scholarly journals What Can We Learn from the Time Evolution of COVID-19 Epidemic in Slovenia?

2020 ◽  
Author(s):  
Ioan Bâldea
Keyword(s):  
Social Science ◽  
Time Dependence ◽  
European Country ◽  
Recent Work ◽  
Epidemiological Data ◽  
Theoretical Assumption ◽  
Time Dynamics ◽  
Travel Restrictions ◽  
Containment Measures ◽  
Self Protection

AbstractA recent work (DOI 10.1101/2020.05.06.20093310) indicated that temporarily splitting larger populations into smaller groups can efficiently mitigate the spread of SARS-CoV-2 virus. The fact that, soon afterwards, on May 15, 2020, the two million people Slovenia was the first European country proclaiming the end of COVID-19 epidemic within national borders may be relevant from this perspective. Motivated by this evolution, in this paper we investigate the time dynamics of coronavirus cases in Slovenia with emphasis on how efficient various containment measures act to diminish the number of COVID-19 infections. Noteworthily, the present analysis does not rely on any speculative theoretical assumption; it is solely based on raw epidemiological data. Out of the results presented here, the most important one is perhaps the finding that, while imposing drastic curfews and travel restrictions reduce the infection rate k by a factor of four with respect to the unrestricted state, they only improve the κ-value by ~ 15% as compared to the much bearable state of social and economical life wherein (justifiable) wearing face masks and social distancing rules are enforced/followed. Significantly for behavioral and social science, our analysis of the time dependence κ = κ(t) may reveal an interesting self-protection instinct of the population, which became manifest even before the official lockdown enforcement.

Understanding Bayesianism: Fundamentals for Process Tracers

2021 ◽  
pp. 1-8
Author(s):  
Andrew Bennett ◽  
Andrew E. Charman ◽  
Tasha Fairfield
Keyword(s):  
Social Science ◽  
Bayesian Analysis ◽  
Qualitative Methods ◽  
Recent Work ◽  
The State ◽  
Process Tracing ◽  
Bayesian Reasoning ◽  
Mutual Exclusivity ◽  
Positive View

Abstract Bayesian analysis has emerged as a rapidly expanding frontier in qualitative methods. Recent work in this journal has voiced various doubts regarding how to implement Bayesian process tracing and the costs versus benefits of this approach. In this response, we articulate a very different understanding of the state of the method and a much more positive view of what Bayesian reasoning can do to strengthen qualitative social science. Drawing on forthcoming research as well as our earlier work, we focus on clarifying issues involving mutual exclusivity of hypotheses, evidentiary import, adjudicating among more than two hypotheses, and the logic of iterative research, with the goal of elucidating how Bayesian analysis operates and pushing the field forward.

scholarly journals Historical Disparities and Gendered Citation Patterns

2018 ◽  
Vol 26 (3) ◽  
pp. 338-344 ◽  
Author(s):  
David A. M. Peterson
Keyword(s):  
Social Science ◽  
Political Science ◽  
Recent Work ◽  
Citation Data ◽  
Science Methodology ◽  
Social Science Methodology ◽  
Over Time

In this comment on Dion, Sumner, and Mitchell’s article “Gendered Citation Patterns across Political Science and Social Science Methodology Fields,” I explore the role of changes in the disparities of citations to work written by women over time. Breaking down their citation data by era, I find that some of the patterns in citations are the result of the legacy of disparity in the field. Citations to more recent work come closer to matching the distribution of the gender of authors of published work. Although the need for more equitable practices of citation remains, the overall patterns are not quite as bad as Dion, Sumner, and Mitchell conclude.

The Significance of Method

1996 ◽  
Vol 39 (3) ◽  
pp. 393-415 ◽  
Author(s):  
Jacek Szmatka ◽  
Michael J. Lovaglia
Keyword(s):  
Resource Allocation ◽  
Social Science ◽  
Recent Work ◽  
Social Theory ◽  
Social Research ◽  
Sociology Of Science ◽  
Theoretical Work ◽  
Theoretical Research ◽  
Important Place ◽  
Effective Manner

While the importance of metatheory for theory growth has received some attention from sociologists, the importance of methodological preferences has been overlooked. We examine an influential model of theory growth in social science. This model focuses on theory. We show how recent work in the sociology of science suggests an equally important place for methodological preference in guiding social research. Bringing in method allows us to recognize that what often passes for fundamental metatheoretical differences among subfields in sociology actually consists of minor squabbles over resource allocation. We show that not only does social theory grow strategically through theoretical research programs, but that the different forms of theoretical work in sociology serve to integrate the work of diverse researchers in a less efficient but still effective manner.

What’s Love Got to Do with It? The Sociology of Godly Love and the Renewal of Modern Pentecostalism

2012 ◽  
Vol 21 (1) ◽  
pp. 113-134 ◽  
Author(s):  
Amos Yong
Keyword(s):  
Social Science ◽  
Recent Work ◽  
Social Science Research ◽  
Science Research ◽  
Interdisciplinary Work ◽  
Social Scientific Study ◽  
Pentecostal Movement ◽  
The Social ◽  
Pentecostal Theology ◽  
Social Scientific

This article uses the recent work of sociologist Margaret M. Poloma to argue that developments in the sociology of Pentecostalism have the potential to revitalize a classical Pentecostal movement that can be otherwise understood as languishing. In particular, the social scientific study of benevolent service in various segments of the Pentecostal movement provides the springboard for the argument. After locating the interdisciplinary work of Poloma and her colleagues on godly love within the broader context of social science research in the last half century, this paper will explore its implications for the future and renewal of especially the classical Pentecostal movement, for Pentecostal theology and self-understanding, and for scholarship on Pentecostalism.

scholarly journals Impact of data accuracy on the evaluation of COVID-19 mitigation policies

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Michele Starnini ◽  
Alberto Aleta ◽  
Michele Tizzoni ◽  
Yamir Moreno
Keyword(s):  
Epidemiological Data ◽  
Surveillance Data ◽  
Inaccurate Data ◽  
Health Authorities ◽  
The Social ◽  
Containment Measures ◽  
Public Health Authorities ◽  
Nonpharmaceutical Interventions ◽  
The Impact ◽  
Extreme Care

Abstract Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number $ R(t) $ , a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of $ R(t) $ could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.

scholarly journals How to improve outbreak response: a case study of integrated outbreak analytics from Ebola in Eastern Democratic Republic of the Congo

2021 ◽  
Vol 6 (8) ◽  
pp. e006736
Author(s):  
Simone E Carter ◽  
Steve Ahuka-Mundeke ◽  
Jérôme Pfaffmann Zambruni ◽  
Carlos Navarro Colorado ◽  
Esther van Kleef ◽  
...  
Keyword(s):  
Social Science ◽  
Democratic Republic ◽  
Epidemiological Data ◽  
Field Studies ◽  
Multiple Sources ◽  
Outbreak Response ◽  
Primary Activity ◽  
Innovative Solution ◽  
Republic Of The Congo ◽  
Social Science Evidence

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018–2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d’Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.

scholarly journals Malta

2020 ◽  
Vol 20 (98) ◽  
Author(s):  
Keyword(s):  
Healthcare System ◽  
Vulnerable Population ◽  
Recent Past ◽  
Childcare Centers ◽  
Infected People ◽  
First Case ◽  
Care Workers ◽  
Travel Restrictions ◽  
Containment Measures ◽  
Sport Centers

Since the beginning of March, when the first COVID-19 case was identified in Malta, the number of infected people has increased rapidly. As of April 1, 188 people have been diagnosed with COVID-19 in Malta. Contagion is not anymore limited to Maltese citizens who have travelled abroad or been in contact with foreign travelers in recent past. Two patients have already recovered and none has died. The authorities have responded swiftly with containment measures and early actions to mobilize the healthcare system. Before the first case was diagnosed in Malta, on March 7, the authorities dedicated facilities within hospitals and accelerated purchases of protective and respiratory equipment while training care workers. As the first cases were reported, travel restrictions were put in place. They were gradually tightened from a partial ban to a full suspension of inbound flights to Malta starting March 21. Social distancing measures have also gradually been stepped up, from partial quarantine measures for travelers to the cancelation of all mass activities, the shutdown of all schools, childcare centers, bars, restaurants, sport centers, non-essential shops and services and, since March 28, the full lockdown of the most vulnerable population.

scholarly journals A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy

2021 ◽  
Author(s):  
Paolo Di Giamberardino ◽  
Daniela Iacoviello ◽  
Federico Papa ◽  
Carmela Sinisgalli
Keyword(s):  
Group Structure ◽  
Human Mobility ◽  
Epidemiological Data ◽  
Group Model ◽  
Disease Evolution ◽  
Analytical Stability ◽  
Containment Measures ◽  
Disease Diffusion ◽  
The Impact ◽  
Second Wave

AbstractAn epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March–October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.

scholarly journals The COVID-19 pandemic in francophone West Africa: from the first cases to responses in seven countries

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
E. Bonnet ◽  
O. Bodson ◽  
F. Le Marcis ◽  
A. Faye ◽  
N. E. Sambieni ◽  
...  
Keyword(s):  
West Africa ◽  
Qualitative Data ◽  
Epidemiological Data ◽  
Economic Sectors ◽  
African Countries ◽  
The Public ◽  
Containment Measures ◽  
Set Up ◽  
Restrictive Measures ◽  
State Responses

Abstract Background In early March 2020, the COVID-19 pandemic hit West Africa. In response, countries in the region quickly set up crisis management committees and implemented drastic measures to stem the spread of the SARS-CoV-2 virus. The objective of this article is to analyse the epidemiological evolution of COVID-19 in seven Francophone West African countries (Benin, Burkina Faso, Côte d’Ivoire, Guinea, Mali, Niger, Senegal) as well as the public health measures decided upon during the first 7 months of the pandemic. Methods Our method is based on quantitative and qualitative data from the pooling of information from a COVID-19 data platform and collected by a network of interdisciplinary collaborators present in the seven countries. Descriptive and spatial analyses of quantitative epidemiological data, as well as content analyses of qualitative data on public measures and management committees were performed. Results Attack rates (October 2020) for COVID-19 have ranged from 20 per 100,000 inhabitants (Benin) to more than 94 per 100,000 inhabitants (Senegal). All these countries reacted quickly to the crisis, in some cases before the first reported infection, and implemented public measures in a relatively homogeneous manner. None of the countries implemented country-wide lockdowns, but some implemented partial or local containment measures. At the end of June 2020, countries began to lift certain restrictive measures, sometimes under pressure from the general population or from certain economic sectors. Conclusion Much research on COVID-19 remains to be conducted in West Africa to better understand the dynamics of the pandemic, and to further examine the state responses to ensure their appropriateness and adaptation to the national contexts.

scholarly journals A data-driven epidemiological model to explain the Covid-19 pandemic in multiple countries and help in choosing mitigation strategies

2020 ◽  
Author(s):  
Maria Jardim Beira ◽  
Anant Kumar ◽  
Lilia Perfeito ◽  
Joana Goncalves-Sa ◽  
Pedro Jose Sebastiao
Keyword(s):  
Epidemiological Data ◽  
Mitigation Strategies ◽  
Mitigation Measures ◽  
Policy Recommendations ◽  
Protection Measures ◽  
Collective Behaviors ◽  
Self Protection ◽  
The Impact ◽  
Government Level

Accurate models are fundamental to understand the dynamics of the COVID-19 pandemic and to evaluate different mitigation strategies. Here, we present a multi-compartmental model that fits the epidemiological data for eleven countries, despite the reduced number of fitting parameters. This model consistently explains the data for the daily infected, recovered, and dead over the first six months of the pandemic. The good quality of the fits makes it possible to explore different scenarios and evaluate the impact of both individual and collective behaviors and government- level decisions to mitigate the epidemic. We identify robust alternatives to lockdown, such as self- protection measures, and massive testing. Furthermore, communication and risk perception are fundamental to modulate the success of different strategies. The fitting/simulation tool is publicly available for use and test of other models, allowing for comparisons between different underlying assumptions, mitigation measures, and policy recommendations.

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