Modelling the propagation of social response during a disease outbreak

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.     

School closures reduced social mixing of children during COVID-19 with implications for transmission risk and school reopening policies

School closures may reduce the size of social networks among children, potentially limiting infectious disease transmission. To estimate the impact of K–12 closures and reopening policies on children''s social interactions and COVID-19 incidence in California''s Bay Area, we collected data on children''s social contacts and assessed implications for transmission using an individual-based model. Elementary and Hispanic children had more contacts during closures than high school and non-Hispanic children, respectively. We estimated that spring 2020 closures of elementary schools averted 2167 cases in the Bay Area (95% CI: −985, 5572), fewer than middle (5884; 95% CI: 1478, 11.550), high school (8650; 95% CI: 3054, 15 940) and workplace (15 813; 95% CI: 9963, 22 617) closures. Under assumptions of moderate community transmission, we estimated that reopening for a four-month semester without any precautions will increase symptomatic illness among high school teachers (an additional 40.7% expected to experience symptomatic infection, 95% CI: 1.9, 61.1), middle school teachers (37.2%, 95% CI: 4.6, 58.1) and elementary school teachers (4.1%, 95% CI: −1.7, 12.0). However, we found that reopening policies for elementary schools that combine universal masking with classroom cohorts could result in few within-school transmissions, while high schools may require masking plus a staggered hybrid schedule. Stronger community interventions (e.g. remote work, social distancing) decreased the risk of within-school transmission across all measures studied, with the influence of community transmission minimized as the effectiveness of the within-school measures increased.     

Rethinking the history of plague in the time of COVID-19

We are currently experiencing one of the most disruptive pandemics in modern history. The outbreak of COVID-19 that was first recorded in Wuhan, China and quickly spread across the globe has resulted in nearly 5 million confirmed cases to date and more than 300,000 deaths. Where we stand now, it is still uncertain how many it will infect or kill worldwide, how long it will continue, and when—if ever—life will return to normal. What we know for sure is that this is a pivotal moment and that we are experiencing a historic event that will transform our societies both profoundly and irreversibly. As we wade into this new age of pandemics, it is critical to rethink how we write the history of pandemics. With a conviction that the past helps us to understand the present and that the present should help us to rethink the past, I turn to the legacy of past plagues. In this essay, I take stock of the lasting legacies of past plagues because they continue to shape the way we think about new pandemics. In particular, I address persistent problems, such as European exceptionalism, triumphalism, and epidemiological Orientalism, that are not only ubiquitous in plague studies, but also staples of public opinion about pandemics, past and present.     

基于地理信息的疫情可视化设计探讨——以《重庆市新型冠状病毒肺炎疫情地图》可视化设计为例

通过对新型冠状病毒肺炎疫情信息和数据的分析,探讨地理信息在疫情可视化设计中的方法及其艺术与社会价值。运用可视化、地图学的相关理论,辅以案例分析与实践性研究。从地理信息的位置、数量、关系等角度出发,聚合疫情信息和数据,借鉴信息可视化的方法,构建视觉语言的颜色、尺寸、形态等视觉化变量,完成对疫情地图的设计创作。将地理信息作为信息的模型与维度,探究可视化设计,提出疫情可视化设计的具体方法。疫情地图提高了时间与空间的信息精度,增强了用户的理解,可以为疫情区域防控决策提供一定的参考和支持。     

The spatial dissemination of COVID-19 and associated socio-economic consequences

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.     

Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79–116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.     

Modelling the impact of household size distribution on the transmission dynamics of COVID-19

Under the implementation of non-pharmaceutical interventions such as social distancing and lockdowns, household transmission has been shown to be significant for COVID-19, posing challenges for reducing incidence in settings where people are asked to self-isolate at home and to spend increasing amounts of time at home due to distancing measures. Accordingly, characteristics of households in a region have been shown to relate to transmission heterogeneity of the virus. We introduce a discrete-time stochastic epidemiological model to examine the impact of the household size distribution in a region on the transmission dynamics. We choose parameters to reflect incidence in two health regions of the Greater Vancouver area in British Columbia and simulate the impact of distancing measures on transmission, with household size distribution the only different parameter between simulations for the two regions. Our result suggests that the dissimilarity in household size distribution alone can cause significant differences in incidence of the two regions, and the distributions drive distinct dynamics that match reported cases. Furthermore, our model suggests that offering individuals a place to isolate outside their household can speed the decline in cases, and does so more effectively where there are more larger households.     

面向新冠疫情防控科普宣传的交互装置设计研究

目的 为进一步化解当前疫情防控中人们易于产生的焦虑情绪及遏制谣言传播,进行面向疫情防控科普宣传的交互装置设计,以此增强防疫科普的多维度体验感和情感交互性,提高受众学习的内驱力和对疫情防控的正向关注。方法 以“装置”为表达媒介作为防疫科普宣传的重要手段,探索了影像装置艺术与科普教育相融合的发展趋势,并分析了超声波测距传感器在触控交互界面设计中的应用,实现了“病.口”为题的新冠疫情防控科普装置设计探索,据此开展了疫情防控科普教育影像的交互展示。结论 建立了防疫科普影像装置系统,为大众开展防疫科学教育提供交互式学习手段,使受众感受到防疫科普宣传的新颖性,构建起防疫、科普、艺术的有机联系,为宣传防疫知识和开展防疫科普教育提供借鉴。     

关于新冠肺炎疫情防控中计量保障工作的几点思考

本文总结了新型冠状病毒肺炎疫情防控中所涉及的计量器具类别,以及对其进行检定、校准、检测等量值保障活动所适用的技术规范。围绕计量工作在新型冠状病毒肺炎疫情防控中的重要作用,文章重点从体温筛查、设备消毒、病毒检测、临床诊断、医疗救治、科学研究等方面分析了计量工作对新冠肺炎疫情防控的技术支撑和量值保障作用。为各疫情防控单位和医疗机构判断防控设备测量结果是否准确、可靠等问题是提供解决路径,也为各级计量技术机构和广大计量工作者针对疫情防控建立相关计量标准提供参考。     

Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

Novel Coronavirus-19 (COVID-19) is a newer type of coronavirus that has not been formally detected in humans. It is established that this disease often affects people of different age groups, particularly those with body disorders, blood pressure, diabetes, heart problems, or weakened immune systems. The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates. Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans. It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken. The latest global coronavirus epidemic (COVID-19) has brought new challenges to the scientific community. Artificial Intelligence (AI)-motivated methodologies may be useful in predicting the conditions, consequences, and implications of such an outbreak. These forecasts may help to monitor and prevent the spread of these outbreaks. This article proposes a predictive framework incorporating Support Vector Machines (SVM) in the forecasting of a potential outbreak of COVID-19. The findings indicate that the suggested system outperforms cutting-edge approaches. The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance. The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity. The proposed SVM system model exhibits 98.88% and 96.79% result in terms of accuracy during training and validation respectively.     

Modelling preventive measures and their effect on generation times in emerging epidemics

We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infectiousness processes, so that each individual has an independent infectivity profile. In particular, we monitor variations of the reproduction number and of the distribution of generation times. We show that some interventions, i.e. uniform reduction and vaccination, affect the former while leaving the latter unchanged, whereas other interventions, i.e. isolation, screening and contact tracing, affect both quantities. We provide a theoretical analysis of the variation of these quantities, and we show that, in practice, the variation of the generation time distribution can be significant and that it can cause biases in the estimation of reproduction numbers. The framework, because of its general nature, captures the properties of many infectious diseases, but particular emphasis is on COVID-19, for which numerical results are provided.     

Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector machine regression (SVMR) algorithms. According to the regression tree results, the age variable plays a predominant role in health habits, safety behaviors, and anxiety. The health habit index, which focuses on the extent of behavioral change toward the commitment to use the health and protection methods, is highly affected by gender and age factors. The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period. The results of the SVMR model reveal a strong positive effect of income, with R² values of 99.59%, 99.93% and 99.88% corresponding to health habits, safety behaviors, and anxiety.     

Modelling seasonal influenza: the role of weather and punctuated antigenic drift

Seasonal influenza appears as annual oscillations in temperate regions of the world, yet little is known as to what drives these annual outbreaks and what factors are responsible for their inter-annual variability. Recent studies suggest that weather variables, such as absolute humidity, are the key drivers of annual influenza outbreaks. The rapid, punctuated, antigenic evolution of the influenza virus is another major factor. We present a new framework for modelling seasonal influenza based on a discrete-time, age-of-infection, epidemic model, which allows the calculation of the model''s likelihood function in closed form. This framework may be used to perform model inference and parameter estimation rigorously. The modelling approach allows us to fit 11 years of Israeli influenza data, with the best models fitting the data with unusually high correlations in which r > 0.9. We show that using actual weather to modulate influenza transmission rate gives better results than using the inter-annual means of the weather variables, providing strong support for the role of weather in shaping the dynamics of influenza. This conclusion remains valid even when incorporating a more realistic depiction of the decay of immunity at the population level, which allows for discrete changes in immunity from year to year.     

Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data

Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and contain re-emergence phenomena. Targeted measures such as case isolation and contact tracing can alleviate the societal cost of lock-downs by containing the spread where and when it occurs. To assess the relative and combined impact of manual contact tracing (MCT) and digital (app-based) contact tracing, we feed a compartmental model for COVID-19 with high-resolution datasets describing contacts between individuals in several contexts. We show that the benefit (epidemic size reduction) is generically linear in the fraction of contacts recalled during MCT and quadratic in the app adoption, with no threshold effect. The cost (number of quarantines) versus benefit curve has a characteristic parabolic shape, independent of the type of tracing, with a potentially high benefit and low cost if app adoption and MCT efficiency are high enough. Benefits are higher and the cost lower if the epidemic reproductive number is lower, showing the importance of combining tracing with additional mitigation measures. The observed phenomenology is qualitatively robust across datasets and parameters. We moreover obtain analytically similar results on simplified models.     

On the predictive ability of mechanistic models for the Haitian cholera epidemic

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management.     

Socio-economic determinants of mobility responses during the first wave of COVID-19 in Italy: from provinces to neighbourhoods

After more than 1 year into the COVID-19 pandemic, governments worldwide still face the challenge of adopting non-pharmaceutical interventions to mitigate the risks posed by the emergence of new SARS-CoV-2 variants and the lack of a worldwide equitable vaccine allocation. Thus, it becomes crucial to identify the drivers of mobility responses to mitigation efforts during different restriction regimes, for planning interventions that are both economically and socially sustainable while effective in controlling an outbreak. Here, using anonymous and privacy-enhanced cell phone data from Italy, we investigate the determinants of spatial variations of reductions in mobility and co-location in response to the adoption and the lift of restrictions, considering both provinces and city neighbourhoods. In large urban areas, our analysis uncovers the desertification of historic city centres, which persisted after the end of the lockdown. Such centre-periphery gradient was mainly associated with differences in educational attainment. At the province level, the local structure of the labour market mainly explained the variations in mobility responses, together with other demographic factors, such as the population’s age and sex composition. In the future, targeted interventions should take into account how the ability to comply with restrictions varies across geographical areas and socio-demographic groups.     

Managing the risk of a COVID-19 outbreak from border arrivals

In an attempt to maintain the elimination of COVID-19 in New Zealand, all international arrivals are required to spend 14 days in government-managed quarantine and to return a negative test result before being released. We model the testing, isolation and transmission of COVID-19 within quarantine facilities to estimate the risk of community outbreaks being seeded at the border. We use a simple branching process model for COVID-19 transmission that includes a time-dependent probability of a false-negative test result. We show that the combination of 14-day quarantine with two tests is highly effective in preventing an infectious case entering the community, provided there is no transmission within quarantine facilities. Shorter quarantine periods, or reliance on testing only with no quarantine, substantially increases the risk of an infectious case being released. We calculate the fraction of cases detected in the second week of their two-week stay and show that this may be a useful indicator of the likelihood of transmission occurring within quarantine facilities. Frontline staff working at the border risk exposure to infected individuals and this has the potential to lead to a community outbreak. We use the model to test surveillance strategies and evaluate the likely size of the outbreak at the time it is first detected. We conclude with some recommendations for managing the risk of potential future outbreaks originating from the border.     

疫情可视化设计中的数据分析与表达方法研究——以“重庆市新型冠状病毒肺炎疫情数据可视化分析”为例

围绕疫情数据可视化这一主题,研究如何针对突发事件的海量数据进行数据分析和可视化表达。在数据量大、内容繁杂的背景下,基于可视化设计的方法论,探讨数据可视化在突发公共卫生事件数据报道中的优势,并以“重庆市新型冠状病毒肺炎疫情数据可视化分析”设计为案例,具体分析数据可视化从设计方法到结论的过程。分析、整理适用于突发公共卫生事件下的疫情可视化设计数据分析和表达方法,为突发公共事件的设计介入、数据可视化的设计方法提供一定的补充。     

Social TV viewing during the COVID-19 lockdown: The mediating role of social presence

The COVID-19 lockdown has transformed the way of life for many people. One key change is media intake, as many individuals reported an increase in media consumption during the COVID-19 lockdown. Specifically, social media and television usage increased. In this regard, the present study examines social TV viewing, the simultaneous use of watching TV while communicating with others about the TV content on various communication technologies, during the COVID-19 lockdown. An online survey was conducted to collect data from college students in the United States during the COVID-19 lockdown. Primary results indicate that different motives predict different uses of communication platforms for social TV engagement, such as public platforms, text-based private platforms, and video-based private platforms. Specifically, the social motive significantly predicts social TV engagement on most of the platforms. Further, the study finds that social presence of virtual co-viewers mediates the relationship between social TV engagement and social TV enjoyment. Overall, the study's findings provide a meaningful understanding of social TV viewing when physical social gatherings are restricted.     

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients’ data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy. Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results. In this paper, we used SqueezNet with a modified output layer to classify X-ray images into three groups: COVID-19, normal, and pneumonia. In this study, we propose a deep learning method with enhance the features of X-ray images collected from Kaggle, Figshare to distinguish between COVID-19, Normal, and Pneumonia infection. In this regard, several techniques were used on the selected image samples which are Unsharp filter, Histogram equal, and Complement image to produce another view of the dataset. The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type (COVID-19, Normal and Pneumonia). In the first scenario, the model has been tested without any enhancement on the datasets. It achieved an accuracy of 91%. But, in the second scenario, the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%. The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images. A comparison of the outcomes demonstrated the effectiveness of our DL method for classifying COVID-19 based on enhanced X-ray images.