Time Series Analysis in the Social Sciences

This book focuses on fundamental elements of time series analysis that social scientists need to understand to employ time series analysis for their research and practice. Avoiding extraordinary mathematical materials, this book explains univariate time-series analysis step by step from the preliminary visual analysis through the modeling of seasonality, trends, and residuals to the prediction and the evaluation of estimated models. Then, this book explains smoothing, multiple time-series analysis, and interrupted time-series analysis. At the end of each step, this book coherently provides an analysis of the monthly violent crime rates as an example.

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The impact of the social distancing policy on COVID-19 new cases in Iran: insights from an interrupted time series analysis

10.21203/rs.3.rs-25818/v1 ◽

2020 ◽

Author(s):

Mahboubeh Khaton Ghanbari ◽

Meysam Behzadifar ◽

Mohammad Hasan Imani-Nasab ◽

Masoud Behzadifar ◽

Ahad Bakhtiari ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Infectious Agent ◽

Interrupted Time Series ◽

P Value ◽

Interrupted Time Series Analysis ◽

Social Distancing ◽

Series Analysis ◽

The Social ◽

The Impact

Abstract Background In late December 2019, a viral outbreak occurred in Wuhan, province of Hubei, People’s Republic of China, and rapidly spread out worldwide. The infectious agent was identified and termed as SARS-CoV-2, responsible of the “coronavirus disease 19” (COVID-19). Due to the lack of vaccines and effective drugs for this disease, many policy- and decision-makers have focused on non-pharmacological methods to prevent and control this disease. Social distancing can be effective in reducing the spread of the outbreak. This study was aimed at assessing the effects of the implementation of the social distancing policy in Iran, one of the countries most affected by the COVID-19. Methods This study was designed as a quasi-experimental study, and was conducted utilizing the interrupted time series analysis (ITSA) approach. Daily data was collected between February 20th 2020 and April 16th 2020. The social distancing policy was launched on March 27th 2020.Results A significant decrease of -288.57 (95% CI: 269.08 (95% CI: -83.37 to -621.55, P-value=0.04) new confirmed cases following the implementation of the social distancing policy was found, corresponding to a daily decrease in the trend of -8.10 (95% CI: -10.02 to -6.19, P-value=0.001). A significant decrease of -24.78 (95% CI: -42.97 to -6.58, P-value=0.01) new deaths following the implementation of the social distancing policy could be found, corresponding to a daily decrease in the trend of -8.10 (95% CI: -10.02 to -6.19, P-value=0.001). Conclusion The growth rate of new cases and deaths from the COVID-19 in Iran has significantly decreased after the implementation of social distancing. By monitoring and implementing this policy in all countries, the burden of COVID-19 can be mitigated.

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Social distancing in São Paulo State: demonstrating the reduction in cases using time series analysis of deaths due to COVID-19

Revista Brasileira de Epidemiologia ◽

10.1590/1980-549720200056 ◽

2020 ◽

Vol 23 ◽

Cited By ~ 1

Author(s):

Carlos Henrique de Brito Cruz

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Sao Paulo ◽

São Paulo ◽

Social Distancing ◽

Paulo State ◽

Series Analysis ◽

The Social ◽

Isolation Index ◽

São Paulo State

ABSTRACT: Introduction: Demonstrating the results of Social Distancing Strategies (SDS) became a relevant factor to obtain support by the population in São Paulo State and in Brazil. The delay in the processing of PCR tests and the small number of tests available limits the ability of sanitary authorities to make meaningful data available as to the number of cases or the number of deaths due to COVID-19. Methodology: We use a time series analysis of deaths due to COVID-19 referenced to the date of deaths (as opposed to the date in which the test results were obtained). Results: We demonstrate that the SDS adopted in São Paulo City and State clearly brought meaningful results to delay the growth of COVID-19 cases. We also show that by using this type of time series it is possible to identify different trends for regions, allowing for targeted approaches. Additionally, by using a time series which is death-oriented makes it possible to identify, for São Paulo City, the effects of the SDS with the Social Isolation Index (SII) adopted in the state and to make a gross estimate for the SII, which prevents the growth of the disease. Conclusion: The use of a time series of deaths due to COVID-19 referenced to the date of the event allows a better understanding of the effects of the SDS on the progression of the COVID-19 epidemic in São Paulo State, Brazil.

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