Spatiotemporal Accessibility of COVID-19 Healthcare Facilities in Jakarta, Indonesia

During the first year of the COVID-19 pandemic in Jakarta, Indonesia, the government designated some hospitals as specific COVID-19 healthcare centers to meet demand and ensure accessibility. However, the policy demand evaluation was based on a purely spatial approach. Studies on accessibility to healthcare are widely available, but those that consider temporal as well as spatial dynamics are lacking. This study aims to analyze the spatiotemporal dynamics of healthcare accessibility against COVID-19 cases within the first year of the COVID-19 pandemic, and the overall pattern of spatiotemporal accessibility. A two-step floating catchment area (2SFCA) was used to analyze the accessibility of COVID-19 healthcare against the monthly data of the COVID-19 infected population, as the demand. Such a spatiotemporal approach to 2SFCA has never been used in previous studies. Furthermore, rather than the traditional buffer commonly used to define catchments, the 2SFCA in this study was improved with automated delineation based on the road network using ArcGIS Service Areas Analysis tools. The accessibility tends to follow the distance decay principle, which is relatively high in the city's center and low in the outskirts. This contrasts with the city's population distribution, which is higher on the outskirts and lower in the center. This research is a step toward optimizing the spatial distribution of hospital locations to correspond with the severity of the pandemic condition. One method to stop the transmission of disease during a pandemic that requires localizing the infected patient is to designate specific healthcare facilities to manage the sick individuals. 'What-if' scenarios may be used to experiment with the locations of these healthcare facilities, which are then assessed using the methodology described in this work to obtain the distribution that is most optimal.

A Year of Spatio-Temporal Clusters of COVID-19 in Indonesia

Coronavirus disease-2019 (COVID-19) in Indonesia began to appear on March 2, 2020 and led to a number of fatalities. Spatial analysis is important to study the spatio-temporal trend of COVID-19 cases and fatalities to get a better understanding of the spread as well as to mitigate it. However, such a comprehensive study at national level is not to be seen in Indonesia with limited health infrastructure. This study aims to analyse the spatio-temporal distribution and clusters of COVID-19 in Indonesia for a year period. COVID-19 cases, as well as the fatalities as a consequence of this disease, were collected from the government through publicly shared data. A geographic information system (GIS) was used to manage and analyse the data on demographics, cases, and fatalities. The case fatality rate (CFR) was produced based on the number of cases and deaths per province weekly. The spatio-temporal data of both cases and fatalities were generated from the data. Finally, K-means clustering was employed to classify the cluster of Indonesia based on the proportion of vulnerable age groups, cases, and CFR. The results show that most of the provinces in Indonesia are affected by COVID-19, but the fatalities are not distributed evenly throughout the country. Based on the K-means clustering, two provinces are classified as moderate, namely the Province of East Kalimantan and North Kalimantan. The Province of Jakarta is classified as high, because the vulnerable age group there is highly correlated with the number of cases and deaths. [ FROM AUTHOR] Copyright of Quaestiones Geographicae is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Monitoring of Social distancing Face mask Using CNN

The outbreak of the Pandemic in last few months, rapid increase in the transmission of the virus and also the new emerging various strains of COVID-19 corona virus has led to complete Iockdown in the entire world. Meanwhile Iockdown imposed on various countries for longer duration has affected almost every sector of the society causing loss leading to hunger and poverty in the world. By considering all the situations and difficulties underwent by the human society a clear scenario where country not only needs Iockdown as it cannot be the effective solution in slowing down the rate of disease affecting people, So Society is Constantly looking for the alternatives that could help every sector in their business without loss is the topic of the hour. An alternative which could satisfy the above conditions is by Social Distancing and Wearing the Face mask. There by proposing our Real Time System which will detect whether required distance is maintained between two people and detect whether the face mask is worn or not by people with the aid of Web Camera using the most trending technologies Artificial Intelligence, Machine Learning Algorithms, Deep Learning, CNN and few more. © 2021 IEEE.

P.0509 Emotion de-regulation in a cohort of young europeans during the spring 2020 lockdown

Introduction: The first lockdown response to the Covid-19 pandemic has been an unexpected public health stress, notably in Europe. Its putative effect on mental health in young people was rapidly emphasized, but thorough evaluation of its impact on emotional components are scarce. Therefore we investigated the emotion regulation in a sample of young europeans during the peri-lockdown period. Aim(s): i) to specify some emotional components assessed by a self-questionnaire in young adults and ii) to investigate the subjective perception of their degradation with the change in their social environment 3 months before and during the first lockdown period. Methods: a modified version of the Coronavirus Health Impact Survey (v.0.2;, was completed online by 447 22-23 years-old participants, part of the IMAGEN cohort (https://imagen-europe.com) in Paris, London, Nottingham, Berlin, and Mannheim areas. It assessed participants’ self-perception in life changes caused by the coronavirus crisis. Participants retrospectively rated their own levels of emotions and worries in the 3 months before the onset of the COVID-19 crisis in the participants’ local area (pre–COVID-19 symptoms), as well as in the past 2 weeks (peri–COVID-19 symptoms) with a Cronbach's alpha =.81 and.87 for the past 3 months and past 2 weeks, respectively. participants indicated their levels of 1) worry, 2) happiness versus sadness, 3) enjoyment in usual activities, 4) feeling relaxed versus anxious, 5) feeling fidgety or restless, 6) feeling fatigued or tired, 7) concentration, 8) irritability or anger, 9) loneliness, 10) experiences of negative thoughts and 11) suicide thoughts/self-harm. An exploratory factor analysis with Varimax rotation and using maximum likelihood method of estimation was performed on scored data. Thereafter, effects on mental health variables were searched with ANOVAs. Results: The factor analysis revealed three factors within the emotion/worries section of the questionnaire: valence, psychomotor, and attention. To investigate the effect of the lockdown on emotional health, a score for every participant has been determined in relation with each factor. Then, the difference between the value of factor score for each participant at three months before and during the last two weeks of the first lockdown was further used in the analyses. ANOVAs showed that the item markedly related to emotional deterioration was social distance, which had a deleterious effect on emotional valence (p=2.09e-06), psychomotricity (p=0.0167), and attention (p=8.99e-05) factor scores. The participants who had a poor emotional health as denoted by their valence scores and suicidal thoughts before the lockdown were in majority better during the lockdown, whereas the reverse was detected in those with initial low scores in those measures. In contrast, the socio-professional category, drug addiction diagnosis, closure of university/business building and financial problems had no statistical impact on the emotional state of this sample of young europeans during the first lockdown. Conclusion: The present participants’ self-evaluations further support that the social distancing was the major item related to emotional health deterioration during the first COVID-19 lockdown. No conflict of interest