health Region, per period: number of hospitalizations, hospitalizations per 100,000 inhabitants, hospitalizations per 100 new cases notified in the Health Region, percentage of hospitalizations with ICU use, percentages of hospitalizations with invasive and non-invasive ventilatory support, percentage of hospitalizations resulting in death and percentage of hospitalizations with ICU use resulting in death. Descriptive statistics of the variables were obtained across all 450 Health Regions in Brazil over the four defined pandemic periods. Maps were generated to capture the spatiotemporal variation and trends during the first year of the COVID-19 pandemic in Brazil.

Results

There was great variation in how COVID-19 hospitalizations grew and spread among Health Regions, with higher numbers between June and August 2020, and, especially, from mid-December 2020 to March 2021. The variation pattern in the proportion of ICU use in the hospitalizations across the Health Regions was broad, with no intensive care provision in large areas in the North, Northeast, and Midwest. The proportions of hospitalizations and hospitalizations with ICU use resulting in deaths were remarkably high, reaching medians of 34.0% and 62.0% across Health Regions, respectively.

Conclusion

The Heath Regions in Brazil are highly diverse, showing broad disparities in the capacity to respond to the demands imposed by COVID-19, services provided, use and outcomes.

COVID-19 in medium-sized municipalities in the 14 health macro-regions of Minas Gerais, Brazil

The present study focused on the scenario of confirmed cases of SARS-CoV-2 infection in the state of Minas Gerais (MG), Brazil, from March 2020 to March 2021. We evaluated the evolution of COVID-19 prevalence and death in one municipality from each of the 14 health macro-regions of MG state. Socio-demographic characteristics and variables related to the municipalities were analyzed. The raw dataset used in this study was freely sourced from the website Brasil.io. From the raw dataset, two time series were extracted: the cumulative confirmed cases of COVID-19 and cumulative death counts, and they were compared to the state data using a nowcasting approach. In order to make time series comparisons possible, all data was normalized per 100,000 inhabitants. When analyzing in light of colored wave code interventions initiated in August 2020 in MG, for the majority of the municipalities, there was an absence of clear influence on prevalence and deaths. The national holidays in the first semester of 2020 had a small impact on the COVID-19 prevalence of the municipalities, but the holidays in the second semester of 2020 and beginning of 2021 caused important impacts on COVID-19 prevalence. The low number of ICU beds in some municipalities contributed to the higher number of deaths. The analysis showed here is expected to contribute to the improvement of decision making of the MG government, as it opened a huge possibility to have the total macro-regions and state data analyzed.