scholarly journals A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support

2022 ◽  
Vol 17 (s1) ◽  
Author(s):  
Agung Syetiawan ◽  
Mira Harimurti ◽  
Yosef Prihanto
Keyword(s):  
Disease Transmission ◽  
Spatiotemporal Analysis ◽  
Space Time ◽  
Policy Makers ◽  
Time Pattern ◽  
Entire Area ◽  
Comprehensive Information ◽  
National Economic ◽  
Referral Hospitals ◽  
Huge Impact

With 25% confirmed cases of the country’s total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran’s I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.

scholarly journals Spatiotemporal Analysis of COVID-19 Spread with Emerging Hotspot Analysis and Space–Time Cube Models in East Java, Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 133
Author(s):  
Purwanto Purwanto ◽  
Sugeng Utaya ◽  
Budi Handoyo ◽  
Syamsul Bachri ◽  
Ike Sari Astuti ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Distribution Patterns ◽  
Spatiotemporal Analysis ◽  
Space Time ◽  
Road Density ◽  
Hotspot Analysis ◽  
Spatiotemporal Information ◽  
Depth Analysis ◽  
Analysis Models ◽  
Oscillating Patterns

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses.

scholarly journals ANÁLISIS ESPACIO-TEMPORAL DE LA MALARIA EN MATINA, LIMÓN, COSTA RICA

2016 ◽  
Vol 1 (54) ◽  
pp. 67
Author(s):  
Silvia Argüello Vargas ◽  
Elba de la Cruz Malavassi ◽  
Marco V Herrero Acosta
Keyword(s):  
Costa Rica ◽  
Temporal Trend ◽  
Space Time ◽  
Time Analysis ◽  
Time Pattern ◽  
Future Studies ◽  
Household Level ◽  
Factores De Riesgo ◽  
Spatial Attributes ◽  
Los Grupos

<p>El objetivo de este estudio fue establecer el patrón espacio-temporal de la malaria en Matina y relacionarlo con factores ambientales. Se utilizaron tecnologías espaciales para capturar, almacenar, analizar y visualizar información relacionada con localidades y viviendas. Los atributos no espaciales fueron analizados usando pruebas paramétricas y no paramétricas. Los datos fueron obtenidos de las bases de datos de casos clínicos del Área Rectora del Ministerio de Salud en Matina. Se presentan los descriptores puntuales de las localidades positivas para los años 2005 y 2006 y en los grupos de viviendas positivo y negativo en la localidad piloto. Se propone una clasificación de áreas macroambientales en el cantón y se relaciona con la distribución de la Incidencia Parasitaria Anual (IPA). Se identificaron factores de riesgo a nivel de vivienda en la localidad piloto. Se describe la ocurrencia temporal de la actividad malárica en el cantón. El patrón espacio-temporal que se presenta en este informe puede servir de línea base para estudiar cambios que podrían ocurrir en el futuro.</p><p> </p><p>SPACE-TIME ANALYSIS OF MALARIA IN MATINA, LIMÓN, COSTA RICA</p><p><strong>ABSTRACT</strong><br /> The purpose of this study was to describe the space-time pattern of the disease, and relate it to environmental factors. Spatial technologies were used to collect, store, analyze and display information regarding locations and household locations. Non-spatial attributes were analyzed using parametric and non parametric tests. The information was obtained from databases of clinical cases form the Governing Area of the Health Ministry in Matina. Centrographic parameters were calculated for localities within Matina and for households within the pilot location. Parasitic Incidence (IPA) was associated with a proposed environmental classifiation for Matina. At the household level, risk factors were determined. The temporal pattern of the disease in Matina is described. A similar temporal trend is shown for households within the pilot location. This is the fist time that the information collected in the Matina Governing Area is used to describe the spatial patterns of malaria.<br /> This pattern will be useful as a comparative baseline for future studies.</p><p> </p><p><span><br /></span></p>

On the space-time pattern formation of the earthquake strain field

1985 ◽  
Vol 113 (1-2) ◽  
pp. 31-48 ◽  
Author(s):  
Toru Ouchi ◽  
Shinichi Goriki ◽  
Keisuke Ito
Keyword(s):  
Pattern Formation ◽  
Strain Field ◽  
Space Time ◽  
Time Pattern

scholarly journals Unfolding Events in Space and Time: Geospatial Insights into COVID-19 Diffusion in Washington State during the Initial Stage of the Outbreak

2020 ◽  
Vol 9 (6) ◽  
pp. 382 ◽  
Author(s):  
Vaishnavi Thakar
Keyword(s):  
Spatial Data ◽  
The United States ◽  
Space Time ◽  
Washington State ◽  
Data Availability ◽  
Time Pattern ◽  
Web Mapping ◽  
Space And Time ◽  
First Case ◽  
Initial Stage

The world witnessed the COVID-19 pandemic in 2020. The first case of COVID-19 in the United States of America (USA) was confirmed on 21 January 2020, in Snohomish County in Washington State (WA). Following this, a rapid explosion of COVID-19 cases was observed throughout WA and the USA. Lack of access to publicly available spatial data at finer scales has prevented scientists from implementing spatial analytical techniques to gain insights into the spread of COVID-19. Datasets were available only as counts at county levels. The spatial response to COVID-19 using coarse-scale publicly available datasets was limited to web mapping applications and dashboards to visualize infected cases from state to county levels only. This research approaches data availability issues by creating proxy datasets for COVID-19 using publicly available news articles. Further, these proxy datasets are used to perform spatial analyses to unfolding events in space and time and to gain insights into the spread of COVID-19 in WA during the initial stage of the outbreak. Spatial analysis of theses proxy datasets from 21 January to 23 March 2020, suggests the presence of a clear space–time pattern. From 21 January to 6 March, a strong presence of community spread of COVID-19 is observed only in close proximity of the outbreak source in Snohomish and King Counties, which are neighbors. Infections diffused to farther locations only after a month, i.e., 6 March. The space–time pattern of diffusion observed in this study suggests that implementing strict social distancing measures during the initial stage in infected locations can drastically help curb the spread to distant locations.

scholarly journals The application of computational fluid dynamics and small-scale physical models to assess the effects of operational practices on the risk to public health within large indoor swimming pools

2015 ◽  
Vol 13 (4) ◽  
pp. 939-952 ◽  
Author(s):  
Lowell Lewis ◽  
John Chew ◽  
Iain Woodley ◽  
Jeni Colbourne ◽  
Katherine Pond
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Disease Transmission ◽  
Time Frame ◽  
Physical Models ◽  
Microbial Pathogens ◽  
Small Scale ◽  
Swimming Pools ◽  
Policy Makers ◽  
Operational Practices

Swimming pools provide an excellent facility for exercise and leisure but are also prone to contamination from microbial pathogens. The study modelled a 50-m × 20-m swimming pool using both a small-scale physical model and computational fluid dynamics to investigate how water and pathogens move around a pool in order to identify potential risk spots. Our study revealed a number of lessons for pool operators, designers and policy-makers: disinfection reaches the majority of a full-scale pool in approximately 16 minutes operating at the maximum permissible inlet velocity of 0.5 m/s. This suggests that where a pool is designed to have 15 paired inlets it is capable of distributing disinfectant throughout the water body within an acceptable time frame. However, the study also showed that the exchange rate of water is not uniform across the pool tank and that there is potential for areas of the pool tank to retain contaminated water for significant periods of time. ‘Dead spots’ exist at either end of the pool where pathogens could remain. This is particularly significant if there is a faecal release into the pool by bathers infected with Cryptosporidium parvum, increasing the potential for waterborne disease transmission.

scholarly journals Individual space–time activity-based modelling of infectious disease transmission within a city

2007 ◽  
Vol 5 (24) ◽  
pp. 759-772 ◽  
Author(s):  
Yong Yang ◽  
Peter Atkinson ◽  
Dick Ettema
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Activity Patterns ◽  
Space Time ◽  
Infectious Disease Transmission ◽  
Time Activity ◽  
Level Ii ◽  
Simulation Results ◽  
The City ◽  
Daily Activity Patterns

This paper provides an example of the application of an individual space–time activity-based model (ISTAM) to the simulation of the transmission of infectious disease in Eemnes, a city in The Netherlands. Four questions were addressed: (i) how to build the whole population at the city level, (ii) how to build the structure of the activity bundles for the city, (iii) how to assign daily activity patterns to each individual, and (iv) how to simulate within-AB transmission. The model was calibrated and examples of simulation results such as dynamics of the population during a whole day, infection distribution and network analysis are presented.

scholarly journals SPATIOTEMPORAL ANALYSIS ON CO2 EMISSIONS FROM HOUSEHOLDS IN JAPAN

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 75-81
Author(s):  
R. Cong ◽  
M. Saito ◽  
R. Hirata ◽  
A. Ito
Keyword(s):  
Carbon Dioxide ◽  
Ghg Emissions ◽  
Spatiotemporal Analysis ◽  
Local Area ◽  
Spatiotemporal Patterns ◽  
Population Census ◽  
Ghg Mitigation ◽  
Policy Makers ◽  
Residential Sector ◽  
Census Area

<p><strong>Abstract.</strong> Global warming has become worse and worse as the increasing greenhouse gas (GHG) emissions especially by the main contributor carbon dioxide (CO<sub>2</sub>). Thus, clarifying the spatiotemporal patterns of CO<sub>2</sub> emissions from residential sector is very important for policy makers. To support the GHG mitigation in local area, this study provides a bottom-up framework that could count the monthly residential CO<sub>2</sub> emissions at community level, demonstrated for Japan. A map-based population census is utilized to count the monthly and yearly emissions by combining the statistics data on households with detailed emission intensities. The residential emissions from each census area are estimated and mapped by Geographic Information System. Through the analysis, we proposed the solutions on GHG mitigation and reported the spatiotemporal patterns for residential emissions.</p>

scholarly journals Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies

2021 ◽  
Vol 10 (9) ◽  
pp. 627
Author(s):  
Anran Zheng ◽  
Tao Wang ◽  
Xiaojuan Li
Keyword(s):  
Risk Factors ◽  
New York ◽  
Environmental Factors ◽  
Spatial Autocorrelation ◽  
Disease Transmission ◽  
New York State ◽  
Space Time ◽  
Spatiotemporal Pattern ◽  
York State ◽  
Time Scan

The Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Based on the county-level COVID-19 cases and environmental factors in the state from March to December 2020, this study investigates spatiotemporal clustering patterns using spatial autocorrelation and space-time scan analysis. Environmental factors influencing the COVID-19 spread were analyzed based on the Geodetector model. Infection clusters first appeared in southern New York State and then moved to the central western parts as the epidemic developed. The statistical results of space-time scan analysis are consistent with those of spatial autocorrelation analysis. The analysis results of Geodetector showed that both temperature and population density were strong indications of the monthly incidence of COVID-19, especially in March and April 2020. There is a trend of increasing interactions between various risk factors. This study explores the spatiotemporal pattern of COVID-19 in New York State over ten months and explains the relationship between the disease transmission and influencing factors.

Sign in / Sign up

Export Citation Format

Share Document