Community engagement in outbreak response: lessons from the 2014–2016 Ebola outbreak in Sierra Leone

Documentation of structured community engagement initiatives and real-time monitoring of community engagement activities during large-scale epidemics is limited. To inform such initiatives, this paper analyses the Community Led Ebola Action (CLEA) approach implemented through the Social Mobilization Action Consortium (SMAC) during the 2014–2016 Ebola epidemic in Sierra Leone. The SMAC initiative consisted of a network of 2466 community mobilisers, >6000 religious leaders and 42 local radio stations across all 14 districts of Sierra Leone. Community mobilisers were active in nearly 70% of all communities across the country using the CLEA approach to facilitate community analysis, trigger collective action planning and maintain community action plans over time. CLEA was complemented by interactive radio programming and intensified religious leader engagement.Community mobilisers trained in the CLEA approach used participatory methods, comprised of an initial community ‘triggering’ event, action plan development and weekly follow-ups to monitor progress on identified action items. Mobilisers collected operational and behavioural data on a weekly basis as part of CLEA. We conducted a retrospective analysis of >50 000 weekly reports from approximately 12 000 communities from December 2014 to September 2015. The data showed that 100% of the communities that were engaged had one or more action plans in place. Out of the 63 110 cumulative action points monitored by community mobilisers, 92% were marked as ‘in-progress’ (85%) or ‘achieved’ (7%) within 9 months. A qualitative examination of action points revealed that the in-progress status was indicative of the long-term sustainability of most action points (eg, continuous monitoring of visitors into the community) versus one-off action items that were marked as achieved (eg, initial installation of handwashing station). Analysis of behavioural outcomes of the intervention indicate an increase over time in the fraction of reported safe burials and fraction of reported cases referred for medical care within 24 hours of symptom onset in the communities that were engaged.Through CLEA, we have demonstrated how large-scale, coordinated community engagement interventions can be achieved and monitored in real-time during future Ebola epidemics and other similar epidemics. The SMAC initiative provides a practical model for the design, implementation and monitoring of community engagement, integration and coordination of community engagement interventions with other health emergency response pillars, and adaptive strategies for large-scale community-based operational data collection.

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Community Engagement during outbreak response: standards, approaches, and lessons from the 2014-2016 Ebola outbreak in Sierra Leone

10.1101/661959 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jamie Bedson ◽

Mohamed F. Jalloh ◽

Danielle Pedi ◽

Saiku M. Bah ◽

Katharine Owen ◽

...

Keyword(s):

Community Engagement ◽

Sierra Leone ◽

Action Planning ◽

Integrated Approach ◽

Community Action ◽

Social Mobilization ◽

Theory Of Change ◽

List Type ◽

Time Data ◽

Outbreak Response

Summary pointsThe Social Mobilization Action Consortium (SMAC) was Sierra Leone’s largest coordinated community engagement initiative during the 2014 - 2016 Ebola outbreak. It worked in all 14 districts in Sierra Leone across >12,000 communities (approximately 70% of all communities), through 2,466 trained Community Mobilizers, a network of 2,000 mosques and churches, and 42 local radio stations.We describe SMAC’s Theory of Change and utilization of the Community-Led Ebola Action (CLEA) approach. We present an extensive dataset of community engagement and monitoring with a focus on over 50,000 SMAC weekly reports collected by Community Mobilizers between December 2014 and September 2015.Community engagement and real-time data collection at scale is achievable in the context of a health emergency if adequately structured, managed, coordinated and resourced.We describe a correlation between systemic community engagement, community action planning and Ebola-safe behaviors at community-level.The SMAC integrated approach demonstrates the scope of data – including surveillance data - that can be generated directly by communities through structured community engagement interventions implemented at scale during an Ebola outbreak.We highlight important insights gleaned over time on how to informally integrate social mobilization into community-based surveillance of sick people and deaths.

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Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

Revista de Chimie ◽

10.37358/rc.17.12.5994 ◽

2018 ◽

Vol 68 (12) ◽

pp. 2857-2859

Author(s):

Cristina Mihaela Ghiciuc ◽

Andreea Silvana Szalontay ◽

Luminita Radulescu ◽

Sebastian Cozma ◽

Catalina Elena Lupusoru ◽

...

Keyword(s):

Real Time ◽

Medical Practice ◽

Salivary Cortisol ◽

Clinical Studies ◽

Large Scale ◽

Psychological Research ◽

Portable Devices ◽

Salivary Amylase ◽

Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

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A Model-Based Real-Time Intrusion Detection System for Large Scale Heterogeneous Networks

10.21236/ada420824 ◽

2003 ◽

Cited By ~ 1

Author(s):

Richard A. Kemmer ◽

Giovanni Vigna

Keyword(s):

Intrusion Detection ◽

Real Time ◽

Heterogeneous Networks ◽

Intrusion Detection System ◽

Large Scale ◽

Detection System ◽

Model Based

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Eternal Today

10.1093/oso/9780198827436.003.0007 ◽

2018 ◽

Author(s):

Jenni Myllykoski ◽

Anniina Rantakari

Keyword(s):

Real Time ◽

Constitutive Relation ◽

Human Agency ◽

Fundamental Characteristic ◽

Time Study ◽

Software Company ◽

Present Experience ◽

Managerial Strategy ◽

Over Time ◽

Strategy Making

This chapter focuses on temporality in managerial strategy making. It adopts an ‘in-time’ view to examine strategy making as the fluidity of the present experience and draws on a longitudinal, real-time study in a small Finnish software company. It shows five manifestations of ‘in-time’ processuality in strategy making, and identifies a temporality paradox that arises from the engagement of managers with two contradictory times: constructed linear ‘over time’ and experienced, becoming ‘in time’. These findings lead to the re-evaluation of the nature of intention in strategy making, and the authors elaborate the constitutive relation between time as ‘the passage of nature’ and human agency. Consequently, they argue that temporality should not be treated merely as an objective background or a subjective managerial orientation, but as a fundamental characteristic of processuality that defines the dynamics of strategy making.

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BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7197 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13849-13850

Author(s):

Donghyeon Lee ◽

Man-Je Kim ◽

Chang Wook Ahn

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Real Time ◽

Large Scale ◽

Outcome Predictor ◽

Short Term ◽

Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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Predictors of historical change in drug treatment coverage among people who inject drugs in 90 large metropolitan areas in the USA, 1993–2007

Substance Abuse Treatment Prevention and Policy ◽

10.1186/s13011-019-0235-0 ◽

2020 ◽

Vol 15 (1) ◽

Cited By ~ 1

Author(s):

Barbara Tempalski ◽

Leslie D. Williams ◽

Brooke S. West ◽

Hannah L. F. Cooper ◽

Stephanie Beane ◽

...

Keyword(s):

Public Health ◽

Drug Treatment ◽

Resource Availability ◽

People Who Inject Drugs ◽

Census Data ◽

Poverty Rate ◽

Community Action ◽

Historical Change ◽

Treatment Coverage ◽

Over Time

Abstract Background Adequate access to effective treatment and medication assisted therapies for opioid dependence has led to improved antiretroviral therapy adherence and decreases in morbidity among people who inject drugs (PWID), and can also address a broad range of social and public health problems. However, even with the success of syringe service programs and opioid substitution programs in European countries (and others) the US remains historically low in terms of coverage and access with regard to these programs. This manuscript investigates predictors of historical change in drug treatment coverage for PWID in 90 US metropolitan statistical areas (MSAs) during 1993–2007, a period in which, overall coverage did not change. Methods Drug treatment coverage was measured as the number of PWID in drug treatment, as calculated by treatment entry and census data, divided by numbers of PWID in each MSA. Variables suggested by the Theory of Community Action (i.e., need, resource availability, institutional opposition, organized support, and service symbiosis) were analyzed using mixed-effects multivariate models within dependent variables lagged in time to study predictors of later change in coverage. Results Mean coverage was low in 1993 (6.7%; SD 3.7), and did not increase by 2007 (6.4%; SD 4.5). Multivariate results indicate that increases in baseline unemployment rate (β = 0.312; pseudo-p < 0.0002) predict significantly higher treatment coverage; baseline poverty rate (β = − 0.486; pseudo-p < 0.0001), and baseline size of public health and social work workforce (β = 0.425; pseudo-p < 0.0001) were predictors of later mean coverage levels, and baseline HIV prevalence among PWID predicted variation in treatment coverage trajectories over time (baseline HIV * Time: β = 0.039; pseudo-p < 0.001). Finally, increases in black/white poverty disparity from baseline predicted significantly higher treatment coverage in MSAs (β = 1.269; pseudo-p < 0.0001). Conclusions While harm reduction programs have historically been contested and difficult to implement in many US communities, and despite efforts to increase treatment coverage for PWID, coverage has not increased. Contrary to our hypothesis, epidemiologic need, seems not to be associated with change in treatment coverage over time. Resource availability and institutional opposition are important predictors of change over time in coverage. These findings suggest that new ways have to be found to increase drug treatment coverage in spite of economic changes and belt-tightening policy changes that will make this difficult.

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Analysis of 220 Years of Floodplain Population Dynamics in the US at Different Spatial Scales

Water ◽

10.3390/w13020141 ◽

2021 ◽

Vol 13 (2) ◽

pp. 141

Author(s):

Firoza Akhter ◽

Maurizio Mazzoleni ◽

Luigia Brandimarte

Keyword(s):

Population Dynamics ◽

Large Scale ◽

Spatial Scales ◽

National Level ◽

State Level ◽

Economic Activities ◽

Protection Measures ◽

Management Planning ◽

The Us ◽

Over Time

In this study, we explore the long-term trends of floodplain population dynamics at different spatial scales in the contiguous United States (U.S.). We exploit different types of datasets from 1790–2010—i.e., decadal spatial distribution for the population density in the US, global floodplains dataset, large-scale data of flood occurrence and damage, and structural and nonstructural flood protection measures for the US. At the national level, we found that the population initially settled down within the floodplains and then spread across its territory over time. At the state level, we observed that flood damages and national protection measures might have contributed to a learning effect, which in turn, shaped the floodplain population dynamics over time. Finally, at the county level, other socio-economic factors such as local flood insurances, economic activities, and socio-political context may predominantly influence the dynamics. Our study shows that different influencing factors affect floodplain population dynamics at different spatial scales. These facts are crucial for a reliable development and implementation of flood risk management planning.

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Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model

Transportation Research Part A Policy and Practice ◽

10.1016/j.tra.2021.03.011 ◽

2021 ◽

Vol 148 ◽

pp. 155-182

Author(s):

Mads Paulsen ◽

Thomas Kjær Rasmussen ◽

Otto Anker Nielsen

Keyword(s):

Real Time ◽

Public Transport ◽

Large Scale ◽

Choice Model ◽

Real Time Information ◽

Time Information

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Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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