scholarly journals Space-Time Patterns, Change, and Propagation of COVID-19 Risk Relative to the Intervention Scenarios in Bangladesh

2020 ◽  
Vol 17 (16) ◽  
pp. 5911 ◽  
Author(s):  
Arif Masrur ◽  
Manzhu Yu ◽  
Wei Luo ◽  
Ashraf Dewan
Keyword(s):  
Public Awareness ◽  
Temporal Analysis ◽  
Space Time ◽  
Health Care Facilities ◽  
Transmission Dynamics ◽  
Disease Spread ◽  
Significant Public Health ◽  
Spatio Temporal ◽  
Novel Coronavirus ◽  
Active Cluster

The novel coronavirus (COVID-19) pandemic continues to be a significant public health threat worldwide, particularly in densely populated countries such as Bangladesh with inadequate health care facilities. While early detection and isolation were identified as important non-pharmaceutical intervention (NPI) measures for containing the disease spread, this may not have been pragmatically implementable in developing countries due to social and economic reasons (i.e., poor education, less public awareness, massive unemployment). Hence, to elucidate COVID-19 transmission dynamics with respect to the NPI status—e.g., social distancing—this study conducted spatio-temporal analysis using the prospective scanning statistic at district and sub-district levels in Bangladesh and its capital, Dhaka city, respectively. Dhaka megacity has remained the highest-risk “active” cluster since early April. Lately, the central and south eastern regions in Bangladesh have been exhibiting a high risk of COVID-19 transmission. The detected space-time progression of COVID-19 infection suggests that Bangladesh has experienced a community-level transmission at the early phase (i.e., March, 2020), primarily introduced by Bangladeshi citizens returning from coronavirus epicenters in Europe and the Middle East. Potential linkages exist between the violation of NPIs and the emergence of new higher-risk clusters over the post-incubation periods around Bangladesh. Novel insights into the COVID-19 transmission dynamics derived in this study on Bangladesh provide important policy guidelines for early preparations and pragmatic NPI measures to effectively deal with infectious diseases in resource-scarce countries worldwide.

scholarly journals Space-time patterns, change, and propagation of COVID-19 risk relative to the intervention scenarios in Bangladesh

2020 ◽  
Author(s):  
Arif Masrur ◽  
Manzhu Yu ◽  
Wei Luo ◽  
Ashraf Dewan
Keyword(s):  
Disease Transmission ◽  
Public Awareness ◽  
Space Time ◽  
Health Care Facilities ◽  
Transmission Dynamics ◽  
Disease Spread ◽  
Time Patterns ◽  
Significant Public Health ◽  
Novel Coronavirus ◽  
Elevated Exposure

The novel coronavirus (COVID-19) pandemic continues to be a significant public health threat worldwide. As of mid-June 2020, COVID-19 has spread worldwide with more than 7.7 million confirmed cases and more than 400,000 deaths. The impacts are substantial particularly in developing and densely populated countries like Bangladesh with inadequate health care facilities, where COVID-19 cases are currently surging. While early detection and isolation were identified as important non-pharmaceutical intervention (NPI) measures for containing the disease spread, this may not be pragmatically implementable in developing countries primarily due to social and economic reasons (i.e. poor education, less public awareness, massive unemployment). To shed light on COVID-19 transmission dynamics and impacts of NPI scenarios, e.g. social distancing, this study conducted emerging pattern analysis using the space-time scan statistic at district and thana (i.e. a sub-district or 'upazila' with at least one police station) levels in Bangladesh and its capital Dhaka city, respectively. We found that the central and south eastern regions in Bangladesh are currently exhibiting a high risk of COVID-19 transmission. Dhaka megacity remains as the highest risk "active" cluster since early April. The space-time progression of COVID-19 infection, when validated against the chronicle of government press releases and newspaper reports, suggests that Bangladesh have experienced a community level transmission at the early phase (i.e., March, 2020) primarily introduced by Bangladeshi citizens returning from coronavirus-affected countries in the Europe and the Middle East. A linkage is evident between the violation of NPIs and post-incubation period emergence of new clusters with elevated exposure risk around Bangladesh. This study provides novel insights into the space-time patterns of COVID-19 transmission dynamics and recommends pragmatic NPI implementation for reducing disease transmission and minimizing impacts in a resource-scarce country with Bangladesh as a case-study example.

scholarly journals The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis

2022 ◽  
Vol 8 ◽  
Author(s):  
Orapun Arjkumpa ◽  
Minta Suwannaboon ◽  
Manoch Boonrod ◽  
Issara Punyawan ◽  
Supawadee Liangchaisiri ◽  
...  
Keyword(s):  
Skin Disease ◽  
Temporal Analysis ◽  
Space Time ◽  
Effective Control ◽  
Outbreak Response ◽  
Lumpy Skin Disease ◽  
Epidemiological Features ◽  
Spatio Temporal ◽  
Epidemiological Characteristics ◽  
Cattle Farms

The first outbreak of lumpy skin disease (LSD) in Thailand was reported in March 2021, but information on the epidemiological characteristics of the outbreak is very limited. The objectives of this study were to describe the epidemiological features of LSD outbreaks and to identify the outbreak spatio-temporal clusters. The LSD-affected farms located in Roi Et province were investigated by veterinary authorities under the outbreak response program. A designed questionnaire was used to obtain the data. Space-time permutation (STP) and Poisson space-time (Poisson ST) models were used to detect areas of high LSD incidence. The authorities identified 293 LSD outbreak farms located in four different districts during the period of March and the first week of April 2021. The overall morbidity and mortality of the affected cattle were 40.5 and 1.2%, respectively. The STP defined seven statistically significant clusters whereas only one cluster was identified by the Poisson ST model. Most of the clusters (n = 6) from the STP had a radius <7 km, and the number of LSD cases in those clusters varied in range of 3–51. On the other hand, the most likely cluster from the Poisson ST included LSD cases (n = 361) from 198 cattle farms with a radius of 17.07 km. This is the first report to provide an epidemiological overview and determine spatio-temporal clusters of the first LSD outbreak in cattle farms in Thailand. The findings from this study may serve as a baseline information for future epidemiological studies and support authorities to establish effective control programs for LSD in Thailand.

scholarly journals Spatio-temporal analysis and visualisation of the anthrax epidemic situation in livestock in Kazakhstan over the period 1933-2016

2017 ◽  
Author(s):  
Sarsenbay K. Abdrakhmanov ◽  
Yersyn Y. Mukhanbetkaliyev ◽  
Fedor I. Korennoy ◽  
Bolat Sh. Karatayev ◽  
Aizada A. Mukhanbetkaliyeva ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Small Ruminants ◽  
Temporal Analysis ◽  
Space Time ◽  
Decision Makers ◽  
Annual Number ◽  
Spatio Temporal ◽  
The Republic ◽  
Increasing Trends ◽  
Historical Periods

An analysis of the anthrax epidemic situation among livestock animals in the Republic of Kazakhstan over the period 1933-2016 is presented. During this time, 4,064 anthrax outbreaks (mainly in cattle, small ruminants, pigs and horses) were recorded. They fall into five historical periods of increase and decrease in the annual anthrax incidence (1933-1953; 1954-1968; 1969-1983; 1984- 2001; and 2002-2016), which has been associated with changes in economic activity and veterinary surveillance. To evaluate the temporal trends of incidence variation for each of these time periods, the following methods were applied: i) spatio-temporal analysis using a space-time cube to assess the presence of hotspots (i.e., areas of outbreak clustering) and the trends of their emergence over time; and ii) a linear regression model that was used to evaluate the annual numbers of outbreaks as a function of time. The results show increasing trends during the first two periods followed by a decreasing trend up to now. The peak years of anthrax outbreaks occurred in 1965-1968 but outbreaks still continue with an average annual number of outbreaks of 1.2 (95% confidence interval: 0.6-1.8). The space-time analysis approach enabled visualisation of areas with statistically significant increasing or decreasing trends of outbreak clustering providing a practical opportunity to inform decision-makers and allowing the veterinary services to concentrate their efforts on monitoring the possible risk factors in the identified locations.

scholarly journals Formation of a system of indicators of balanced economic development in the context of globalization: space-time analysis in order to ensure economic security

2021 ◽  
Vol 92 ◽  
pp. 07001
Author(s):  
Lubov Afanasyeva ◽  
Larisa Belousova ◽  
Tatyana Tkacheva
Keyword(s):  
Value Added ◽  
Temporal Analysis ◽  
Economic Security ◽  
Space Time ◽  
Modern World ◽  
Time Analysis ◽  
Balanced Development ◽  
Federal Districts ◽  
Spatio Temporal

Research background: The modern world economy of the 21st century, being innovatively oriented, global and based on the information space, is constantly being modified, focusing on the growth of the level and quality of life of the population by accelerating innovation processes. For the purposes of monitoring the innovation and investment activities of the regions in order to ensure the economic security of Russia, it is necessary to have a system of indicators that reflect the processes of innovative development of its territories as comprehensively as possible. Purpose of the article: Formation of a system of indicators for the balanced development of the region’s economy in the context of globalization: space-time analysis in order to ensure economic security. Methods: Spatio-temporal analysis of the identified indicators of balanced development of the regional economy in order to ensure economic security. Findings & Value added: The proposed system of indicators of economic security of the region and their threshold values can be used in assessing the development forecasts of the regions and the Federal Districts developed by the administration, draft budgets, expertise of the federal target programs, as well as in other elements of the regional economic security management.

scholarly journals G-STC-M Spatio-Temporal Analysis Method for Archaeological Sites

2021 ◽  
Vol 10 (5) ◽  
pp. 312
Author(s):  
Jing Cui ◽  
Yanrong Liu ◽  
Junling Sun ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Qing Dynasty ◽  
Tang Dynasty ◽  
Temporal Analysis ◽  
Space Time ◽  
Test Method ◽  
Archaeological Sites ◽  
Trend Test ◽  
Analysis Method ◽  
The Qing Dynasty ◽  
Spatio Temporal

Based on the significant hotspots analysis method (Getis-Ord Gi* significance statistics), space-time cube model (STC) and the Mann–Kendall trend test method, this paper proposes a G-STC-M spatio-temporal analysis method based on Archaeological Sites. This method can integrate spatio-temporal data variable analysis and the space-time cube model to explore the spatio-temporal distribution of Archaeological Sites. The G-STC-M method was used to conduct time slice analysis on the data of Archaeological Sites in the study area, and the spatio-temporal variation characteristics of Archaeological Sites in East China from the Tang Dynasty to the Qing Dynasty were discussed. The distribution of Archaeological Sites has temporal hotspots and spatial hotspots. Temporally, the distribution of Archaeological Sites showed a gradual increasing trend, and the number of Archaeological Sites reached the maximum in the Qing Dynasty. Spatially, the hotspots of Archaeological Sites are mainly distributed in Jiangsu (30°~33° N, 118°~121° E) and Anhui (29°~31° N, 117°~119° E) and the central region of Zhejiang (28°~31° N, 118°~121° E). Temporally and spatially, the distribution of Archaeological Sites is mainly centered in Shanghai (30°~32° N, 121°~122° E), spreading to the southern region.

scholarly journals A spatio-temporal modelling of Covid-19 infections in Toronto’s neighbourhoods

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Sze Hang Fu
Keyword(s):  
Population Density ◽  
Temporal Trends ◽  
Social Factor ◽  
Age Groups ◽  
Temporal Analysis ◽  
Disease Spread ◽  
Lower Socioeconomic ◽  
Neighbourhood Level ◽  
Spatio Temporal ◽  
Control Disease

Introduction & Objective: Besides age and sex as established risk factors of COVID-19 infection, social factor is found to be a determinant, with people of lower socioeconomic status suffer disproportionately from the disease. The city of Toronto has one of the highest COVID-19 infection rates in Canada. This analysis aims to explore the socioeconomic correlates associated with COVID-19 infection and the temporal trends among different age groups in Toronto using geospatial modeling. Methods: A Bayesian spatio-temporal analysis was conducted using public COVID-19 cases data for Toronto. The case data were modeled using the Besag-York-Mollie (BYM) model, implemented in R-INLA. The model adjusted for age, sex, neighbourhood-level socioeconomic factors, crime rates, and population density. Random effects were included to account for neighbourhood-level variation and for spatial autocorrelation. Temporal trends of COVID-19 cases were modelled using second-order random walks to allow non-parametric estimations. Results: The model estimates showed that men are at higher risk of COVID-19 infection. Among neighbourhood factors, higher home prices, education level, and population density are at lower risks, while belonging to an improvement area showed elevated risks. The temporal trends differed by age, with ages 20-59 showed increased risks over time, compared to the youngest and older age groups. Model predictions showed that northwest Toronto has higher risk compared to the rest of Toronto. Conclusion: The higher COVID-19 infection risks in the Northwest will require increase public health effort to control disease spread in this area. The ecological correlates identified in this analysis will also help to guide the ongoing vaccination plans.

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