Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 96
Filter
1.
Rev Panam Salud Publica ; 46, abr. 2022
Article in Spanish | PAHO, PAHOIRIS | ID: covidwho-1791369

ABSTRACT

[RESUMEN]. Objetivo. Determinar la estructura temporal y espacial del virus del síndrome respiratorio agudo grave (SARSCoV- 2, por su sigla en inglés), causante de la enfermedad por coronavirus (COVID-19, por su sigla en inglés) en las ciudades de Cartagena y Barranquilla para tomar acciones necesarias que apoyen el rastreo de contactos. Métodos. Estudio ecológico transversal que incluye análisis espacial basado en densidades Kernel de variables como casos, alertas desde una aplicación móvil, vulnerabilidad poblacional, índice de pobreza multidimensional, aplicación de interpolación espacial (IDW, por su sigla en inglés) de los casos activos y, por último, la aplicación de la técnica de superposición espacial como resultado final. Se utilizó la base de datos del Instituto Nacional de Salud de las ciudades de Cartagena y Barranquilla y el Departamento de Estadística Nacional. Resultados. El análisis determinó el comportamiento epidemiológico ascendente de los casos en las dos ciudades e identificó la dirección espacial de propagación de la enfermedad en los barrios, a través de la interpolación espacial. Se detectaron las zonas en las cuales intervenir en 15 barrios de Cartagena y 13 de Barranquilla, en 50 metros alrededor de los casos activos con menos de 21 días de evolución y según las capas de riesgo geográfico, como mecanismo para frenar la propagación de la COVID-19. Conclusiones. El análisis espacial permitió determinar la estructura temporal y espacial como metodología complementaria útil para el rastreo de contactos, y aportó la evidencia científica necesaria para la aplicación de medidas de intervención directa donde fuera necesario, dirigidas a reducir el contagio del SARS-CoV-2.


[ABSTRACT]. Objective. Determine the temporal and spatial structure of the severe acute respiratory syndrome virus (SARSCoV- 2) that causes coronavirus disease (COVID-19), in the cities of Cartagena and Barranquilla, Colombia, in order to take necessary actions to support contact tracing. Methods. Cross-sectional ecological study with spatial analysis based on kernel densities of variables, including cases, mobile application alerts, population vulnerability, multidimensional poverty index; inverse distance weighted spatial interpolation of active cases; and, finally, the spatial superposition technique as a final result. The database of the National Institute of Health of the cities of Cartagena and Barranquilla and the Department of National Statistics was used. Results. The analysis identified an upward epidemiological trend in cases in the two cities, and determined the spatial direction of disease spread in neighborhoods, through spatial interpolation. Intervention areas were detected in 15 neighborhoods in Cartagena and 13 in Barranquilla, 50 meters around active cases with fewer than 21 days of evolution and by geographical risk layers, as a mechanism to stop the spread of COVID-19. Conclusions. Spatial analysis proved to be a useful complementary methodology for contact tracing, by determining temporal and spatial structure and providing necessary scientific evidence for the application of direct intervention measures, where necessary, to reduce the spread of SARS-CoV-2.


[RESUMO]. Objetivo. Determinar a estrutura temporal e espacial do vírus da síndrome respiratória aguda grave (SARSCoV- 2, na sigla em inglês), causador da doença pelo coronavírus de 2019 (COVID-19, na sigla em inglês), nas cidades de Cartagena e Barranquilla, visando a tomar ações necessárias que apoiem o rastreamento de contatos. Métodos. Estudo ecológico transversal que inclui análise espacial baseada em densidade de Kernel de variáveis como casos, alertas de um aplicativo móvel, vulnerabilidade populacional, índice de pobreza multidimensional, aplicação de interpolação espacial (IDW, na sigla em inglês) de casos ativos e, por último, aplicação da técnica de sobreposição espacial como resultado final. Foram utilizadas as bases de dados do Instituto Nacional de Saúde para as cidades de Cartagena e Barranquilla e do Departamento Nacional de Estatística. Resultados. A análise determinou o comportamento epidemiológico ascendente dos casos nas duas cidades e identificou a direção espacial de propagação da doença nos bairros, por meio de interpolação espacial. Foram detectadas áreas para intervenção em 15 bairros de Cartagena e 13 de Barranquilla, em 50 metros ao redor dos casos ativos com menos de 21 dias de evolução e de acordo com as camadas de risco geográfico, como mecanismo para impedir a propagação da COVID-19. Conclusões. A análise espacial permitiu determinar a estrutura temporal e espacial como uma metodologia complementar útil para o rastreamento de contatos, e forneceu a evidência científica necessária para a aplicação de medidas de intervenção direta, quando necessário, visando a reduzir o contágio pelo SARS-CoV-2.


Subject(s)
Geographic Information Systems , Coronavirus Infections , Public Health Surveillance , Risk Map , Colombia , Geographic Information Systems , Coronavirus Infections , Public Health Surveillance , Risk Map , Geographic Information Systems , Coronavirus Infections , Public Health Surveillance , Risk Map , Colombia
2.
JMIR Mhealth Uhealth ; 10(3): e22544, 2022 03 17.
Article in English | MEDLINE | ID: covidwho-1745200

ABSTRACT

BACKGROUND: The ongoing COVID-19 pandemic in Africa is an urgent public health crisis. Estimated models projected over 150,000 deaths and 4,600,000 hospitalizations in the first year of the disease in the absence of adequate interventions. Therefore, electronic contact tracing and surveillance have critical roles in decreasing COVID-19 transmission; yet, if not conducted properly, these methods can rapidly become a bottleneck for synchronized data collection, case detection, and case management. While the continent is currently reporting relatively low COVID-19 cases, digitized contact tracing mechanisms and surveillance reporting are necessary for standardizing real-time reporting of new chains of infection in order to quickly reverse growing trends and halt the pandemic. OBJECTIVE: This paper aims to describe a COVID-19 contact tracing smartphone app that includes health facility surveillance with a real-time visualization platform. The app was developed by the AFRO (African Regional Office) GIS (geographic information system) Center, in collaboration with the World Health Organization (WHO) emergency preparedness and response team. The app was developed through the expertise and experience gained from numerous digital apps that had been developed for polio surveillance and immunization via the WHO's polio program in the African region. METHODS: We repurposed the GIS infrastructures of the polio program and the database structure that relies on mobile data collection that is built on the Open Data Kit. We harnessed the technology for visualization of real-time COVID-19 data using dynamic dashboards built on Power BI, ArcGIS Online, and Tableau. The contact tracing app was developed with the pragmatic considerations of COVID-19 peculiarities. The app underwent testing by field surveillance colleagues to meet the requirements of linking contacts to cases and monitoring chains of transmission. The health facility surveillance app was developed from the knowledge and assessment of models of surveillance at the health facility level for other diseases of public health importance. The Integrated Supportive Supervision app was added as an appendage to the pre-existing paper-based surveillance form. These two mobile apps collected information on cases and contact tracing, alongside alert information on COVID-19 reports at the health facility level; the information was linked to visualization platforms in order to enable actionable insights. RESULTS: The contact tracing app and platform were piloted between April and June 2020; they were then put to use in Zimbabwe, Benin, Cameroon, Uganda, Nigeria, and South Sudan, and their use has generated some palpable successes with respect to COVID-19 surveillance. However, the COVID-19 health facility-based surveillance app has been used more extensively, as it has been used in 27 countries in the region. CONCLUSIONS: In light of the above information, this paper was written to give an overview of the app and visualization platform development, app and platform deployment, ease of replicability, and preliminary outcome evaluation of their use in the field. From a regional perspective, integration of contact tracing and surveillance data into one platform provides the AFRO with a more accurate method of monitoring countries' efforts in their response to COVID-19, while guiding public health decisions and the assessment of risk of COVID-19.


Subject(s)
COVID-19 , Poliomyelitis , COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing/methods , Geographic Information Systems , Humans , Pandemics/prevention & control , Poliomyelitis/epidemiology , Poliomyelitis/prevention & control
3.
Ciênc. Saúde Colet ; 25(supl.1): 2461-2468, Mar. 2020. graf
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1725050

ABSTRACT

Resumo A distribuição geográfica da COVID-19 por meio de recursos de Sistemas de Informação Geográfica é pouco explorada. O objetivo foi analisar a distribuição de casos da COVID-19 e de leitos de terapia intensiva exclusivos para a doença no estado do Ceará, Brasil. Estudo ecológico, com distribuição geográfica do coeficiente de detecção de casos da doença em 184 municípios. Construíram-se mapas dos valores brutos e estimados (método bayesiano global e local), com cálculo do índice de Moran e utilização do "BoxMap" e "MoranMap" Os leitos foram distribuídos por meio de pontos geolocalizados. Estudaram-se 3.000 casos e 459 leitos. As maiores taxas encontram-se na capital Fortaleza, região metropolitana (RM) e ao sul dessa região. Há autocorrelação espacial positiva na taxa bayesiana local (I = 0,66). A distribuição dos leitos de terapia intensiva sobreposta ao "BoxMap" evidenciou aglomerados com padrão Alto-Alto apresentando número de leitos (capital, RM, porção noroeste); porém, há o mesmo padrão (extremo leste) e em áreas de transição com insuficiência de leito. O "MoranMap" evidenciou "clusters" estatisticamente significativos no estado. A interiorização da COVID-19 no Ceará demanda medidas de contingência voltadas à distribuição dos leitos de terapia intensiva específicos para casos de COVID19 para atender à demanda.


Abstract The geographical distribution of COVID-19 through Geographic Information Systems resources is hardly explored. We aimed to analyze the distribution of COVID-19 cases and the exclusive intensive care beds in the state of Ceará, Brazil. This is an ecological study with the geographic distribution of the case detection coefficient in 184 municipalities. Maps of crude and estimated values (global and local Bayesian method) were developed, calculating the Moran index and using BoxMap and MoranMap. Intensive care beds were distributed through geolocalized points. In total, 3,000 cases and 459 beds were studied. The highest rates were found in the capital Fortaleza, the Metropolitan Region (MR), and the south of this region. A positive spatial autocorrelation has been identified in the local Bayesian rate (I = 0.66). The distribution of beds superimposed on the BoxMap shows clusters with a High-High pattern of number of beds (capital, MR, northwestern part). However, a similar pattern is found in the far east or transition areas with insufficient beds. The MoranMap shows clusters statistically significant in the state. COVID-19 interiorization in Ceará requires contingency measures geared to the distribution of specific intensive care beds for COVID-19 cases in order to meet the demand.


Subject(s)
Humans , Pneumonia, Viral/epidemiology , Coronavirus Infections/epidemiology , Pandemics , Geographic Mapping , Betacoronavirus , Hospital Bed Capacity/statistics & numerical data , Intensive Care Units/supply & distribution , Pneumonia, Viral/transmission , Brazil/epidemiology , Bayes Theorem , Coronavirus Infections , Coronavirus Infections/transmission , Geographic Information Systems
4.
Sensors (Basel) ; 22(3)2022 Jan 22.
Article in English | MEDLINE | ID: covidwho-1686938

ABSTRACT

One of the causes of positioning inaccuracies in the Unmanned Aircraft System (UAS) is navigation error. In urban environment operations, multipaths could be the dominant contributor to navigation errors. This paper presents a study on how the operation environment affects the lateral (horizontal) navigation performance when a self-built UAS is going near different types of urban obstructions in real flight tests. Selected test sites are representative of urban environments, including open carparks, flight paths obstructed by buildings along one or both sides, changing sky access when flying towards corners formed by two buildings or dead ends, and buildings with reflective glass-clad surfaces. The data was analysed to obtain the horizontal position error between Global Positioning System (GPS) position and ground truth derived from Real Time Kinematics (RTK), with considerations for (1) horizontal position uncertainty estimate (EPH) reported by the GPS receiver, (2) no. of visible satellites, and (3) percentage of sky visible (or sky openness ratio, SOR) at various altitudes along the flight paths inside the aforementioned urban environments. The investigation showed that there is no direct correlation between the measured horizontal position error and the reported EPH; thus, the EPH could not be used for the purpose of monitoring navigation performance. The investigation further concluded that there is no universal correlation between the sky openness ratio (SOR) seen by the UAS and the resulting horizontal position error, and a more complex model would need to be considered to translate 3D urban models to expected horizontal navigation uncertainty for the UAS Traffic Management (UTM) airspace.


Subject(s)
Aircraft , Geographic Information Systems , Biomechanical Phenomena
5.
Int J Environ Res Public Health ; 19(3)2022 01 30.
Article in English | MEDLINE | ID: covidwho-1667154

ABSTRACT

The coronavirus disease of 2019 (COVID-19) pandemic is currently a global challenge, with 210 countries, including Indonesia, seeking to minimize its spread. Therefore, this study aims to determine the spatiotemporal spread pattern of this virus in Surabaya using various data on confirmed cases from 28 April to 26 October 2021. It also aims to determine the relationship between pollutant parameters, such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), as well as the government's high social restrictions policy in Java-Bali. Several methods, such as the weighted mean center, directional distribution, Getis-Ord Gi*, Moran's I, and geographically weighted regression, were used to identify the spatial spread pattern of the virus. The weighted mean center indicated that the epicenter location of the outbreak moved randomly. The directional distribution demonstrated a decrease of 21 km2 at the end of the study phase, which proved that its spread has significantly reduced in Surabaya. Meanwhile, the Getis-Ord Gi* results demonstrated that the eastern and southern parts of the study region were highly infected. Moran's I demonstrate that COVID-19 cases clustered during the spike. The geographically weighted regression model indicated a number of influence zones in the northeast, northwest, and a few in the southwest parts at the peak of R2 0.55. The relationship between COVID-19 cases and air pollution parameters proved that people living at the outbreak's center have low pollution levels due to lockdown. Furthermore, the lockdown policy reduced CO, NO2, SO2, and O3. In addition, increase in air pollutants; namely, NO2, CO, SO2 and O3, was recorded after 7 weeks of lockdown implementation (started from 18 August).


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Communicable Disease Control , Environmental Monitoring , Geographic Information Systems , Humans , Particulate Matter/analysis , SARS-CoV-2 , Spatio-Temporal Analysis
6.
PLoS One ; 16(12): e0260931, 2021.
Article in English | MEDLINE | ID: covidwho-1632675

ABSTRACT

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.


Subject(s)
COVID-19/psychology , Cell Phone/statistics & numerical data , Search Engine/statistics & numerical data , Socioeconomic Factors , Suicide/psychology , Geographic Information Systems , Humans , Mental Health/statistics & numerical data , New York City , Quarantine/statistics & numerical data , Search Engine/trends , Stress, Psychological , Time Factors , United States
7.
J Med Internet Res ; 23(2): e23467, 2021 02 09.
Article in English | MEDLINE | ID: covidwho-1574242

ABSTRACT

BACKGROUND: Many countries across the globe have released their own COVID-19 contact tracing apps. This has resulted in the proliferation of several apps that used a variety of technologies. With the absence of a standardized approach used by the authorities, policy makers, and developers, many of these apps were unique. Therefore, they varied by function and the underlying technology used for contact tracing and infection reporting. OBJECTIVE: The goal of this study was to analyze most of the COVID-19 contact tracing apps in use today. Beyond investigating the privacy features, design, and implications of these apps, this research examined the underlying technologies used in contact tracing apps. It also attempted to provide some insights into their level of penetration and to gauge their public reception. This research also investigated the data collection, reporting, retention, and destruction procedures used by each of the apps under review. METHODS: This research study evaluated 13 apps corresponding to 10 countries based on the underlying technology used. The inclusion criteria ensured that most COVID-19-declared epicenters (ie, countries) were included in the sample, such as Italy. The evaluated apps also included countries that did relatively well in controlling the outbreak of COVID-19, such as Singapore. Informational and unofficial contact tracing apps were excluded from this study. A total of 30,000 reviews corresponding to the 13 apps were scraped from app store webpages and analyzed. RESULTS: This study identified seven distinct technologies used by COVID-19 tracing apps and 13 distinct apps. The United States was reported to have released the most contact tracing apps, followed by Italy. Bluetooth was the most frequently used underlying technology, employed by seven apps, whereas three apps used GPS. The Norwegian, Singaporean, Georgian, and New Zealand apps were among those that collected the most personal information from users, whereas some apps, such as the Swiss app and the Italian (Immuni) app, did not collect any user information. The observed minimum amount of time implemented for most of the apps with regard to data destruction was 14 days, while the Georgian app retained records for 3 years. No significant battery drainage issue was reported for most of the apps. Interestingly, only about 2% of the reviewers expressed concerns about their privacy across all apps. The number and frequency of technical issues reported on the Apple App Store were significantly more than those reported on Google Play; the highest was with the New Zealand app, with 27% of the reviewers reporting technical difficulties (ie, 10% out of 27% scraped reviews reported that the app did not work). The Norwegian, Swiss, and US (PathCheck) apps had the least reported technical issues, sitting at just below 10%. In terms of usability, many apps, such as those from Singapore, Australia, and Switzerland, did not provide the users with an option to sign out from their apps. CONCLUSIONS: This article highlighted the fact that COVID-19 contact tracing apps are still facing many obstacles toward their widespread and public acceptance. The main challenges are related to the technical, usability, and privacy issues or to the requirements reported by some users.


Subject(s)
Attitude , COVID-19/prevention & control , Contact Tracing/methods , Mobile Applications , Privacy , Australia , Data Collection , Disease Outbreaks , Geographic Information Systems , Georgia (Republic) , Humans , Italy , New Zealand , Norway , SARS-CoV-2 , Singapore , Switzerland , Technology , United States , Wireless Technology
8.
BMC Public Health ; 21(1): 2227, 2021 12 08.
Article in English | MEDLINE | ID: covidwho-1561120

ABSTRACT

BACKGROUND: Using geographical analysis to identify geographical factors related to the prevalence of COVID-19 infection can affect public health policies aiming at controlling the virus. This study aimed to determine the spatial analysis of COVID-19 in Qom Province, using the local indicators of spatial association (LISA). METHODS: In a primary descriptive-analytical study, all individuals infected with COVID-19 in Qom Province from February 19th, 2020 to September 30th, 2020 were identified and included in the study. The spatial distribution in urban areas was determined using the Moran coefficient in geographic information systems (GIS); in addition, the spatial autocorrelation of the coronavirus in different urban districts of the province was calculated using the LISA method. RESULTS: The prevalence of COVID-19 in Qom Province was estimated to be 356.75 per 100,000 populations. The pattern of spatial distribution of the prevalence of COVID-19 in Qom was clustered. District 3 (Imam Khomeini St.) and District 6 (Imamzadeh Ebrahim St.) were set in the High-High category of LISA: a high-value area surrounded by high-value areas as the two foci of COVID-19 in Qom Province. District 1 (Bajak) of urban districts was set in the Low-High category: a low-value area surrounded by high values. This district is located in a low-value area surrounded by high values. CONCLUSIONS: According to the results, district 3 (Imam Khomeini St.) and district 6 (Imamzadeh Ebrahim St.) areas are key areas for preventing and controlling interventional measures. In addition, considering the location of District 1 (Bajak) as an urban district in the Low-High category surrounded by high values, it seems that distance and spatial proximity play a major role in the spread of the disease.


Subject(s)
COVID-19 , Geographic Information Systems , Humans , Iran/epidemiology , SARS-CoV-2 , Spatial Analysis
9.
JMIR Mhealth Uhealth ; 10(3): e22544, 2022 03 17.
Article in English | MEDLINE | ID: covidwho-1547103

ABSTRACT

BACKGROUND: The ongoing COVID-19 pandemic in Africa is an urgent public health crisis. Estimated models projected over 150,000 deaths and 4,600,000 hospitalizations in the first year of the disease in the absence of adequate interventions. Therefore, electronic contact tracing and surveillance have critical roles in decreasing COVID-19 transmission; yet, if not conducted properly, these methods can rapidly become a bottleneck for synchronized data collection, case detection, and case management. While the continent is currently reporting relatively low COVID-19 cases, digitized contact tracing mechanisms and surveillance reporting are necessary for standardizing real-time reporting of new chains of infection in order to quickly reverse growing trends and halt the pandemic. OBJECTIVE: This paper aims to describe a COVID-19 contact tracing smartphone app that includes health facility surveillance with a real-time visualization platform. The app was developed by the AFRO (African Regional Office) GIS (geographic information system) Center, in collaboration with the World Health Organization (WHO) emergency preparedness and response team. The app was developed through the expertise and experience gained from numerous digital apps that had been developed for polio surveillance and immunization via the WHO's polio program in the African region. METHODS: We repurposed the GIS infrastructures of the polio program and the database structure that relies on mobile data collection that is built on the Open Data Kit. We harnessed the technology for visualization of real-time COVID-19 data using dynamic dashboards built on Power BI, ArcGIS Online, and Tableau. The contact tracing app was developed with the pragmatic considerations of COVID-19 peculiarities. The app underwent testing by field surveillance colleagues to meet the requirements of linking contacts to cases and monitoring chains of transmission. The health facility surveillance app was developed from the knowledge and assessment of models of surveillance at the health facility level for other diseases of public health importance. The Integrated Supportive Supervision app was added as an appendage to the pre-existing paper-based surveillance form. These two mobile apps collected information on cases and contact tracing, alongside alert information on COVID-19 reports at the health facility level; the information was linked to visualization platforms in order to enable actionable insights. RESULTS: The contact tracing app and platform were piloted between April and June 2020; they were then put to use in Zimbabwe, Benin, Cameroon, Uganda, Nigeria, and South Sudan, and their use has generated some palpable successes with respect to COVID-19 surveillance. However, the COVID-19 health facility-based surveillance app has been used more extensively, as it has been used in 27 countries in the region. CONCLUSIONS: In light of the above information, this paper was written to give an overview of the app and visualization platform development, app and platform deployment, ease of replicability, and preliminary outcome evaluation of their use in the field. From a regional perspective, integration of contact tracing and surveillance data into one platform provides the AFRO with a more accurate method of monitoring countries' efforts in their response to COVID-19, while guiding public health decisions and the assessment of risk of COVID-19.


Subject(s)
COVID-19 , Poliomyelitis , COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing/methods , Geographic Information Systems , Humans , Pandemics/prevention & control , Poliomyelitis/epidemiology , Poliomyelitis/prevention & control
10.
Sci Data ; 8(1): 310, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1545633

ABSTRACT

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions.


Subject(s)
COVID-19/epidemiology , Geographic Information Systems , Travel , COVID-19/transmission , Cell Phone , Humans , Pandemics , Spain/epidemiology
11.
BMC Infect Dis ; 21(1): 1185, 2021 Nov 25.
Article in English | MEDLINE | ID: covidwho-1538061

ABSTRACT

BACKGROUND: The first confirmed cases of COVID-19 in Iran were reported in Qom city. Subsequently, the neighboring provinces and gradually all 31 provinces of Iran were involved. This study aimed to investigate the case fatility rate, basic reproductive number in different period of epidemic, projection of daily and cumulative incidence cases and also spatiotemporal mapping of SARS-CoV-2 in Alborz province, Iran. METHODS: A confirmed case of COVID-19 infection was defined as a case with a positive result of viral nucleic acid testing in respiratory specimens. Serial interval (SI) was fitted by gamma distribution and considered the likelihood-based R0 using a branching process with Poisson likelihood. Seven days average of cases, deaths, doubling times and CFRs used to draw smooth charts. kernel density tool in Arc GIS (Esri) software has been employed to compute hot spot area of the study site. RESULTS: The maximum-likelihood value of R0 was 2.88 (95%, CI: 2.57-3.23) in the early 14 days of epidemic. The case fatility rate for Alborz province (Iran) on March 10, was 8.33% (95%, CI:6.3-11), and by April 20, it had an increasing trend and reached 12.9% (95%,CI:11.5-14.4). The doubling time has been increasing from about two days and then reached about 97 days on April 20, 2020, which shows the slowdown in the spread rate of the disease. Also, from March 26 to April 2, 2020 the whole Geographical area of Karj city was almost affected by SARS-CoV-2. CONCLUSIONS: The R0 of COVID-19 in Alborz province was substantially high at the beginning of the epidemic, but with preventive measures and public education and GIS based monitoring of the cases,it has been reduced to 1.19 within two months. This reduction highpoints the attainment of preventive measures in place, however we must be ready for any second epidemic waves during the next months.


Subject(s)
COVID-19 , Epidemics , Geographic Information Systems , Humans , Iran/epidemiology , Likelihood Functions , SARS-CoV-2
12.
Int J Environ Res Public Health ; 18(22)2021 11 21.
Article in English | MEDLINE | ID: covidwho-1524013

ABSTRACT

The COVID-19 pandemic interrupted professional football in the 2019/2020 season, and football experts anticipate that the consequences of lockdown measures will negatively affect the physical performance of players once competition restarts. This study aimed to evaluate position-specific match running performance (MRP) to determine the effect of COVID-19 lockdowns on the physical performance of professional football players. Players' MRPs (n = 124) were observed in matches before and after the COVID-19 lockdown in the 2019/2020 season of the highest level of national competition in Croatia and were classified according to player position: central defenders (CD; n = 42), fullbacks (FB; n = 20), midfielders (MF; n = 46), and forwards (FW; n = 16). The MRPs were measured using Global Positioning System, and included the total distance covered, low-intensity running (≤14.3 km/h), running (14.4-19.7 km/h), high-intensity running (≥19.8 km/h), total accelerations (>0.5 m/s2), high-intensity accelerations (>3 m/s2), total decelerations (less than -0.5 m/s2), and high-intensity decelerations (less than -3 m/s2). The results indicated that, in matches after the COVID-19 lockdown, (i) CDs and FBs featured lower running and high-intensity running (t-value: from 2.05 to 3.51; all p < 0.05; moderate to large effect sizes), (ii) MFs covered a greater distance in low-intensity running and achieved a lower number of total accelerations, and total and high-intensity decelerations (t-value: from -3.54 to 2.46; all p < 0.05, moderate to large effect sizes), and (iii) FWs featured lower high-intensity running (t-value = 2.66, p = 0.02, large effect size). These findings demonstrate that the physical performances of football players from the Croatian first division significantly decreased in matches after the COVID-19 lockdown. A combination of inadequate adaptation to football-specific match demands and a crowded schedule after the competition was restarted most likely resulted in such an effect.


Subject(s)
Athletic Performance , COVID-19 , Football , Running , Soccer , Communicable Disease Control , Geographic Information Systems , Humans , Pandemics , SARS-CoV-2
13.
PLoS Comput Biol ; 17(10): e1009363, 2021 10.
Article in English | MEDLINE | ID: covidwho-1468148

ABSTRACT

The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19 are beginning to be broadly understood, making precise forecasts on case count and mortality is still difficult. In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms. We call this the Universal Influenza-like Transmission (UnIT) score, which is computed as an information-theoretic divergence of the local incidence time series from an high-risk process of epidemic initiation, inferred from almost a decade of flu season incidence data gleaned from the diagnostic history of nearly a third of the US population. Despite being computed from the past seasonal flu incidence records, the UnIT score emerges as the dominant factor explaining incidence trends for the COVID-19 pandemic over putative demographic and socio-economic factors. The predictive ability of the UnIT score is further demonstrated via county-specific weekly case count forecasts which consistently outperform the state of the art models throughout the time-line of the COVID-19 pandemic. This study demonstrates that knowledge of past epidemics may be used to chart the course of future ones, if transmission mechanisms are broadly similar, despite distinct disease processes and causative pathogens.


Subject(s)
COVID-19/epidemiology , Forecasting , Respiratory Tract Infections/epidemiology , Geographic Information Systems , Humans , Incidence , Influenza, Human/epidemiology , Local Government , Models, Biological , Respiratory Tract Infections/transmission , United States/epidemiology
14.
Br J Sports Med ; 55(14): 807-813, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1388482

ABSTRACT

OBJECTIVES: To examine the interactions between SARS-CoV-2 positive players and other players during rugby league matches and determine within-match SARS-CoV-2 transmission risk. METHODS: Four Super League matches in which SARS-CoV-2 positive players were subsequently found to have participated were analysed. Players were identified as increased-risk contacts, and player interactions and proximities were analysed by video footage and global positioning system (GPS) data. The primary outcome was new positive cases of SARS-CoV-2 within 14 days of the match in increased-risk contacts and other players participating in the matches. RESULTS: Out of 136 total players, there were 8 SARS-CoV-2 positive players, 28 players identified as increased-risk contacts and 100 other players in the matches. Increased-risk contacts and other players were involved in 11.4±9.0 (maximum 32) and 4.0±5.2 (maximum 23) tackles, respectively. From GPS data, increased-risk contacts and other players were within 2 m of SARS-CoV-2 positive players on 10.4±18.0 (maximum 88) and 12.5±20.7 (maximum 121) occasions, totalling 65.7±137.7 (maximum 689) and 89.5±169.4 (maximum 1003) s, respectively. Within 14 days of the match, one increased-risk contact and five players returned positive SARS-CoV-2 reverse transcriptase PCR (RT-PCR) tests, and 27 increased-risk contacts and 95 other participants returned negative SARS-CoV-2 RT-PCR tests. Positive cases were most likely traced to social interactions, car sharing and wider community transmission and not linked to in-match transmission. CONCLUSION: Despite tackle involvements and close proximity interactions with SARS-CoV-2 positive players, in-match SARS-CoV-2 transmission was not confirmed. While larger datasets are needed, these findings suggest rugby presents a lower risk of viral transmission than previously predicted.


Subject(s)
Athletic Performance , COVID-19/transmission , Competitive Behavior , Football , Geographic Information Systems , Humans , Male , Pandemics , SARS-CoV-2
15.
J Travel Med ; 27(8)2020 12 23.
Article in English | MEDLINE | ID: covidwho-1387947

ABSTRACT

RATIONALE FOR REVIEW: In response to increased concerns about emerging infectious diseases, GeoSentinel, the Global Surveillance Network of the International Society of Travel Medicine in partnership with the US Centers for Disease Control and Prevention (CDC), was established in 1995 in order to serve as a global provider-based emerging infections sentinel network, conduct surveillance for travel-related infections and communicate and assist global public health responses. This review summarizes the history, past achievements and future directions of the GeoSentinel Network. KEY FINDINGS: Funded by the US CDC in 1996, GeoSentinel has grown from a group of eight US-based travel and tropical medicine centers to a global network, which currently consists of 68 sites in 28 countries. GeoSentinel has provided important contributions that have enhanced the ability to use destination-specific differences to guide diagnosis and treatment of returning travelers, migrants and refugees. During the last two decades, GeoSentinel has identified a number of sentinel infectious disease events including previously unrecognized outbreaks and occurrence of diseases in locations thought not to harbor certain infectious agents. GeoSentinel has also provided useful insight into illnesses affecting different traveling populations such as migrants, business travelers and students, while characterizing in greater detail the epidemiology of infectious diseases such as typhoid fever, leishmaniasis and Zika virus disease. CONCLUSIONS: Surveillance of travel- and migration-related infectious diseases has been the main focus of GeoSentinel for the last 25 years. However, GeoSentinel is now evolving into a network that will conduct both research and surveillance. The large number of participating sites and excellent geographic coverage for identification of both common and illnesses in individuals who have traversed international borders uniquely position GeoSentinel to make important contributions of travel-related infectious diseases in the years to come.


Subject(s)
COVID-19 , International Cooperation , Sentinel Surveillance , Travel Medicine , COVID-19/epidemiology , COVID-19/prevention & control , Centers for Disease Control and Prevention, U.S. , Geographic Information Systems , Humans , SARS-CoV-2 , Travel Medicine/methods , Travel Medicine/trends , Travel-Related Illness , United States
16.
Int J Health Geogr ; 20(1): 40, 2021 08 28.
Article in English | MEDLINE | ID: covidwho-1376586

ABSTRACT

BACKGROUND: Various applications have been developed worldwide to contain and to combat the coronavirus disease-19 (COVID-19) pandemic. In this context, spatial information is always of great significance. The aim of this study is to describe the development of a Web GIS based on open source products for the collection and analysis of COVID-19 cases and its feasibility in terms of technical implementation and data protection. METHODS: With the help of this Web GIS, data on this issue were collected voluntarily from the Cologne area. Using house perimeters as a data basis, it was possible to check, in conjunction with the Official Topographic Cartographic Information System object type catalog, whether buildings with certain functions, for example residential building with trade and services, have been visited more frequently by infected persons than other types of buildings. In this context, data protection and ethical and legal issues were considered. RESULTS: The results of this study show that the development of a Web GIS for the generation and evaluation of volunteered geographic information (VGI) with the help of open source software is possible. Furthermore, there are numerous data protection and ethical and legal aspects to consider, which not only affect VGI per se but also affect IT security. CONCLUSIONS: From a data protection perspective, more attention needs to be paid to the intervention and post-processing of data. In addition, official data must always be used as a reference for the actual spatial consideration of the number of infections. However, VGI provides added value at a small-scale level, so that valid information can also be reliably derived in the context of health issues. The creation of guidelines for the consideration of data protection, ethical aspects, and legal requirements in the context of VGI-based applications must also be considered. Trial registration The article does not report the results of a health care intervention for human participants.


Subject(s)
COVID-19 , SARS-CoV-2 , Geographic Information Systems , Germany/epidemiology , Humans , Pandemics
17.
J Prim Care Community Health ; 12: 21501327211041208, 2021.
Article in English | MEDLINE | ID: covidwho-1374096

ABSTRACT

Corona virus diseases 2019 (COVID-19) pandemic is an extraordinary threat with significant implications in all aspects of human life; therefore, it represents the most immediate challenge for the countries all over the world. This study, hence, is intended to identify the best GIS-based model that can explore, quantify, and model the determinants of COVID-19 incidence and fatality. For this purpose, geospatial models were developed to estimate COVID-19 incidence and fatality rates in Africa, up to 16th of August 2020 at the national level. The models involved Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) analysis using ArcGIS. Spatial autocorrelation analysis recorded a positive spatial autocorrelation in COVID-19 incidence (Moran index 0.16, P = 0.1) and fatality (Moran index 0.26, P = 0.01) rates within different African countries. GWR model had higher R2 than OLS for prediction of incidence and mortality (58% vs 45% and 55% vs 53%). The main predictors of COVID-19 incidence rate were overcrowding, health expenditure, HIV infections, air pollution, and BCG vaccination (mean ß = 3.10, 1.66, 0.01, 3.79, and -66.60 respectively, P < 0.05). The main determinants of COVID-19 fatality were prevalence of bronchial asthma, tobacco use, poverty, aging, and cardiovascular diseases fatality (mean ß = 0.00162, 0.00004, -0.00025, -0.00144, and -0.00027 respectively, P < 0.05). Application of the suggested model can assist in guiding intervention strategies, particularly at the local and community level whenever the data on COVID-19 cases and predictors variables are available.


Subject(s)
COVID-19 , HIV Infections , Africa/epidemiology , Geographic Information Systems , Humans , Incidence , SARS-CoV-2
18.
Environ Sci Pollut Res Int ; 29(2): 1985-1997, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1345177

ABSTRACT

COVID-19 poses many challenges for hospitals around the world. Each country attempts to solve the problems in its hospitals using different methods. In Turkey, two pandemic hospitals were built in Istanbul, the most crowded province. In addition, some hospitals were designated as pandemic hospitals. This study focuses on the methods used for site selection for a pandemic hospital in Atakum, a district of Samsun City, Turkey. As a solution to the problem, initially, spatial analysis was performed using GIS to produce maps based on seven criteria obtained from the insight of an expert team. Analytic hierarchy process (AHP) augmented by interval-valued Pythagorean fuzzy numbers (PFNs) was then used to determine weights for the criteria. Distance to transportation network was the most important criterion influencing the selection process and the least significant one was the distance to fire stations. Based on the criteria weights, and five rules specified by the expert team, 13 suitable locations for a pandemic hospital were determined using GIS. The technique for order preference by similarity to ideal solution (TOPSIS) method was used to determine the final ranking of 13 alternative locations (A1-A13). A10 was identified as the most appropriate site and A11 as the least appropriate site for a pandemic hospital. Finally, sensitivity analysis was performed to investigate how changes in weight values of the criteria affect the ranking of the alternatives.


Subject(s)
COVID-19 , Mobile Health Units , Refuse Disposal , Geographic Information Systems , Hospitals , Humans , Pandemics , SARS-CoV-2 , Turkey
19.
Sci Rep ; 11(1): 15389, 2021 07 28.
Article in English | MEDLINE | ID: covidwho-1331395

ABSTRACT

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.


Subject(s)
Population Dynamics/statistics & numerical data , Cell Phone , Geographic Information Systems , Humans , Kenya , Models, Statistical , Risk Factors , Seasons , Socioeconomic Factors , Spatio-Temporal Analysis , Travel/statistics & numerical data
20.
Curr Biol ; 31(17): 3952-3955.e3, 2021 09 13.
Article in English | MEDLINE | ID: covidwho-1292093

ABSTRACT

Humans have outsized effects on ecosystems, in part by initiating trophic cascades that impact all levels of the food chain.1,2 Theory suggests that disease outbreaks can reverse these impacts by modifying human behavior,3,4 but this has not yet been tested. The COVID-19 pandemic provided a natural experiment to test whether a virus could subordinate humans to an intermediate link in the trophic chain, releasing a top carnivore from a landscape of fear. Shelter-in-place orders in the Bay Area of California led to a 50% decline in human mobility, which resulted in a relaxation of mountain lion aversion to urban areas. Rapid changes in human mobility thus appear to act quickly on food web functions, suggesting an important pathway by which emerging infectious diseases will impact not only human health but ecosystems as well.


Subject(s)
Behavior, Animal , COVID-19/prevention & control , Puma , Animals , Automobile Driving/statistics & numerical data , California , Cities , Ecosystem , Fear , Female , Geographic Information Systems , Humans , Male , Physical Distancing , Quarantine
SELECTION OF CITATIONS
SEARCH DETAIL