ePyDGGA: automatic configuration for fitting epidemic curves.

Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .

Socioeconomic reorganization of communication and mobility networks in response to external shocks.

Socioeconomic segregation patterns in networks usually evolve gradually, yet they can change abruptly in response to external shocks. The recent COVID-19 pandemic and the subsequent government policies induced several interruptions in societies, potentially disadvantaging the socioeconomically most vulnerable groups. Using large-scale digital behavioral observations as a natural laboratory, here we analyze how lockdown interventions lead to the reorganization of socioeconomic segregation patterns simultaneously in communication and mobility networks in Sierra Leone. We find that while segregation in mobility clearly increased during lockdown, the social communication network reorganized into a less segregated configuration as compared to reference periods. Moreover, due to differences in adaption capacities, the effects of lockdown policies varied across socioeconomic groups, leading to different or even opposite segregation patterns between the lower and higher socioeconomic classes. Such secondary effects of interventions need to be considered for better and more equitable policies.

Strengths and limitations of relative wealth indices derived from big data in Indonesia.

Accurate relative wealth estimates in Low and Middle-Income Countries (LMICS) are crucial to help policymakers address socio-demographic inequalities under the guidance of the Sustainable Development Goals set by the United Nations. Survey-based approaches have traditionally been employed to collect highly granular data about income, consumption, or household material goods to create index-based poverty estimates. However, these methods are only capture persons in households (i.e., in the household sample framework) and they do not include migrant populations or unhoused citizens. Novel approaches combining frontier data, computer vision, and machine learning have been proposed to complement these existing approaches. However, the strengths and limitations of these big-data-derived indices have yet to be sufficiently studied. In this paper, we focus on the case of Indonesia and examine one frontier-data derived Relative Wealth Index (RWI), created by the Facebook Data for Good initiative, that utilizes connectivity data from the Facebook Platform and satellite imagery data to produce a high-resolution estimate of relative wealth for 135 countries. We examine it concerning asset-based relative wealth indices estimated from existing high-quality national-level traditional survey instruments, the USAID-developed Demographic Health Survey (DHS), and the Indonesian National Socio-economic survey (SUSENAS). In this work, we aim to understand how the frontier-data derived index can be used to inform anti-poverty programs in Indonesia and the Asia Pacific region. First, we unveil key features that affect the comparison between the traditional and non-traditional sources, such as the publishing time and authority and the granularity of the spatial aggregation of the data. Second, to provide operational input, we hypothesize how a re-distribution of resources based on the RWI map would impact a current social program, the Social Protection Card (KPS) of Indonesia and assess impact. In this hypothetical scenario, we estimate the percentage of Indonesians eligible for the program, which would have been incorrectly excluded from a social protection payment had the RWI been used in place of the survey-based wealth index. The exclusion error in that case would be 32.82%. Within the context of the KPS program targeting, we noted significant differences between the RWI map's predictions and the SUSENAS ground truth index estimates.

Trade-offs between individual and ensemble forecasts of an emerging infectious disease.

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

Correction: Monitoring of the Venezuelan exodus through Facebook's advertising platform.

[This corrects the article DOI: 10.1371/journal.pone.0229175.].

Monitoring of the Venezuelan exodus through Facebook's advertising platform.

Venezuela is going through the worst economical, political and social crisis in its modern history. Basic products like food or medicine are scarce and hyperinflation is combined with economic depression. This situation is creating an unprecedented refugee and migrant crisis in the region. Governments and international agencies have not been able to consistently leverage reliable information using traditional methods. Therefore, to organize and deploy any kind of humanitarian response, it is crucial to evaluate new methodologies to measure the number and location of Venezuelan refugees and migrants across Latin America. In this paper, we propose to use Facebook's advertising platform as an additional data source for monitoring the ongoing crisis. We estimate and validate national and sub-national numbers of refugees and migrants and break-down their socio-economic profiles to further understand the complexity of the phenomenon. Although limitations exist, we believe that the presented methodology can be of value for real-time assessment of refugee and migrant crises world-wide.

Predicting and containing epidemic risk using on-line friendship networks.

To what extent can online social networks predict who is at risk of an infection? Many infections are transmitted through physical encounter between humans, but collecting detailed information about it can be expensive, might invade privacy, or might not even be possible. In this paper, we ask whether online social networks help predict and contain epidemic risk. Using a dataset from a popular online review service which includes over 100 thousand users and spans 4 years of activity, we build a time-varying network that is a proxy of physical encounter between its users (the encounter network) and a static network based on their reported online friendship (the friendship With computer simulations, we compare stochastic infection processes on the two networks, considering infections on the encounter network as the benchmark. First, we show that the friendship network is useful to identify the individuals at risk of infection, despite providing lower accuracy than the ideal case in which the encounters are known. This limited prediction accuracy is not only due to the static nature of the friendship network because a static version of the encounter network provides more accurate prediction of risk than the friendship network. Then, we show that periodical monitoring of the infection spreading on the encounter network allows to correct the infection predicted by a process spreading on the friendly staff ndship network, and achieves high prediction accuracy. Finally, we show that the friendship network contains valuable information to effectively contain epidemic outbreaks even when a limited budget is available for immunization. In particular, a strategy that immunizes random friends of random individuals achieves the same performance as knowing individuals' encounters at a small additional cost, even if the infection spreads on the encounter network.

Inferring mechanisms for global constitutional progress.

Constitutions help define domestic political orders, but are known to be influenced by international mechanisms that are normative, temporal and network based. Here we introduce the concept of the 'provision space'-the set of all legal provisions existing across the world's constitutions, which grows over time. We make use of techniques from network science and information retrieval to quantify and compare temporal and network effects on constitutional change, which have been the focus of previous work. Furthermore, we propose that hierarchical effects-a set of mechanisms by which the adoption of certain constitutional provisions leads to or facilitates the adoption of additional provisions-are also crucial. These hierarchical mechanisms appear to play an important role in the emergence of new political rights, and may therefore provide a useful roadmap for advocates of those rights.

Social media fingerprints of unemployment.

Recent widespread adoption of electronic and pervasive technologies has enabled the study of human behavior at an unprecedented level, uncovering universal patterns underlying human activity, mobility, and interpersonal communication. In the present work, we investigate whether deviations from these universal patterns may reveal information about the socio-economical status of geographical regions. We quantify the extent to which deviations in diurnal rhythm, mobility patterns, and communication styles across regions relate to their unemployment incidence. For this we examine a country-scale publicly articulated social media dataset, where we quantify individual behavioral features from over 19 million geo-located messages distributed among more than 340 different Spanish economic regions, inferred by computing communities of cohesive mobility fluxes. We find that regions exhibiting more diverse mobility fluxes, earlier diurnal rhythms, and more correct grammatical styles display lower unemployment rates. As a result, we provide a simple model able to produce accurate, easily interpretable reconstruction of regional unemployment incidence from their social-media digital fingerprints alone. Our results show that cost-effective economical indicators can be built based on publicly-available social media datasets.

Using friends as sensors to detect global-scale contagious outbreaks.

Recent research has focused on the monitoring of global-scale online data for improved detection of epidemics, mood patterns, movements in the stock market political revolutions, box-office revenues, consumer behaviour and many other important phenomena. However, privacy considerations and the sheer scale of data available online are quickly making global monitoring infeasible, and existing methods do not take full advantage of local network structure to identify key nodes for monitoring. Here, we develop a model of the contagious spread of information in a global-scale, publicly-articulated social network and show that a simple method can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a friend of each node to include in a group for local monitoring. Using six months of data from most of the full Twittersphere, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 7 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than expected due to network structure alone because highly central actors are both more active and exhibit increased diversity in the information they transmit to others. These results suggest that local monitoring is not just more efficient, but also more effective, and it may be applied to monitor contagious processes in global-scale networks.

Harnessing the interaction continuum for subtle assisted living.

People interact with each other in many levels of attention, intention and meaning. This Interaction Continuum is used daily to deal with different contexts, adapting the interaction to communication needs and available resources. Nevertheless, computer-supported interaction has mainly focused on the most direct, explicit and intrusive types of human to human Interaction such as phone calls, emails, or video conferences. This paper presents the results of exploring and exploiting the potentials of undemanding interaction mechanisms, paying special attention to subtle communication and background interaction. As we argue the benefits of this type of interaction for people with special needs, we present a theoretical framework to define it and propose a proof of concept based on Augmented Objects and a color codification mechanism. Finally, we evaluate and analyze the strengths and limitations of such approach with people with cognitive disabilities.