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1.
Int J Environ Res Public Health ; 19(1)2022 01 05.
Article in English | MEDLINE | ID: covidwho-1613774

ABSTRACT

In 2019, a novel coronavirus, SARS-CoV-2, was first reported in Wuhan, China. The virus causes the disease commonly known as COVID-19, and, since its emergence, it has infected over 252 million individuals globally and taken the lives of over 5 million in the same time span. Primary research on SARS-CoV-2 and COVID-19 focused on understanding the biomolecular composition of the virus. This research has led to the development of multiple vaccines with great efficacy and antiviral treatments for the disease. The development of biomedical interventions has been crucial to combating this pandemic; additionally, environmental confounding variables that could have exacerbated the pandemic need further assessment. In this research study, we conducted a spatial analysis of particulate matter (PM) concentration and its association with COVID-19 mortality in the United States. Results of this study demonstrate a significant positive correlation between PM concentration levels and COVID-19 mortality; however, this does not necessarily imply a causal relationship. These results are consistent with similar studies in Italy and China, where significant COVID-19 cases and corresponding deaths were exhibited. Furthermore, maps of the data demonstrate clustering of COVID-19 mortality which suggest further investigation into the social determinants of health impacting the pandemic.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/adverse effects , Air Pollution/analysis , Humans , Pandemics , Particulate Matter/analysis , Particulate Matter/toxicity , SARS-CoV-2 , Spatial Analysis
2.
JAMA Netw Open ; 5(1): e2142046, 2022 01 04.
Article in English | MEDLINE | ID: covidwho-1605268

ABSTRACT

Importance: The COVID-19 pandemic has had a distinct spatiotemporal pattern in the United States. Patients with cancer are at higher risk of severe complications from COVID-19, but it is not well known whether COVID-19 outcomes in this patient population were associated with geography. Objective: To quantify spatiotemporal variation in COVID-19 outcomes among patients with cancer. Design, Setting, and Participants: This registry-based retrospective cohort study included patients with a historical diagnosis of invasive malignant neoplasm and laboratory-confirmed SARS-CoV-2 infection between March and November 2020. Data were collected from cancer care delivery centers in the United States. Exposures: Patient residence was categorized into 9 US census divisions. Cancer center characteristics included academic or community classification, rural-urban continuum code (RUCC), and social vulnerability index. Main Outcomes and Measures: The primary outcome was 30-day all-cause mortality. The secondary composite outcome consisted of receipt of mechanical ventilation, intensive care unit admission, and all-cause death. Multilevel mixed-effects models estimated associations of center-level and census division-level exposures with outcomes after adjustment for patient-level risk factors and quantified variation in adjusted outcomes across centers, census divisions, and calendar time. Results: Data for 4749 patients (median [IQR] age, 66 [56-76] years; 2439 [51.4%] female individuals, 1079 [22.7%] non-Hispanic Black individuals, and 690 [14.5%] Hispanic individuals) were reported from 83 centers in the Northeast (1564 patients [32.9%]), Midwest (1638 [34.5%]), South (894 [18.8%]), and West (653 [13.8%]). After adjustment for patient characteristics, including month of COVID-19 diagnosis, estimated 30-day mortality rates ranged from 5.2% to 26.6% across centers. Patients from centers located in metropolitan areas with population less than 250 000 (RUCC 3) had lower odds of 30-day mortality compared with patients from centers in metropolitan areas with population at least 1 million (RUCC 1) (adjusted odds ratio [aOR], 0.31; 95% CI, 0.11-0.84). The type of center was not significantly associated with primary or secondary outcomes. There were no statistically significant differences in outcome rates across the 9 census divisions, but adjusted mortality rates significantly improved over time (eg, September to November vs March to May: aOR, 0.32; 95% CI, 0.17-0.58). Conclusions and Relevance: In this registry-based cohort study, significant differences in COVID-19 outcomes across US census divisions were not observed. However, substantial heterogeneity in COVID-19 outcomes across cancer care delivery centers was found. Attention to implementing standardized guidelines for the care of patients with cancer and COVID-19 could improve outcomes for these vulnerable patients.


Subject(s)
COVID-19/epidemiology , Neoplasms/epidemiology , Pandemics , Rural Population , Social Vulnerability , Urban Population , Aged , Cause of Death , Censuses , Female , Health Facilities , Humans , Intensive Care Units , Male , Middle Aged , Odds Ratio , Registries , Respiration, Artificial , Retrospective Studies , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Spatial Analysis , United States/epidemiology
3.
Sensors (Basel) ; 21(24)2021 Dec 08.
Article in English | MEDLINE | ID: covidwho-1592974

ABSTRACT

The encroachment of wild boars into urban areas is a growing problem. The occurrence of wild boars in cities leads to conflict situations. Socio-spatial conflicts can escalate to a varied degree. Assessments of these conflicts can be performed by analyzing spatial data concerning the affected locations and wild boar behaviors. The collection of spatial data is a laborious and costly process that requires access to urban surveillance systems, in addition to regular analyses of intervention reports. A supporting method for assessing the risk of wild boar encroachment and socio-spatial conflict in cities was proposed in the present study. The developed approach relies on big data, namely, multimedia and descriptive data that are on social media. The proposed method was tested in the city of Olsztyn in Poland. The main aim of this study was to evaluate the applicability of data crowdsourced from a popular social networking site for determining the location and severity of conflicts. A photointerpretation method and the kernel density estimation (KDE) tool implemented in ArcGIS Desktop 10.7.1 software were applied in the study. The proposed approach fills a gap in the application of crowdsourcing data to identify types of socio-spatial conflicts involving wild boars in urban areas. Validation of the results with reports of calls to intervention services showed the high coverage of this approach and thus the usefulness of crowdsourcing data.


Subject(s)
Social Media , Sus scrofa , Animals , Cities , Humans , Poland , Spatial Analysis , Swine
4.
PLoS One ; 16(3): e0247794, 2021.
Article in English | MEDLINE | ID: covidwho-1575402

ABSTRACT

BACKGROUND: Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country. METHODS: This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). FINDINGS: The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. DISCUSSION: Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.


Subject(s)
COVID-19/epidemiology , Nurses/statistics & numerical data , Brazil/epidemiology , COVID-19/diagnosis , COVID-19/mortality , Cities/epidemiology , Demography , Female , Humans , Incidence , Male , Risk Factors , Socioeconomic Factors , Spatial Analysis , Spatial Regression
5.
Transbound Emerg Dis ; 68(6): 3643-3657, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1526427

ABSTRACT

The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40 years age). The incidence varies from 0.03 - 0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p ≤ 0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.


Subject(s)
COVID-19 , Animals , Bangladesh/epidemiology , COVID-19/veterinary , Pandemics , Retrospective Studies , SARS-CoV-2 , Spatial Analysis
6.
Elife ; 102021 10 15.
Article in English | MEDLINE | ID: covidwho-1518778

ABSTRACT

Simulating nationwide realistic individual movements with a detailed geographical structure can help optimise public health policies. However, existing tools have limited resolution or can only account for a limited number of agents. We introduce Epidemap, a new framework that can capture the daily movement of more than 60 million people in a country at a building-level resolution in a realistic and computationally efficient way. By applying it to the case of an infectious disease spreading in France, we uncover hitherto neglected effects, such as the emergence of two distinct peaks in the daily number of cases or the importance of local density in the timing of arrival of the epidemic. Finally, we show that the importance of super-spreading events strongly varies over time.


Subject(s)
COVID-19/epidemiology , Communicable Diseases/epidemiology , Epidemics/statistics & numerical data , Geography/methods , Public Health/methods , France/epidemiology , Humans , Public Health/instrumentation , Spatial Analysis
7.
Spat Spatiotemporal Epidemiol ; 39: 100461, 2021 11.
Article in English | MEDLINE | ID: covidwho-1510319

ABSTRACT

With the whole world being affected by the pandemic, it is a matter of great importance that studies about spatial and spatio-temporal aspects of the COVID-19 (Sars-Cov-2) pandemic should be conducted, therefore the main goal of this paper is to present the Global Moran's I and the Local Moran's I used to evaluate spatial association in the number of deaths and infections by COVID-19, and a spatio-temporal Poisson scan statistic used to identify emerging or "alive" clusters of infections by Sars-Cov-2 in space and time. As of January 2021 vaccination against COVID-19 already started, since the use of spatial clustering methods to identify non-vaccinated populations is not new among studies on vaccination coverage strategies, this paper also aims to discuss the implementation of spatial and spatio-temporal clustering methods in early vaccination.


Subject(s)
COVID-19 , Cluster Analysis , Humans , SARS-CoV-2 , Spatial Analysis , Spatio-Temporal Analysis , Vaccination
8.
Infect Dis Poverty ; 10(1): 118, 2021 Sep 16.
Article in English | MEDLINE | ID: covidwho-1496234

ABSTRACT

BACKGROUND: There are only limited studies on access to COVID-19 vaccines and identifying the most appropriate health centres for performing vaccination in metropolitan areas. This study aimed to measure potential spatial access to COVID-19 vaccination centres in Mashhad, the second-most populous city in Iran. METHODS: The 2021 age structure of the urban census tracts was integrated into the enhanced two-step floating catchment area model to improve accuracy. The model was developed based on three different access scenarios: only public hospitals, only public healthcare centres and both (either hospitals or healthcare centres) as potential vaccination facilities. The weighted decision-matrix and analytic hierarchy process, based on four criteria (i.e. service area, accessibility index, capacity of vaccination centres and distance to main roads), were used to choose potential vaccination centres looking for the highest suitability for residents. Global Moran's index (GMI) was used to measure the spatial autocorrelation of the accessibility index in different scenarios and the proposed model. RESULTS: There were 26 public hospitals and 271 public healthcare centres in the study area. Although the exclusive use of public healthcare centres for vaccination can provide the highest accessibility in the eastern and north-eastern parts of the study area, our findings indicate that including both public hospitals and public healthcare centres provide high accessibility to vaccination in central urban part. Therefore, a combination of public hospitals and public healthcare centres is recommended for efficient vaccination coverage. The value of GMI for the proposed model (accessibility to selected vaccination centres) was calculated as 0.53 (Z = 162.42, P < 0.01). Both GMI and Z-score values decreased in the proposed model, suggesting an enhancement in accessibility to COVID-19 vaccination services. CONCLUSIONS: The periphery and poor areas of the city had the least access to COVID-19 vaccination centres. Measuring spatial access to COVID-19 vaccination centres can provide valuable insights for urban public health decision-makers. Our model, coupled with geographical information systems, provides more efficient vaccination coverage by identifying the most suitable healthcare centres, which is of special importance when only few centres are available.


Subject(s)
COVID-19 Vaccines , COVID-19 , Health Services Accessibility , Vaccination Coverage , Delivery of Health Care , Healthcare Disparities , Humans , Iran , SARS-CoV-2 , Spatial Analysis
9.
Health Econ ; 31(1): 154-173, 2022 01.
Article in English | MEDLINE | ID: covidwho-1479404

ABSTRACT

This paper examines the propagation of COVID-19 across the Spanish provinces and assesses the effectiveness of the Spanish lockdown of the population implemented on March 14, 2020 in order to battle this pandemic. To achieve these objectives, a standard spatial econometric model used in economics is adapted to resemble the popular reproduction models employed in the epidemiological literature. In addition, we introduce a counterfactual exercise that allows us to examine the Gross domestic product (GDP) gains of bringing forward the date of the Spanish Lockdown. We find that the number of COVID-19 cases would have been reduced by 70.4% in the absence of spatial propagation between the Spanish provinces. We also determine that the lockdown prevented the propagation of the virus within and between provinces. As such, the Spanish lockdown reduced the number of potential COVID-19 cases by 82.8%. However, the number of coronavirus cases would have been reduced by an additional 11.6% if the lockdown had been brought forward to March 7, 2020. Finally, an earlier lockdown would have saved approximately 26,900,000,000 euros.


Subject(s)
COVID-19 , Communicable Disease Control , Humans , Reproduction , SARS-CoV-2 , Spatial Analysis
10.
Int J Environ Res Public Health ; 18(20)2021 10 14.
Article in English | MEDLINE | ID: covidwho-1470842

ABSTRACT

The ongoing highly contagious coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China, in December 2019, has now become a global public health problem. Using publicly available data from the COVID-19 data repository of Our World in Data, we aimed to investigate the influences of spatial socio-economic vulnerabilities and neighbourliness on the COVID-19 burden in African countries. We analyzed the first wave (January-September 2020) and second wave (October 2020 to May 2021) of the COVID-19 pandemic using spatial statistics regression models. As of 31 May 2021, there was a total of 4,748,948 confirmed COVID-19 cases, with an average, median, and range per country of 101,041, 26,963, and 2191 to 1,665,617, respectively. We found that COVID-19 prevalence in an Africa country was highly dependent on those of neighbouring Africa countries as well as its economic wealth, transparency, and proportion of the population aged 65 or older (p-value < 0.05). Our finding regarding the high COVID-19 burden in countries with better transparency and higher economic wealth is surprising and counterintuitive. We believe this is a reflection on the differences in COVID-19 testing capacity, which is mostly higher in more developed countries, or data modification by less transparent governments. Country-wide integrated COVID suppression strategies such as limiting human mobility from more urbanized to less urbanized countries, as well as an understanding of a county's social-economic characteristics, could prepare a country to promptly and effectively respond to future outbreaks of highly contagious viral infections such as COVID-19.


Subject(s)
COVID-19 , Pandemics , Africa/epidemiology , COVID-19 Testing , Humans , SARS-CoV-2 , Socioeconomic Factors , Spatial Analysis
11.
Ann Intern Med ; 174(7): 936-944, 2021 07.
Article in English | MEDLINE | ID: covidwho-1456488

ABSTRACT

BACKGROUND: Preliminary evidence has shown inequities in coronavirus disease 2019 (COVID-19)-related cases and deaths in the United States. OBJECTIVE: To explore the emergence of spatial inequities in COVID-19 testing, positivity, confirmed cases, and mortality in New York, Philadelphia, and Chicago during the first 6 months of the pandemic. DESIGN: Ecological, observational study at the ZIP code tabulation area (ZCTA) level from March to September 2020. SETTING: Chicago, New York, and Philadelphia. PARTICIPANTS: All populated ZCTAs in the 3 cities. MEASUREMENTS: Outcomes were ZCTA-level COVID-19 testing, positivity, confirmed cases, and mortality cumulatively through the end of September 2020. Predictors were the Centers for Disease Control and Prevention Social Vulnerability Index and its 4 domains, obtained from the 2014-2018 American Community Survey. The spatial autocorrelation of COVID-19 outcomes was examined by using global and local Moran I statistics, and estimated associations were examined by using spatial conditional autoregressive negative binomial models. RESULTS: Spatial clusters of high and low positivity, confirmed cases, and mortality were found, co-located with clusters of low and high social vulnerability in the 3 cities. Evidence was also found for spatial inequities in testing, positivity, confirmed cases, and mortality. Specifically, neighborhoods with higher social vulnerability had lower testing rates and higher positivity ratios, confirmed case rates, and mortality rates. LIMITATIONS: The ZCTAs are imperfect and heterogeneous geographic units of analysis. Surveillance data were used, which may be incomplete. CONCLUSION: Spatial inequities exist in COVID-19 testing, positivity, confirmed cases, and mortality in 3 large U.S. cities. PRIMARY FUNDING SOURCE: National Institutes of Health.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , Pandemics/statistics & numerical data , SARS-CoV-2 , COVID-19/epidemiology , Cities , Humans , Socioeconomic Factors , Spatial Analysis , United States/epidemiology
12.
Int J Health Serv ; 52(1): 38-46, 2022 01.
Article in English | MEDLINE | ID: covidwho-1455832

ABSTRACT

After more than 1 year from the beginning of the pandemic, the coronavirus disease 2019 (COVID-19) has reached all continents. The number of infected people is still increasing, and Brazil is among the countries with the highest number of registered cases in the world. In this study, we investigated the profile of hospitalized COVID-19 cases and the eventual clusters of similar areas, using geographic information systems. The study was conducted using secondary data. Variables such as sociodemographic characteristics, comorbidities, hospitalization, signs, and symptoms among confirmed cases were considered for each microregion/city of the state of Rio de Janeiro. These proportions were used when calculating the Global Moran's I. The local indicator of spatial association was used to identify local clusters. A significant global spatial auto correlation was found in 28% of the variables. The presence of spatial autocorrelation indicates that the proportions of patients with COVID-19 according to these characteristics are spatially oriented. Moran maps reveal 2 clusters, 1 of high proportions and 1 of low proportions. Understanding the geographic patterns of COVID-19 may assist public health investigators, contributing to actions to prevent and control the pandemic in the state.


Subject(s)
COVID-19 , Brazil/epidemiology , Hospitalization , Humans , SARS-CoV-2 , Spatial Analysis
13.
PLoS One ; 16(9): e0257533, 2021.
Article in English | MEDLINE | ID: covidwho-1443841

ABSTRACT

BACKGROUND: COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19's infection ratio (IR) is essential for formulating policies and interventions. OBJECTIVE: The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India's socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. DATA AND METHODS: We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. RESULTS: The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran's- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15-59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. CONCLUSION: The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.


Subject(s)
COVID-19/epidemiology , Adolescent , Adult , Family Characteristics , Humans , India/epidemiology , Middle Aged , Pandemics , SARS-CoV-2/isolation & purification , Socioeconomic Factors , Spatial Analysis , Urban Population , Young Adult
14.
Elife ; 102021 09 17.
Article in English | MEDLINE | ID: covidwho-1438866

ABSTRACT

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.


Subject(s)
Human Migration/statistics & numerical data , Models, Biological , Rural Population/statistics & numerical data , Africa South of the Sahara/epidemiology , Humans , Spatial Analysis , Travel/statistics & numerical data
15.
Sci Total Environ ; 806(Pt 1): 150521, 2022 Feb 01.
Article in English | MEDLINE | ID: covidwho-1433808

ABSTRACT

We live in a global pandemic caused by the COVID-19 disease where severe social distancing measures are necessary. Some of these measures have been taken into account by the administrative boundaries within cities (neighborhoods, postal districts, etc.). However, considering only administrative boundaries in decision making can prove imprecise, and could have consequences when it comes to taking effective measures. To solve the described problems, we present an epidemiological study that proposes using spatial point patterns to delimit spatial units of analysis based on the highest local incidence of hospitalisations instead of administrative limits during the first COVID-19 wave. For this purpose, the 579 addresses of the cases hospitalised between March 3 and April 6, 2020, in Albacete (Spain), and the addresses of the random sample of 383 controls from the Inhabitants Register of the city of Albacete, were georeferenced. The risk ratio in those hospitalised for COVID-19 was compatible with the constant risk ratio in Albacete (p = 0.49), but areas with a significantly higher risk were found and coincided with those with greater economic inequality (Gini Index). Moreover, two districts had areas with a significantly high incidence that were masked by others with a significantly low incidence. In conclusion, taking measures conditioned exclusively by administrative limits in a pandemic can cause problems caused by managing entire districts with lax measures despite having interior areas with high significant incidences. In a pandemic context, georeferencing disease cases in real time and spatially comparing them to updated random population controls to automatically and accurately detect areas with significant incidences are suggested. This would facilitate decision making, which must be fast and accurate in these situations.


Subject(s)
COVID-19 , Cities , Humans , Pandemics , SARS-CoV-2 , Spatial Analysis
16.
PLoS One ; 16(9): e0256857, 2021.
Article in English | MEDLINE | ID: covidwho-1416876

ABSTRACT

BACKGROUND: The 2019 coronavirus (COVID-19) epidemic began in Wuhan, China in December 2019 and quickly spread to the rest of the world. This study aimed to analyse the associations between the COVID-19 mortality rate in hospitals, the availability of health services, and socio-spatial and health risk factors at department level. METHODS AND FINDINGS: This spatial cross-sectional study used cumulative mortality data due to the COVID-19 pandemic in hospitals until 30 November 2020 as a main outcome, across 96 departments of mainland France. Data concerning health services, health risk factors, and socio-spatial factors were used as independent variables. Independently, we performed negative binomial, spatial and geographically weighted regression models. Our results revealed substantial geographic disparities. The spatial exploratory analysis showed a global positive spatial autocorrelation in each wave indicating a spatial dependence of the COVID-19 deaths across departments. In first wave about 75% of COVID-19 deaths were concentrated in departments of five regions compared to a total of 13 regions. The COVID-19 mortality rate was associated with the physicians density, and not the number of resuscitation beds. Socio-spatial factors were only associated with the COVID-19 mortality rate in first wave compared to wave 2. For example, the COVID-19 mortality rate increased by 35.69% for departments densely populated. Health risk factors were associated with the COVID-19 mortality rate depending on each wave. This study had inherent limitations to the ecological analysis as ecological bias risks and lack of individual data. CONCLUSIONS: Our results suggest that the COVID-19 pandemic has spread more rapidly and takes more severe forms in environments where there is already a high level of vulnerability due to social and health factors. This study showed a different dissemination pattern of COVID-19 mortality between the two waves: a spatial non-stationarity followed by a spatial stationarity in the relationships between the COVID-19 mortality rate and its potential drivers.


Subject(s)
COVID-19/mortality , Pandemics , Aged , Cross-Sectional Studies , Female , France/epidemiology , Health Services Accessibility , Humans , Male , Middle Aged , Risk Factors , Spatial Analysis
17.
J Infect Dev Ctries ; 15(8): 1066-1073, 2021 08 31.
Article in English | MEDLINE | ID: covidwho-1405471

ABSTRACT

INTRODUCTION: COVID-19 is a severe respiratory syndrome caused by the SARS-CoV-2 virus. In Brazil the highest infection rates are associated with socially vulnerable populations. This study therefore sought to analyze the spatial distribution of the disease and its relation with geographic, socioeconomic and public health policy characteristics associated with quilombola communities in Salvaterra municipality, state of Pará, for the period of March to September, 2020. METHODOLOGY: This cross-sectional and ecological study used data from the Disease Notification System and the National Registry of Health Establishments of the Ministry of Health, the Income Transfer Registry of the Ministry of Citizenship and the 2010 census of the Brazilian Institute of Geography and Statistics. Statistical and spatial analysis of the data was done through percentages of cases and Flow and Kernel map techniques. RESULTS: Seventy-five notified cases of COVID-19 distributed among 7 quilombola communities in the municipality were analyzed. The epidemiological profile followed a national trend, with a higher percentage of cases among persons who were female, adults with low schooling levels, working as family farmers and with an outcome ending in recovery. The spatial distribution of the disease was not homogenous and showed clusters of cases and high incidence rates, especially in communities close to the municipal seat or to highways. CONCLUSIONS: The use of data analysis techniques was satisfactory for providing an understanding of the socioeconomic production of the disease in the areas studied. Accordingly, the need for intensifying epidemiological survey actions in the quilombola communities of the municipality is emphasized.


Subject(s)
COVID-19/epidemiology , Public Health/statistics & numerical data , Socioeconomic Factors , Vulnerable Populations/statistics & numerical data , Brazil/epidemiology , Cross-Sectional Studies , Female , Humans , Incidence , Male , Public Health/legislation & jurisprudence , Qualitative Research , Risk Factors , Spatial Analysis
18.
Int J Environ Res Public Health ; 18(7)2021 03 31.
Article in English | MEDLINE | ID: covidwho-1378278

ABSTRACT

Food safety is related to public health, social welfare, and human survival, all of which are important and pressing areas of concern all over the world. The government plays an increasingly important role in the supervision of food safety. The role of the government, however, is also controversial. Using provincial panel data of China from 2005 to 2015, the present study intends to shed light on the associations between government intervention and food safety performance under two scenarios of local government-competition and noncompetition. This will be accomplished through an exploratory spatial data analysis and a spatial econometric model. The results reveal negative associations between food safety performance and government intervention without considering local government competition. As was also observed, government intervention not only inhibits the improvement of food safety in the region, but also has a negative spatial spillover effect on food safety in neighboring provinces. This is the result after considering government competition, thus, showing the competitive strategic interaction of the "race to the bottom". Further analysis reveals that, if geographically similar regions are selected as reference objects, the food safety performance of each province will have a stronger tendency to compete for the better. If regions with similar economic development levels are selected as reference objects, food safety performance will have a stronger tendency to compete for the worse. This work provides new evidence for the relationships between government intervention and food safety, and, also, proposes some insightful implications for policymakers for governing food safety.


Subject(s)
Economic Development , Local Government , China , Food Safety , Humans , Spatial Analysis
19.
Spat Spatiotemporal Epidemiol ; 39: 100454, 2021 11.
Article in English | MEDLINE | ID: covidwho-1373271

ABSTRACT

Coronavirus disease 2019 (COVID-19) has been spread globally and brought health and socioeconomic issues. Jakarta tried to accommodate health and economic interests through the Large-Scale Social Restriction (LSSR) policy that should be assessed. This study aims to (1) visualize the spatial patterns of confirmed Covid-19 cases and the locations of potential risk of transmission, and (2) determine the spatial processes underlying the spatial patterns of Covid-19 cases. The emerging hot spot analysis and space-time scan statistic were employed to analyze the dynamic of infected cases and transmission risk. A Geographical Weighted Regression (GWR) model was developed to define factors that influence the spatial transmission. The result shows that spatial transmission keeps continuing, despite a decline in the aggregate pandemic curve during LSSR implementation. This was likely affected by settlements types and population density distribution, and transportation networks. Spatial analysis supports the aggregate pandemic curve to increase the pandemic surveillance effectiveness.


Subject(s)
COVID-19 , Pandemics , Disease Outbreaks/prevention & control , Humans , Policy , SARS-CoV-2 , Spatial Analysis
20.
PLoS One ; 16(8): e0254660, 2021.
Article in English | MEDLINE | ID: covidwho-1362084

ABSTRACT

The SARS-CoV-2 virus has spread around the world with over 100 million infections to date, and currently many countries are fighting the second wave of infections. With neither sufficient vaccination capacity nor effective medication, non-pharmaceutical interventions (NPIs) remain the measure of choice. However, NPIs place a great burden on society, the mental health of individuals, and economics. Therefore the cost/benefit ratio must be carefully balanced and a target-oriented small-scale implementation of these NPIs could help achieve this balance. To this end, we introduce a modified SEIRD-class compartment model and parametrize it locally for all 412 districts of Germany. The NPIs are modeled at district level by time varying contact rates. This high spatial resolution makes it possible to apply geostatistical methods to analyse the spatial patterns of the pandemic in Germany and to compare the results of different spatial resolutions. We find that the modified SEIRD model can successfully be fitted to the COVID-19 cases in German districts, states, and also nationwide. We propose the correlation length as a further measure, besides the weekly incidence rates, to describe the current situation of the epidemic.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/methods , Pandemics/prevention & control , COVID-19/prevention & control , Cost-Benefit Analysis , Germany/epidemiology , Humans , Incidence , Models, Statistical , Spatial Analysis
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