scholarly journals Data-driven modelling and prediction of COVID-19 infection in India and correlation analysis of the virus transmission with socio-economic factors

2020 ◽  
Vol 14 (5) ◽  
pp. 1231-1240
Author(s):  
Amit Kumar ◽  
Poonam Rani ◽  
Rahul Kumar ◽  
Vasudha Sharma ◽  
Soumya Ranjan Purohit
Keyword(s):  
Correlation Analysis ◽  
Virus Transmission ◽  
Data Driven ◽  
Economic Factors ◽  
Socio Economic Factors

scholarly journals Understanding spatial patterns in rape reporting delays

2021 ◽  
Vol 8 (2) ◽  
pp. 201795
Author(s):  
Konstantin Klemmer ◽  
Daniel B. Neill ◽  
Stephen A. Jarvis
Keyword(s):  
Data Driven ◽  
Economic Factors ◽  
Rape Survivors ◽  
Learning Framework ◽  
Vulnerable Communities ◽  
Police Data ◽  
Reporting Delays ◽  
The Right ◽  
Us Cities ◽  
Socio Economic Factors

Under-reporting and delayed reporting of rape crime are severe issues that can complicate the prosecution of perpetrators and prevent rape survivors from receiving needed support. Building on a massive database of publicly available criminal reports from two US cities, we develop a machine learning framework to predict delayed reporting of rape to help tackle this issue. Motivated by large and unexplained spatial variation in reporting delays, we build predictive models to analyse spatial, temporal and socio-economic factors that might explain this variation. Our findings suggest that we can explain a substantial proportion of the variation in rape reporting delays using only openly available data. The insights from this study can be used to motivate targeted, data-driven policies to assist vulnerable communities. For example, we find that younger rape survivors and crimes committed during holiday seasons exhibit longer delays. Our insights can thus help organizations focused on supporting survivors of sexual violence to provide their services at the right place and time. Due to the non-confidential nature of the data used in our models, even community organizations lacking access to sensitive police data can use these findings to optimize their operations.

scholarly journals Socio-economic factors in the spread of SARS-COV-2 across Russian regions

R-Economy ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 129-145
Author(s):  
Evgeny V. Sinitsyn ◽  
◽  
Alexander V. Tolmachev ◽  
Alexander S. Ovchinnikov ◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Correlation Analysis ◽  
Federal State ◽  
Economic Factors ◽  
Factors Affecting ◽  
Average Value ◽  
Frequency Probability ◽  
The Impact ◽  
Epidemiological Situation ◽  
Socio Economic Factors

Relevance. The worldwide spread of a new infection SARS-CoV-2 makes relevant the analysis of the socio-economic factors that make modern civilization vulnerable to previously unknown diseases. In this regard, the development of mathematical models describing the spread of pandemics like COVID-19 and the identification of socio-economic factors affecting the epidemiological situation in regions is an important research task. Research objective. This study seeks to develop a mathematical model describing the spread of COVID-19, thus enabling the analysis of the main characteristics of the spread of the disease and assessment of the impact of various socio-economic factors. Data and methods. The study relies on the official statistical data on the pandemic presented on coronavirus sites in Russia and other countries, Yandex DataLens dataset service, as well as data from the Federal State Statistics Service. The data were analyzed by using a correlation analysis of COVID-19 incidence parameters and socio-economic characteristics of regions; multivariate regression – to determine the parameters of the probabilistic mathematical model of the spread of the pandemic proposed by the authors; clustering – to group the regions with similar incidence characteristics and exclude the regions with abnormal parameters from the analysis. Results. A mathematical model of the spread of the COVID-19 pandemic is proposed. The parameters of this model are determined on the basis of official statistics on morbidity, in particular the frequency (probability) of infections, the reliability of the disease detection, the probability density of the disease duration, and its average value. Based on the specificity of COVID-19, Russia regions are clustered according to disease-related characteristics. For clusters that include regions with typical disease-related characteristics, a correlation analysis of the relationship between the number of cases and the rate of infection ( with the socio-economic characteristics of the region is carried out. The most significant factors associated with the parameters of the pandemic are identified. Conclusions. The proposed mathematical model of the pandemic and the established correlations between the parameters of the epidemiological situation and the socio-economic characteristics of the regions can be used to make informed decisions regarding the key risk factors and their impact on the course of the pandemic.

DINAMIKA KELOMPOK TANI ANUGERAH MOLLO DI DESA NETPALA KECAMATAN MOLLO UTARA KABUPATEN TIMOR TENGAH SELATAN

2020 ◽  
Vol 21 (1) ◽  
pp. 71-80
Author(s):  
Tanggu Dedo Yeremias ◽  
Ernantje Hendrik ◽  
Ignatius Sinu
Keyword(s):  
Formal Education ◽  
Secondary Data ◽  
Nonparametric Test ◽  
Primary Data ◽  
Economic Factors ◽  
Dynamic Level ◽  
South Central ◽  
Group Members ◽  
Relationship Of ◽  
Socio Economic Factors

ABSTRACT This research has been carried out in the Anugerah Mollo Farmer Group, in Netpala Village, North Mollo District, South Central Timor Regency, starting in March - April 2019. This study aims to determine: (1) The dynamic level of the Anugerah Mollo Farmer Group in Netpala Village, North Mollo District, South Central Timor Regency, (2) Relationship between Socio-economic factors of farmer group members and the level of dynamics of the Anugerah Mollo Farmer Group in Netpala Village, North Mollo District, South Central Timor Regency. Determination of the location of the study carried out intentionally (purposive sampling) The type of data collected is primary data obtained from direct interviews with respondents guided by the questionnaire, while secondary data is obtained from the relevant agencies. To find out the first purpose of the data analyzed using a Likert scale, to find out the second purpose of the data analyzed using the Sperman Rank statistical Nonparametric test. The results of this study indicate that: (1) The level of dynamism of the Anugerah Mollo Farmer Group in Netpala Village, North Mollo District, South Central Timor Regency, is in the very dynamic category of 84%, (2) The relationship of socio-economic factors is only one of the five variables that are significantly related namely land area with a coefficient of rs 0.278 and t = 1.782 count greater than t table 1.699 (p> 0.05), while other social factors such as age, formal education, number of family dependents, and experience of farming show no significant relationship with the level of dynamism of Anugerah Mollo Farmers Group in Netpala Village.

Implication of Socio-economic Factors on Land Use and Forest Cover Changes in and Around Magamba Nature Reserve in Tanzania

2019 ◽  
Vol 5 (1) ◽  
pp. 108-117
Author(s):  
Solomon Jeremiah Sembosi
Keyword(s):  
Land Use ◽  
Forest Cover ◽  
Nature Reserve ◽  
Agricultural Land ◽  
Population Pressure ◽  
Economic Factors ◽  
Rural Settlements ◽  
Mountainous Regions ◽  
Use Of Resources ◽  
Socio Economic Factors

Rural settlements in mountainous regions are a typical process that occurs in many places around the world and have a number of implications on the landscape. Among them is a threat it possesses to the conservation and management of Afromontane ecosystems. This study assessed the socio-economic factors that drive the changes in land use and forest cover and the extent of land use and vegetation cover in and around Magamba Nature Reserve. Focus group discussion, direct field observation and household survey were used to acquire socio-economic information that impacts land use and forest cover. Through the use of Remote Sensing and GIS methods Landsat satellite images of 1995, 2008 and 2015 were employed to identify the extent of the changes in land use and forest cover. The perceived factors for the changes include education level, unemployment, landless/limited, landholding, population pressure, expansion of built-up areas and agricultural land at the expense of other land covers. This study revealed the transformation of natural forest and associated vegetation from one form to another. There was a decrease in natural vegetation from 61.06% in 1995 to 26.02% in 2015 and increase in built-up areas by 6.69% and agricultural areas by 4.70%. This study recommends conservation monitoring and strong law enforcement relating to natural resources so as to promote sustainable use of resources to rescue the diminishing ecosystem services.

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