Geospatial Artificial Intelligence (GeoAI): Applications in Health Care

GeoAI is a new emerging research area that refers to set of technologies that integrate AI technology with a diversity of GIS (Geographic Information System) techniques. The present study observed that GeoAI goes beyond current GIS expectations and into the domain of possibility in the not-too-distant future. This emerging interdisciplinary science will lead us to sustainable decisions and explore the most suitable solutions to the existing problems. GeoAI has the potential to transform current geography and geomatics programs by incorporating a GeoAI dimension into modern GIS curricula. In this review, we have studied the application GeoAI in various healthcare fields. GeoAI has the potential to revolutionize healthcare, public health, infectious disease control, disaster aid, and the achievements of Sustainable Development Goals (SDG). in healthcare, GeoAI can help with disease diagnosis, treatment planning, and resource allocation. In public health, it can aid in disease surveillance, emergency response planning, and identifying health disparities. In infectious disease control, GeoAI can help predict and track disease outbreaks and support vaccination campaigns. In disaster aid, GeoAI can provide real time data on environmental hazards and their impact on public health. In achieving Sustainable Development Goals, it can support in land use planning, urban development, and resource allocation to promote health and environmental sustainability. Overall GeoAI has the potential to transform multiple sectors and improve the well-being of populations worldwide

Counting equivalence classes of Boolean functions

У овој дисертацији разматран јe проблем израчунавања броја класа еквиваленције Булових функција. Тежина одређивања броја класа еквивален- ције нагло расте са бројем променљивих n. Мотивација за избор ове теме лежи у чињеници да су конкретни бројеви до сада били познати само за релативно мале вредности n, иако је сам проблем теоријски одавно решен...In this dissertation, the problem of calculating the number of equiva- lence classes of Boolean functions is discussed. The difficulty of determining the number of equivalence classes increases sharply with the number of variables n. The motivation for choosing this topic lies in the fact that concrete numbers have been known so far only for relatively small values of n, although the problem itself was theoretically solved a long time ago..

Identifying highly influential travellers for spreading disease on a public transport system

The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the physical encounters of individuals on public transit systems is thus critical to understand the drivers of infectious disease outbreaks. For instance previous work has explored the impact of recurring patterns inherent in human mobility on disease spread, but has not considered other dimensions such as the distance travelled or the number of encounters. Here, we consider multiple mobility dimensions simultaneously to uncover critical information for the design of effective intervention strategies. We use one month of citywide smart card travel data collected in Sydney, Australia to classify bus passengers along three dimensions, namely the degree of exploration, the distance travelled and the number of encounters. Additionally, we simulate disease spread on the transport network and trace the infection paths. We investigate in detail the transmissions between the classified groups while varying the infection probability and the suspension time of pathogens. Our results show that characterizing individuals along multiple dimensions simultaneously uncovers a complex infection interplay between the different groups of passengers, that would remain hidden when considering only a single dimension. We also identify groups that are more influential than others given specific disease characteristics, which can guide containment and vaccination efforts.Comment: 10 pages, 10 figures and 1 table. To be published in the 2020 21st IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (IEEE WOWMOM 2020) conference program and the proceeding

Call detail record aggregation methodology impacts infectious disease models informed by human mobility

This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions

Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale

Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the “First 90”

Background: The UNAIDS 90-90-90 Fast-Track targets provide a framework for assessing coverage of HIV testing services (HTS) and awareness of HIV status – the “first 90.” In Kenya, the bulk of HIV testing targets are aligned to the five highest HIV-burden counties. However, we do not know if most of the new HIV diagnoses are in these five highest-burden counties or elsewhere. Methods: We analyzed facility-level HTS data in Kenya from 1 October 2015 to 30 September 2016 to assess the spatial distribution of newly diagnosed HIV-positives. We used the Moran's Index (Moran's I) to assess global and local spatial auto-correlation of newly diagnosed HIV-positive tests and Kulldorff spatial scan statistics to detect hotspots of newly diagnosed HIV-positive tests. For aggregated data, we used Kruskal-Wallis equality-of-populations non-parametric rank test to compare absolute numbers across classes. Results: Out of 4,021 HTS sites, 3,969 (98.7%) had geocodes available. Most facilities (3,034, 76.4%), were not spatially autocorrelated for the number of newly diagnosed HIV-positives. For the rest, clustering occurred as follows; 438 (11.0%) were HH, 66 (1.7%) HL, 275 (6.9%) LH, and 156 (3.9%) LL. Of the HH sites, 301 (68.7%) were in high HIV-burden counties. Over half of 123 clusters with a significantly high number of newly diagnosed HIV-infected persons, 73(59.3%) were not in the five highest HIV-burden counties. Clusters with a high number of newly diagnosed persons had twice the number of positives per 1,000,000 tests than clusters with lower numbers (29,856 vs. 14,172). Conclusions: Although high HIV-burden counties contain clusters of sites with a high number of newly diagnosed HIV-infected persons, we detected many such clusters in low-burden counties as well. To expand HTS where most needed and reach the “first 90” targets, geospatial analyses and mapping make it easier to identify and describe localized epidemic patterns in a spatially dispersed epidemic like Kenya's, and consequently, reorient and prioritize HTS strategies.publishedVersio

Reproducibility and scientific integrity of big data research in urban public health and digital epidemiology: a call to action

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level

Policy, power, stigma and silence: exploring the complexities of a primary mental health care model in a rural South African setting

The Movement for Global Mental Health’s (MGMH) efforts to scale up the availability of mental health services have been moderately successful. Investigations in resource-poor countries like South Africa have pointed to the value of an integrated primary mental health care model and multidisciplinary collaboration to support mental health needs in underserved and underresourced communities. However, there remains a need to explore how these policies play out within the daily realities of communities marked by varied environmental and relational complexities. Arguably, the lived realities of mental health policy and service delivery processes are best viewed through ethnographic approaches, which remain underutilised in the field of global mental health. This paper reports on findings from a case study of mental health services for HIV-affected women in a rural South African setting, which employed a motivated ethnography in order to explore the realities of the primary mental health care model and related policies in South Africa. Findings highlighted the influence of three key symbolic (intangible) factors that impact on the efficacy of the primary mental health care model: power dynamics, which shaped relationships within multidisciplinary teams; stigma, which limited the efficacy of task-shifting strategies; and the silencing of women’s narratives of distress within services. The resultant gap between policy ideals and the reality of practice is discussed. The paper concludes with recommendations for building on existing successes in the delivery of primary mental health care in South Africa