scholarly journals Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis

2021 ◽  
pp. 096228022110370
Author(s):  
Edwin van Leeuwen ◽  
Frank Sandmann ◽  
Keyword(s):  
Public Health ◽  
Time Use ◽  
Large Scale ◽  
Leisure Activities ◽  
Public Health Intervention ◽  
Social Contact ◽  
Daily Activities ◽  
Transmission Model ◽  
Fine Grained ◽  
The Impact

Social distancing is an important public health intervention to reduce or interrupt the sustained community transmission of emerging infectious pathogens, such as severe acute respiratory syndrome-coronavirus-2 during the coronavirus disease 2019 pandemic. Contact matrices are typically used when evaluating such public health interventions to account for the heterogeneity in social mixing of individuals, but the surveys used to obtain the number of contacts often lack detailed information on the time individuals spend on daily activities. The present work addresses this problem by combining the large-scale empirical data of a social contact survey and a time-use survey to estimate contact matrices by age group (0--15, 16--24, 25–44, 45–64, 65+ years) and daily activity (work, schooling, transportation, and four leisure activities: social visits, bar/cafe/restaurant visits, park visits, and non-essential shopping). This augmentation allows exploring the impact of fewer contacts when individuals reduce the time they spend on selected daily activities as well as when lifting such restrictions again. For illustration, the derived matrices were then applied to an age-structured dynamic-transmission model of coronavirus disease 2019. Findings show how contact matrices can be successfully augmented with time-use data to inform the relative reductions in contacts by activity, which allows for more fine-grained mixing patterns and infectious disease modelling.

scholarly journals Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis

2020 ◽  
Author(s):  
Edwin van Leeuwen ◽  
Frank Sandmann ◽  
Keyword(s):  
Time Use ◽  
Large Scale ◽  
Close Contact ◽  
Public Health Intervention ◽  
Social Contact ◽  
Social Activities ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Mixing Patterns ◽  
The Impact

AbstractBackgroundSocial distancing is an important public health intervention to reduce or interrupt the sustained community transmission of emerging infectious pathogens, such as SARS-CoV-2 during the coronavirus disease 2019 (COVID-19) pandemic. We aimed to explore the impact on the epidemic curve of fewer contacts when individuals reduce the time they spend on selected daily activities.MethodsWe combined the large-scale empirical data of a social contact survey and a time-use survey to estimate contact matrices by age group (0-15, 16-24, 25-44, 45-64, 65+) and daily activity (work, schooling, transportation, and four leisure activities: social visits, bar/cafe/restaurant visits, park visits, and non-essential shopping). We assumed that reductions in time are proportional to reductions in contacts. The derived matrices were then applied in an age-structured dynamic-transmission model of COVID-19 to explore the effects.FindingsThe relative reductions in the derived contact matrices were highest when closing schools (in ages 0-14 years), workplaces (15-64 years), and stopping social visits (65+ years). For COVID-19, the closure of workplaces, schools, and stopping social visits had the largest impact on reducing the epidemic curve and delaying its peak, while the predicted impact of fewer contacts in parks, bars/cafes/restaurants, and non-essential shopping were minimal.InterpretationWe successfully augmented contact matrices with time-use data to predict the highest impact of social distancing measures from reduced contacts when spending less time at work, school, and on social visits. Although the predicted impact from other leisure activities with potential for close physical contact were minimal, changes in mixing patterns and time-use immediately after re-allowing social activities may pose increased short-term transmission risks, especially in potentially crowded environments indoors.Research in contextEvidence before this studyWe searched PubMed for mathematical models using social contact matrices and time-use data to explore the impact of reduced social contacts as seen from social distancing measures adopted during the coronavirus disease 2019 (COVID-19) pandemic with the search string ((social OR physical) AND distancing) OR (contact* OR (contact matri*)) AND (time-use) AND (model OR models OR modeling OR modelling) from inception to May 06, 2020, with no language restrictions. We found several studies that used time-use data to re-create contact matrices based on time spent in similar locations or to calculate the length of exposure. We identified no study that augmented social contact matrices with time-use data to estimate the impact on transmission dynamics of reducing selected social activities and lifting these restrictions again, as seen during the COVID-19 pandemic.Added value of this studyOur study combines the empirical data of two large-scale, representative surveys to derive social contact matrices that enrich the frequency of contacts with the duration of exposure for selected social activities, which allows for more fine-grained mixing patterns and infectious disease modelling. We successfully applied the resulting matrices to estimate reductions in contacts from social distancing measures such as adopted during the COVID-19 pandemic, as well as the effect on the epidemic curve from increased social contacts when lifting such restrictions again.Implications of all the available evidenceSocial distancing measures are an important public health intervention to limit the close-contact transmission of emerging infectious pathogens by reducing the social mixing of individuals. Our model findings suggest a higher fraction of close-contact transmission occurs at work, schools, and social visits than from visits to parks, bars/cafes/restaurants, and non-essential shopping. The minimal predicted impact is suggestive of lifting the restrictions on certain activities and excluding them from the list of social distancing measures, unless required to maintain sufficient healthcare capacity. However, potential replacement effects of activities and in mixing patterns remain unclear, particularly immediately after re-allowing social activities again.

The complication of cyber manhunt: How cyber manhunt further complicated the ethical conflict between breaching the confidentiality and endangering the society in face of the COVID-19 pandemic (Preprint)

2021 ◽  
Author(s):  
Yen-Chang Chen ◽  
Yen-Yuan Chen
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Health Workers ◽  
Early Stage ◽  
Personal Information ◽  
Public Health Intervention ◽  
Contact Tracing ◽  
Online Activity ◽  
Contact Investigation ◽  
The Impact

UNSTRUCTURED While health care and public health workers are working on measures to mitigate the COVID-19 pandemic, there is an unprecedentedly large number of people spending much more time indoors, and relying heavily on the Internet as their lifeline. What has been overlooked is the influence of the increasing online activities on public health issues. In this article, we pointed out how a large-scale online activity called cyber manhunt may threaten to offset the efficacy of contact tracing investigation, a public health intervention considered highly effective in limiting further transmission in the early stage of a highly contagious disease outbreak such as the COVID-19 pandemic. In the first section, we presented a case to show how personal information obtained from contact investigation and disclosed in part on the media provoked a vehement cyber manhunt. We then discussed the possible reasons why netizens collaborate to reveal anonymized personal information about contact investigation, and specify, from the perspective of public health and public health ethics, four problems of cyber manhunt, including the lack of legitimate public health goals, the concerns about privacy breach, the impact of misinformation, and social inequality. Based on our analysis, we concluded that more moral weight may be given to protecting one's confidentiality, especially in an era with the rapid advance of digital and information technologies.

scholarly journals Impact of non-pharmaceutical interventions on the COVID-19 epidemic: A modelling study

2020 ◽  
Vol 8 ◽  
pp. 205031212097946
Author(s):  
Salah Al Awaidy ◽  
Ozayr Mahomed
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Disease Transmission ◽  
Large Scale ◽  
Public Health Intervention ◽  
Transmission Model ◽  
Early Implementation ◽  
The Government ◽  
The Impact ◽  
Pharmaceutical Interventions

Objective: This study aimed to assess the impact of non-pharmaceutical interventions on the COVID-19 epidemic in Oman. Methods: Data were retrieved from published national surveillance data between 24 February and 30 June 2020. To show the impact of the Government introduced public health intervention early in the epidemic, we used a simple disease-transmission model equation of the 2019-n CoV epidemic. Results: From all confirmed cases, the rates of intensive care unit admission were 4.56% (1824). We estimated an R0 of 3.11 with no intervention would result in nearly the entire population of Oman being infected within 65 days. A reduction of the R0 to 1.51 provided an estimated 89,056 confirmed cases, with 167 deaths or 0.4% mortality by June 30 with a requirement of 4052 intensive care unit beds. The current scenario (24 February to 30 June 2020) indicates an R0 of 1.41, resulting in 40,070 confirmed COVID-19 cases, 176 deaths and 69% of confirmed cases recovered. Conclusion: In early implementation of non-pharmaceutical interventions, an intensive lockdown has had a profound impact on the mitigation of a large-scale COVID-19 outbreak in Oman.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pietro Coletti ◽  
Pieter Libin ◽  
Oana Petrof ◽  
Lander Willem ◽  
Steven Abrams ◽  
...  
Keyword(s):  
Phase 1 ◽  
Leisure Activities ◽  
National Level ◽  
Social Contact ◽  
Exit Strategy ◽  
Initial Wave ◽  
Contact Data ◽  
Stochastic Data ◽  
The Impact ◽  
Second Wave

Abstract Background In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. Methods We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. Results Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. Conclusions Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.

scholarly journals The influence of risk perceptions on close contact frequency during the SARS-CoV-2 pandemic

2021 ◽  
Author(s):  
James Wambua ◽  
Lisa Hermans ◽  
Pietro Coletti ◽  
Frederik Verelst ◽  
Lander Willem ◽  
...  
Keyword(s):  
Public Health ◽  
Health Communication ◽  
Risk Perceptions ◽  
Close Contact ◽  
Social Contact ◽  
Perceived Severity ◽  
Public Health Communication ◽  
Contact Behaviour ◽  
Contact Frequency ◽  
The Impact

Abstract Human behaviour is known to be crucial in the propagation of infectious diseases through respiratory or close-contact routes like the current SARS-CoV-2 virus. Intervention measures implemented to curb the spread of the virus mainly aim at limiting the number of close contacts, until vaccine roll-out is complete. Our main objective was to assess the relationships between SARS-CoV-2 perceptions and social contact behaviour in Belgium. Understanding these relationships is crucial to maximize interventions' effectiveness, e.g. by tailoring public health communication campaigns. In this study, we surveyed a representative sample of adults in Belgium in two longitudinal surveys (8 waves of survey 1 in April 2020 to August 2020, and 11 waves of survey 2 in November 2020 to April 2021). Generalized linear mixed effects models were used to analyse the two surveys. Participants with low and neutral perceptions on perceived severity made a significantly higher number of social contacts as compared to participants with high levels of perceived severity after controlling for other variables. Furthermore, participants with higher levels of perceived effectiveness of measures and perceived adherence to measures made fewer contacts. However, the differences were small. Our results highlight the key role of perceived severity on social contact behaviour during a pandemic. Nevertheless, additional research is required to investigate the impact of public health communication on severity of COVID-19 in terms of changes in social contact behaviour.

scholarly journals Geospatial Analysis and Mapping Strategies for Fine Grained and Detailed COVID-19 Data With Geographic Information Systems

2021 ◽  
Author(s):  
ÁNGEL MIRAMONTES CARBALLADA ◽  
JOSE BALSA-BARREIRO
Keyword(s):  
Geographic Location ◽  
Social Contact ◽  
Temporal Behaviour ◽  
Fine Grained ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Spatio Temporal ◽  
Media Reports ◽  
Predictive Risk ◽  
The Impact

Abstract The CoVID-19 pandemic is showing a dramatic impact across the world. To the tragedy of the loss of human lives, we must add the great uncertainty that the new coronavirus is causing to our lives. Governments and public health authorities must be able to respond this emergency by taking the appropriate decisions for minimizing the impact of the virus. In the absence of an immediate solution, governments have concentrated their efforts on adopting non-pharmaceutical interventions for restricting the mobility of people and reducing the social contact. Health authorities are publishing most of data for supporting their interventions and policies. The geographic location of the cases is a vital information with exceptional value for analysing the spatio-temporal behaviour of the virus, doing feasible to anticipate potential outbreaks and to elaborate predictive risk mapping. In fact, a great number of media reports, research papers, and web-browsers have presented the COVID-19 disease spreading by using maps. However, processing and visualization of this sort of data presents some aspects that must be carefully reviewed. Based on our experience with fine-grained and detailed data related to COVID-19 in a Spanish region, we present a bunch of mapping strategies and good practices using geospatial tools. The ultimate goal is create appropriate maps at any spatial scale while avoiding conflicts with data such as those related to patients’ privacy.

scholarly journals Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020 ◽  
Vol 12 (18) ◽  
pp. 3053 ◽  
Author(s):  
Thorsten Hoeser ◽  
Felix Bachofer ◽  
Claudia Kuenzer
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Object Detection ◽  
Land Surface ◽  
Large Scale ◽  
Earth Observation ◽  
Observation Data ◽  
Fine Grained ◽  
The Impact ◽  
Object Dynamics

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

scholarly journals Years of life lost due to the psychosocial consequences of COVID-19 mitigation strategies based on Swiss data

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Dominik A. Moser ◽  
Jennifer Glaus ◽  
Sophia Frangou ◽  
Daniel S. Schechter
Keyword(s):  
Public Health ◽  
Significant Risk ◽  
Social Contact ◽  
Mitigation Strategies ◽  
Mitigation Measures ◽  
Years Of Life Lost ◽  
Psychosocial Consequence ◽  
Psychosocial Consequences ◽  
The Impact

Abstract Background. The pandemic caused by coronavirus disease 2019 (COVID-19) has forced governments to implement strict social mitigation strategies to reduce the morbidity and mortality from acute infections. These strategies, however, carry a significant risk for mental health, which can lead to increased short-term and long-term mortality and is currently not included in modeling the impact of the pandemic. Methods. We used years of life lost (YLL) as the main outcome measure, applied to Switzerland as an example. We focused on suicide, depression, alcohol use disorder, childhood trauma due to domestic violence, changes in marital status, and social isolation, as these are known to increase YLL in the context of imposed restriction in social contact and freedom of movement. We stipulated a minimum duration of mitigation of 3 months based on current public health plans. Results. The study projects that the average person would suffer 0.205 YLL due to psychosocial consequence of COVID-19 mitigation measures. However, this loss would be entirely borne by 2.1% of the population, who will suffer an average of 9.79 YLL. Conclusions. The results presented here are likely to underestimate the true impact of the mitigation strategies on YLL. However, they highlight the need for public health models to expand their scope in order to provide better estimates of the risks and benefits of mitigation.

scholarly journals How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sam McCrabb ◽  
Kaitlin Mooney ◽  
Benjamin Elton ◽  
Alice Grady ◽  
Sze Lin Yoong ◽  
...  
Keyword(s):  
Public Health ◽  
Scoping Review ◽  
Implementation Strategy ◽  
Public Health Intervention ◽  
Health Intervention ◽  
Health Interventions ◽  
Problem Identification ◽  
Key Concepts ◽  
The Common ◽  
The Impact

Abstract Background Optimisation processes have the potential to rapidly improve the impact of health interventions. Optimisation can be defined as a deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts within resource constraints. This study aimed to identify frameworks used to optimise the impact of health interventions and/or their implementation, and characterise the key concepts, steps or processes of identified frameworks. Methods A scoping review of MEDLINE, CINAL, PsycINFO, and ProQuest Nursing & Allied Health Source databases was undertaken. Two reviewers independently coded the key concepts, steps or processes involved in each frameworks, and identified if it was a framework aimed to optimise interventions or their implementation. Two review authors then identified the common steps across included frameworks. Results Twenty optimisation frameworks were identified. Eight frameworks were for optimising interventions, 11 for optimising implementation and one covered both intervention and implementation optimisation. The mean number of steps within the frameworks was six (range 3–9). Almost half (n = 8) could be classified as both linear and cyclic frameworks, indicating that some steps may occur multiple times in a single framework. Two meta-frameworks are proposed, one for intervention optimisation and one for implementation strategy optimisation. Steps for intervention optimisation are: Problem identification; Preparation; Theoretical/Literature base; Pilot/Feasibility testing; Optimisation; Evaluation; and Long-term implementation. Steps for implementation strategy optimisation are: Problem identification; Collaborate; Plan/design; Pilot; Do/change; Study/evaluate/check; Act; Sustain/endure; and Disseminate/extend. Conclusions This review provides a useful summary of the common steps followed to optimise a public health intervention or its implementation according to established frameworks. Further opportunities to study and/or validate such frameworks and their impact on improving outcomes exist.

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