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Predicting forced displacement is an important undertaking of many humanitarian aid agencies, which must anticipate flows in advance in order to provide vulnerable refugees and Internally Displaced Persons (IDPs) with shelter, food, and medical care. While there is a growing interest in using machine learning to better anticipate future arrivals, there is little standardized knowledge on how to predict refugee and IDP flows in practice. Researchers and humanitarian officers are confronted with the need to make decisions about how to structure their datasets and how to fit their problem to predictive analytics approaches, and they must choose from a variety of modeling options. Most of the time, these decisions are made without an understanding of the full range of options that could be considered, and using methodologies that have primarily been applied in different contexts - and with different goals - as opportunistic references. In this work, we attempt to facilitate a more comprehensive understanding of this emerging field of research by providing a systematic model-agnostic framework, adapted to the use of big data sources, for structuring the prediction problem. As we do so, we highlight existing work on predicting refugee and IDP flows. We also draw on our own experience building models to predict forced displacement in Somalia, in order to illustrate the choices facing modelers and point to open research questions that may be used to guide future work.

Does YouTube facilitate the spread of anti-vaccine sentiment online? Leveraging the collective intelligence of the crowd, this study attempts to trace the trajectory from a credible seed video posted by the World Health Organization (WHO) to an anti-vaccine video, following only video links suggested by YouTube’s recommendation system. We gamify the process by asking participants to intentionally find an anti-vaccine video with as few clicks as possible. Having collected real-world trajectory data from respondents from (1) the WHO/United Nations (UN) system (N = 33) and (2) Amazon Mechanical Turk (N = 80), we next compare the recommendations seen by these users to recommended videos that are obtained from (3) the YouTube API’s RelatedToVideoID parameter (N = 40) and (4) from clean browsers without any identifying cookies (N = 40), which serve as reference points. This study allows us to see how users’ real-world experiences are different from the personalized recommendations obtained by querying YouTube’s API, which are often used to study YouTube’s recommendation system. Results show that watch histories of the users significantly affect video recommendations, suggesting that data from the API or from a clean browser does not offer an accurate picture of the recommendations that real users are seeing. However, we found no evidence that YouTube promotes anti-vaccine content to its users. Recommended videos suggested by YouTube are more likely to contain health-related content that is not specifically related to vaccination. These videos are usually longer and contain more popular content. Our findings suggest that the common perception that YouTube’s recommendation system acts as a “rabbit hole” may be inaccurate, and that YouTube may instead be pushing a blockbuster strategy that attempts to engage users by promoting other content that has been reliably successful across the platform.

Analysis of bone marrow aspirates (BMA) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on visual examination of the samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store and analyze BMA samples remotely, but is also supported by an artificial intelligence (AI) pipeline that accelerates the differential cell counting (DCC) process and reduces inter-observer variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.

As secretary general of the United Nations, Antonio Guterres said during the 2020 Nelson Mandela Annual Lecture, “COVID-19 has been likened to an X-ray, revealing fractures in the fragile skeleton of the societies we have built.” Without a doubt, the COVID-19 pandemic has exposed and exacerbated existing global inequalities. Whether at the local, national, or international scale, the gap between the privileged and the vulnerable is growing wider, resulting in a broad increase in inequality across all dimensions of society. The disease has strained health systems, social support programs, and the economy as a whole, drawing an ever-widening distinction between those with access to treatment, services, and job opportunities and those without. Global lockdown restrictions have led to increases in childcare and housework responsibilities, and most of the burden has fallen on women, further increasing existing gender inequality [1] , [2] . Indigenous populations worldwide find themselves more vulnerable to infection, many times with less access to health services or hygiene measures and limited updated scientific information about the virus and measures that can be taken to mitigate it [3] . Inequality has also pervaded the education sector, with only a subset of students able to attend safe in-person schooling or access online education when needed.

BACKGROUND Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed altering a correct epidemiological surveillance. OBJECTIVE To evaluate an artificial intelligence-based smartphone application, connected to a web telemedicine platform, to automatically and objectively read rapid diagnostic test (RDT) results and assess its impact on COVID-19 pandemic management. METHODS Overall, 252 human sera from individuals with PCR-positive SARS-CoV-2 infection were used to inoculate a total of 1,165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department. RESULTS Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8-96.1%) for reading IgG band of COVID-19 antibodies RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100% and specificity was 95.8%, CI 94.3-97.3%. All COVID-19 antigen RDTs were correctly read by the app. CONCLUSIONS The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDTs brands. The web platform serves as a real time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.

ObjectivesTo evaluate an artificial intelligence-based smartphone application to automatically and objectively read rapid diagnostic test (RDT) results and assess its impact on COVID-19 pandemic management.MethodsOverall, 252 human sera from individuals with PCR-positive SARS-CoV-2 infection were used to inoculate a total of 1165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 92 antigen RDTs at one hospital emergency department.ResultsField studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8-96.1%) for reading IgG band of COVID-19 antibodies RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100% and specificity was 95.8%, CI 94.3-97.3%. All COVID-19 antigen RDTs were correctly read by the app.ConclusionsThe proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDTs brands. The platform can serve as a real time epidemiological tracking tool and facilitate reporting of positive RDTs to relevant health authorities.

Visual inspection of microscopic samples is still the gold standard diagnostic methodology for many global health diseases. Soil-transmitted helminth infection affects 1.5 billion people worldwide, and is the most prevalent disease among the Neglected Tropical Diseases. It is diagnosed by manual examination of stool samples by microscopy, which is a time-consuming task and requires trained personnel and high specialization. Artificial intelligence could automate this task making the diagnosis more accessible. Still, it needs a large amount of annotated training data coming from experts.In this work, we proposed the use of crowdsourced annotated medical images to train AI models (neural networks) for the detection of soil-transmitted helminthiasis in microscopy images from stool samples leveraging non-expert knowledge collected through playing a video game. We collected annotations made by both school-age children and adults, and we showed that, although the quality of crowdsourced annotations made by school-age children are sightly inferior than the ones made by adults, AI models trained on these crowdsourced annotations perform similarly (AUC of 0.928 and 0.939 respectively), and reach similar performance to the AI model trained on expert annotations (AUC of 0.932). We also showed the impact of the training sample size and continuous training on the performance of the AI models.In conclusion, the workflow proposed in this work combined collective and artificial intelligence for detecting soil-transmitted helminthiasis. Embedded within a digital health platform can be applied to any other medical image analysis task and contribute to reduce the burden of disease.

Microscopy plays a crucial role in the diagnosis of numerous diseases. However, the need for trained microscopists and pathologists, the complexity of pathology, and the accessibility and affordability of the technology can hinder the provision of rapid and high-quality diagnoses and healthcare. In this work, we present an affordable, 3D-printed, portable, robotic, mobile-based slide scanning microscope. The proposed device is composed of electronic, mechanical, and optical modules operated via smartphone with a control app. The device is connected and fully integrated with a telemedicine web platform, where digitized microscopy images can be remotely visualized and analyzed. The robotic scanner, which has approximately 1-µm resolution, has been evaluated in two clinical scenarios with histology and stool samples. The results showed sufficient image quality for performing a proper diagnosis in all cases under study.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable., Author summary The spread of infectious diseases presents many challenges to healthcare systems and infrastructures across the world. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to the dangers of disease spread. This study seeks to understand how COVID-19 spreads in settlements, focusing on the Cox’s Bazar refugee settlement in Bangladesh. Our model simulates the movements and interactions of each individual in the settlement, incorporating information about family structures and demographic attributes, to understand how COVID-19 might spread under various intervention strategies. Our analysis suggests that mask wearing in indoor locations can have a significant effect on disease spread, even when wearing reusable cotton masks, which the people in the settlement can make themselves. We also look at different ways to treat individuals who only have milder symptoms and don’t yet require hospitalisation, as well as various scenarios which might allow for the safe reopening of schools in the settlement. With almost 80 million forcibly displaced people in the world, we hope that this work will inspire more modeling groups to focus on these vulnerable populations, which have been traditionally under-served by such efforts, to ensure no one is left behind.

In an unprecedented effort of scientific collaboration, researchers across fields are racing to support the response to COVID-19. Making a global impact with AI tools will require scalable approaches for data, model and code sharing; adapting applications to local contexts; and cooperation across borders.

Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne disease. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’sr=0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility network in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed network, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data capture a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. Non-pharmaceutical public health interventions can be used to mitigate transmission, and modeling efforts can provide crucial insights on the potential effectiveness of such interventions to help inform decision making processes. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements. The model, based on the JUNE open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. Furthermore, we present a visual analytics tool which allows decision makers to distill insights by comparing the results of different simulations and scenarios. Through simulating their effects on the epidemiological development of COVID-19, we evaluate several public health interventions ranging from increasing mask wearing compliance to the reopening of learning institutions. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings.

We invited authors of selected Comments and Perspectives published in Nature Machine Intelligence in the latter half of 2019 and first half of 2020 to describe how their topic has developed, what their thoughts are about the challenges of 2020, and what they look forward to in 2021.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.

In addition to moving people and goods, ships can spread disease. Vessel trajectory data from ship Automatic Identification Systems (AIS) is available online and can be extracted and analyzed, as we illustrate in the case of the current coronavirus epidemic. This data should be included in epidemiological models of disease transmission to complement air traffic data and inform operational responses.

This work is framed within the general objective of helping to reduce the cost of telepathology in developing countries and rural areas with no access to automated whole slide imaging (WSI) scanners. We present an automated software pipeline to the problem of mosaicing images acquired with a smartphone, attached to a portable, low-cost, robotic microscopic scanner fabricated using 3D printing technology. To achieve this goal, we propose a robust and automatic workflow, which solves all necessary steps to obtain a stitched image, covering the area of interest, from a set of initial 2D grid of overlapping images, including vignetting correction, lens distortion correction, registration and blending. Optimized solutions, like Voronoi cells and Laplacian blending strategies, are adapted to the low-cost optics and scanner device, and solve imperfections caused using smartphone camera optics. The presented solution can obtain histopathological virtual slides with diagnostic value using a low-cost portable device.

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics., 39 pages, v2: much larger to reflect the significant increase in the size of the body of literature, v3: uploaded with JAIR page numbers and references

Humanitarian response to natural disasters and conflicts can be assisted by satellite image analysis. In a humanitarian context, very specific satellite image analysis tasks must be done accurately and in a timely manner to provide operational support. We present PulseSatellite, a collaborative satellite image analysis tool which leverages neural network models that can be retrained on-the fly and adapted to specific humanitarian contexts and geographies. We present two case studies, in mapping shelters and floods respectively, that illustrate the capabilities of PulseSatellite., 2 pages, 2 figures

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior., Digital traces of our lives have the potential to allow insights into collective behaviors. Here, the authors cluster consumers by their credit card purchase sequences and discover five distinct groups, within which individuals also share similar mobility and demographic attributes.

Big data can be used to assess perceptions about public health issues. This study assessed social media data from Twitter to inform communication campaigns to promote HIV testing and reduce discrimination related to HIV/AIDS or towards key populations to the HIV epidemic, and its potential utility to evaluate such campaigns through HIV testing uptake. Tweets from Brazil were collected from January 2014 to March 2015 and filtered by four categories of keywords including discrimination, HIV prevention, HIV testing, and HIV campaigns. In total over 100,000 geo-located tweets were extracted and analyzed. A dynamic online dashboard updated daily allowed mapping trends, anomalies and influencers, and enabled its use for feedback to campaigns, including correcting misconceptions. These results encourage the use of social networking data for improved messaging in campaigns. Clinical HIV test data was collected monthly from the city of Curitiba and compared to the number of tweets mapped to the city showing a moderate positive correlation (r = 0.39). Results are limited due to the availability of the HIV testing data. The potential of social media as a proxy for HIV testing uptake needs further validation, which can only be done with higher frequency and higher spatial granularity of service delivery data, enabling comparisons with the social media data. Such timely information could empower early response immediate media messaging to support programmatic efforts, such as HIV prevention, testing, and treatment scale up. Electronic supplementary material The online version of this article (doi:10.1007/s10461-017-1753-2) contains supplementary material, which is available to authorized users.

In this chapter, we contribute a methodological framework for measuring integration through the lens of spatial and social segregation using CDR data. We illustrate the application of this framework using the datasets provided by Turk Telekom. Integration is one of the main durable solutions to refugee crises recognized by the UN High Commissioner for Refugees (UNHCR). It is a complex and gradual legal, economic, social and cultural process that burdens both the settling population, and the receiving society. Successful integration requires actions from a variety of stakeholders (including different levels of government, NGOs, welfare service providers, etc.), which can make evaluating the outcomes of targeted programmes and policies extremely difficult. While these generally differ from country to country, UNHCR recognizes a need for standardized indicators that can be used to compare integration across countries and regions, and to assess the success of various efforts. Here, we show how segregation, isolation and homophily can be measured by deriving population estimates from CDRs, and how the evolution of refugees’ communication patterns and mobility traces can provide initial insights into their social integration.

BACKGROUND: Current World Health Organization recommendations for the management of malaria include the need for a parasitological confirmation prior to triggering appropriate treatment. The use of rapid diagnostic tests (RDTs) for malaria has contributed to a better infection recognition and a more targeted treatment. Nevertheless, low-density infections and parasites that fail to produce HRP2 can cause false-negative RDT results. Microscopy has traditionally been the methodology most commonly used to quantify malaria and characterize the infecting species, but the wider use of this technique remains challenging, as it requires trained personnel and processing capacity. OBJECTIVE: In this study, the feasibility of an on-line system for remote malaria species identification and differentiation has been investigated by crowdsourcing the analysis of digitalized infected thin blood smears by non-expert observers using a mobile app. METHODS: An on-line videogame in which players learned how to differentiate the young trophozoite stage of the five Plasmodium species has been designed. Images were digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Images from infected red blood cells were cropped and puzzled into an on-line game. During the game, players had to decide the malaria species (Plasmodium falciparum, Plasmodium malariae, Plasmodium vivax, Plasmodium ovale, Plasmodium knowlesi) of the infected cells that were shown in the screen. After 2 months, each player's decisions were analysed individually and collectively. RESULTS: On-line volunteers playing the game made more than 500,000 assessments for species differentiation. Statistically, when the choice of several players was combined (n > 25), they were able to significantly discriminate Plasmodium species, reaching a level of accuracy of 99% for all species combinations, except for P. knowlesi (80%). Non-expert decisions on which Plasmodium species was shown in the screen were made in less than 3 s. CONCLUSION: These findings show that it is possible to train malaria-naïve non-experts to identify and differentiate malaria species in digitalized thin blood samples. Although the accuracy of a single player is not perfect, the combination of the responses of multiple casual gamers can achieve an accuracy that is within the range of the diagnostic accuracy made by a trained microscopist. M.L. held a postdoctoral Fellowship of the Spanish Ministry of Economy and Competitiveness (FPDI-2013-16409) and holds a grant from the Spanish Society of Hematology and Hemotherapy. This work was supported by the Universidad Politécnica de Madrid (COOP-XVII-02), Spain’s Science, Innovation & Universities Ministry (TEC2015-66978-R), Madrid Regional Government (TOPUS S2013/MIT-3024), the CDTI NEOTEC SNEO-20171197 grant from the Spanish Ministry of Economy, Industry and Competitiveness, the European Regional Development Funds, Amazon Web Services, Fundación Renta Corporación and Ashoka. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Sí

The Mediterranean Sea is the stage of one of the biggest humanitarian crises to affect Europe. Since 2014, thousands of migrants and refugees have died or gone missing in dangerous attempts to cross into the continent. However, there is relatively little structured information available on how they attempt the crossing. Such information could be used to better target maritime rescue efforts or to anticipate smuggling patterns, which could potentially save lives. In this article, we provide an overview of data sources available for the study of migration in the Central Mediterranean. We describe how these data can be structured, combined, and analyzed to provide quantitative insights on the situation in the region. We define a quantified rescue framework for fusing different data sources around individual rescue operations, and we explore the potential of machine learning to perform automated rescue detection based on vessel trajectory information. We conclude with technical research questions, and potential policy and operational implications related to the use of these data sources.

We propose a framework for the systematic analysis of mobile phone data to identify relevant mobility profiles in a population. The proposed framework allows finding distinct human mobility profiles based on the digital trace of mobile phone users characterized by a Matrix of Individual Trajectories (IT-Matrix). This matrix gathers a consistent and regularized description of individual trajectories that enables multi-scale representations along time and space, which can be used to extract aggregated indicators such as a dynamic multi-scale population count. Unsupervised clustering of individual trajectories generates mobility profiles (clusters of similar individual trajectories) which characterize relevant group behaviors preserving optimal aggregation levels for detailed and privacy-secured mobility characterization. The application of the proposed framework is illustrated by analyzing fully anonymized data on human mobility from mobile phones in Senegal at the arrondissement level over a calendar year. The analysis of monthly mobility patterns at the livelihood zone resolution resulted in the discovery and characterization of seasonal mobility profiles related with economic activities, agricultural calendars and rainfalls. The use of these mobility profiles could support the timely identification of mobility changes in vulnerable populations in response to external shocks (such as natural disasters, civil conflicts or sudden increases of food prices) to monitor food security.

Malaria eradication of the worldwide is currently one of the main WHO's global goals. In this work, we focus on the use of human-machine interaction strategies for low-cost fast reliable malaria diagnostic based on a crowdsourced approach. The addressed technical problem consists in detecting spots in images even under very harsh conditions when positive objects are very similar to some artifacts. The clicks or tags delivered by several annotators labeling an image are modeled as a robust finite mixture, and techniques based on the Expectation-Maximization (EM) algorithm are proposed for accurately counting malaria parasites on thick blood smears obtained by microscopic Giemsa-stained techniques. This approach outperforms other traditional methods as it is shown through experimentation with real data.

We propose to directly process 3D+t image sequences with mathematical morphology operators, using a new classification of the 3D+t structuring elements. Several methods (filtering, tracking, segmentation) dedicated to the analysis of 3D+t datasets of zebrafish embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebrafish early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it benefits from the intrinsic redundancy of the temporal dimension, and minimizes the needs for human intervention in semi-automatic algorithms.

Motivation: Multi-view microscopy techniques such as Light-Sheet Fluorescence Microscopy (LSFM) are powerful tools for 3D + time studies of live embryos in developmental biology. The sample is imaged from several points of view, acquiring a set of 3D views that are then combined or fused in order to overcome their individual limitations. Views fusion is still an open problem despite recent contributions in the field. Results: We developed a wavelet-based multi-view fusion method that, due to wavelet decomposition properties, is able to combine the complementary directional information from all available views into a single volume. Our method is demonstrated on LSFM acquisitions from live sea urchin and zebrafish embryos. The fusion results show improved overall contrast and details when compared with any of the acquired volumes. The proposed method does not need knowledge of the system's point spread function (PSF) and performs better than other existing PSF independent fusion methods. Availability and Implementation: The described method was implemented in Matlab (The Mathworks, Inc., USA) and a graphic user interface was developed in Java. The software, together with two sample datasets, is available at http://www.die.upm.es/im/software/SPIMFusionGUI.zip A public release, free of charge for non-commercial use, is planned after the publication of this article. Contact: jlrubio@die.upm.es; nadine.peyrieras@inaf.cnrs-gif.fr; mledesma@die.upm.es Supplementary Information: Supplementary data are available at Bioinformatics online.

The coordination of humanitarian relief, e.g. in a natural disaster or a conflict situation, is often complicated by a scarcity of data to inform planning. Remote sensing imagery, from satellites or drones, can give important insights into conditions on the ground, including in areas which are difficult to access. Applications include situation awareness after natural disasters, structural damage assessment in conflict, monitoring human rights violations or population estimation in settlements. We review machine learning approaches for automating these problems, and discuss their potential and limitations. We also provide a case study of experiments using deep learning methods to count the numbers of structures in multiple refugee settlements in Africa and the Middle East. We find that while high levels of accuracy are possible, there is considerable variation in the characteristics of imagery collected from different sensors and regions. In this, as in the other applications discussed in the paper, critical inferences must be made from a relatively small amount of pixel data. We, therefore, consider that using machine learning systems as an augmentation of human analysts is a reasonable strategy to transition from current fully manual operational pipelines to ones which are both more efficient and have the necessary levels of quality control. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.