Vaccination strategies against COVID-19 and the diffusion of anti-vaccination views

Miss-information is usually adjusted to fit distinct narratives and can propagate rapidly through communities of interest, which work as echo chambers, cause reinforcement and foster confirmation bias. False beliefs, once adopted, are rarely corrected. Amidst the COVID-19 crisis, pandemic-deniers and people who oppose wearing face masks or quarantines have already been a substantial aspect of the development of the pandemic. With a potential vaccine for COVID-19, different anti-vaccine narratives will be created and, likely, adopted by large population groups, with critical consequences. Here, we analyse epidemic spreading and optimal vaccination strategies, measured with the average years of life lost, in two network topologies (scale-free and small-world) assuming full adherence to vaccine administration. We consider the spread of anti-vaccine views in the network, using a similar diffusion model as the one used in epidemics, which are adopted based on a persuasiveness parameter of anti-vaccine views. Results show that even if an anti-vaccine narrative has a small persuasiveness, a large part of the population will be rapidly exposed to them. Assuming that all individuals are equally likely to adopt anti-vaccine views after being exposed, more central nodes in the network are more exposed and therefore are more likely to adopt them. Comparing years of life lost, anti-vaccine views could have a significant cost not only on those who share them, since the core social benefits of a limited vaccination strategy (reduction of susceptible hosts, network disruptions and slowing the spread of the disease) are substantially shortened.Comment: 13 pages, 3 figure

Large cities are less efficient for sustainable transport: The ABC of mobility

The distance travelled by car in a city has many negative impacts on its population, including pollution, noise and the use of space. Yet, quantifying the motorisation of urban mobility is a serious challenge, particularly across cities of different regions. Here we model the number of kilometres travelled by different modes of transport in a city by aggregating active mobility (A), public transport (B) and cars (C), thus expressing the modal share of a city by its ABC triplet. Data for over 800 cities across over 60 countries is used to model kilometres travelled by car and its relationship with city size. Our findings suggest that although public transport is more prominent in large cities, it is insufficient to reduce the distance travelled by car users within the city and, ultimately, their emissions. For cities outside the US, results show that although the proportion of journeys by car decreases in larger cities, distances become more prolonged, thus experiencing more distance travelled by car. When a city doubles its size, it has 87\% more car journeys, but they are 41% longer, thus experiencing 2.6 times more vehicle kilometres travelled. Further, by matching cities of similar size inside and outside the US, we estimate that cities in the US have 2.3 times more vehicle kilometres travelled than cities elsewhere.Comment: 14 pages, 6 figure

Mathematical modelling of social systems

Mobility and migration patterns, the concentration of crime and opinion dynamics observed on the fear of crime are all examples of social systems in which complex patterns emerge that subsequently feed back into the overall system. This thesis describes new methods established to analyse such patterns which appear in social systems. The main application area is in the field of crime science, but the methods developed here have wider applications within other social systems, some of which are also explored in the thesis, such as migration or road accidents. Based on new assessments of data, by utilising novel techniques of analysis and visualisation, models are also developed to determine how the perception of security is affected by particular crimes. The new metrics and models developed here consider different types of situation. Firstly, for events which have low frequency and yet a high degree of concentration; secondly, the distribution of such events which allows them to be simulated under different conditions; and then finally, understanding the impact of different degrees of concentration. An individual's fear of crime is the result of a mixture of factors which go beyond merely the actual crime experienced by that person, such as fear shared by others, memory of past events and of previous perceptions, crime reported in the media and more. This thesis quantifies fear of crime in a way that may prove useful to identify factors which increase fear of crime besides crime itself, explain why fear of crime emerges in a population and suggests policies for controlling fear

Opinion Dynamics and the Inevitability of a Polarised and Homophilic Society

A polarised society is frequently observed among ideological extremes, despite individual and collective efforts to reach a consensual opinion. Human factors, such as the tendency to interact with similar people and the reinforcement of such homophilic interactions or the selective exposure and assimilation to distinct views are some of the mechanisms why opinions might evolve into a more divergent distribution. A complex model in which individuals are exposed to alternating waves of propaganda which fully support different extreme views is considered here within an opinion dynamics model. People exposed to different extreme narratives adopt and share them with their peers based on the persuasiveness of the propaganda and are mixed with their previous opinions based on the volatility of opinions to form a new individual view. Social networks help capture elements such as homophily, whilst persuasiveness and memory capture bias assimilation and the exposure to ideas inside and outside echo chambers. The social levels of homophily and polarisation after iterations of people being exposed to extreme narratives define distinct trajectories of society becoming more or less homophilic and reaching extremism or consensus. There is extreme sensitivity to the parameters so that a small perturbation to the persuasiveness or the memory of a network in which consensus is reached could lead to the polarisation of opinions, but there is also unpredictability of the system since even under the same starting point, a society could follow substantially different trajectories and end with a consensual opinion or with extreme polarising views

How international research teams respond to disruption in their mobility patterns

Combining social network analysis with personal interviews, the paper examines how the social structure and internal composition of three Africa-focused international research networks contributes to their resilience. It shows that research networks are structured around a small number of highly influential coordinators. This structure facilitates information exchange and trust between countries and across fields. The study also suggests that the surveyed teams tend to exchange information or trust each other irrespective of their social and professional attributes, indicating that diversity is key to understanding their responses to major shocks such as the COVID-19 pandemic. In a second part, the paper analyzes how the spatial constraints imposed by distance and borders affect their ability to function internationally. It shows that the probability of exchanging information, trusting each other, and co-publishing decreases considerably with distance and that research communities are more likely formed inside the same country than internationally. Interviews reveal that teams responded to travel bans and border closure by emphasizing what they already did best, suggesting that resilience should be considered as an evolutionary attribute of a system

A Ubiquitous Collective Tragedy in Transport

A tragedy of the commons is said to occur when individuals act only in their own interest but, in so doing, create a collective state of a group that is less than optimal due to uncoordinated action. Here, we explore the individual decision-making processes of commuters using various forms of transport within a city, forming a modal share which is then built into a dynamical model using travel time as the key variable. From a randomised start in the distribution of the modal share, assuming that some individuals change their commuting method, favouring lower travel times, we show that a stable modal share is reached corresponding to an equilibrium in the model. Considering the average travel time for all commuters within the city, we show that an optimal result is achieved only if the direct and induced factors and the number of users are equal for all transport modes. For asymmetric factors, the equilibrium reached is always sub-optimal, leading to city travel trajectories being “tragic”, meaning that individuals choose a faster commuting time but create a slower urban mobility as a collective result. Hence, the city evolves, producing longer average commuting times. It is also shown that if a new mode of transport has a small baseline commuting time but has a high induced impact for other users, then introducing it might result in a counter-intuitive result producing more congestion, rather than less

Detecting the sensitive spots of the African interurban transport network

Transport systems are vulnerable to events that create disruptions. This situation is particularly sensitive in parts of Africa with a low density of highways and an increasing level of violence. Here, we measure the risk of the African transport network based on two separate indices: the intensity of future events $\mu$ and the impact $\nu$ of one event on the flow that travels through the network. To estimate the intensity of future events happening in city $i$, we construct a self-exciting point process. To estimate the impact of an event, we consider a network of all highways in the continent and a modelled flow between any pair of cities. Based on both indicators, we construct the $\mu-\nu$ diagram and classify cities based on their combined impact and intensity. Results show that certain cities in the network have a high risk and increase the vulnerability of Africa's infrastructure. These cities have a high propensity for suffering subsequent violence against their civilians, and given their connectivity structure, they also substantially affect the overall regional functioning. Removing just ten edges would require rerouting 32% of trips according to our model. The top 100 edges where violence might happen account for 17% of the trips. We find that cities with the highest $\mu-\nu$ risk are those characterised by small and medium size and large degree, meaning they act as hubs. Vulnerable areas tend to be characterised by the presence of terrorist groups like Boko Haram in Nigeria

Análisis de problemáticas urbanas a escala continental basado en datos abiertos: espacios verdes, forma urbana y sostenibilidad futura de las ciudades en África

Las próximas décadas serán de rápida urbanización y estrés climático en las ciudades africanas. Los espacios verdes pueden aumentar la resiliencia de las ciudades frente a las olas de calor, las inundaciones, los deslizamientos de tierra e incluso la erosión costera, además de mejorar la sostenibilidad al reparar la calidad del aire, proteger la biodiversidad y absorber carbono. Sin embargo, datos cuantitativos sobre la forma urbana, la disponibilidad de espacios verdes y la contaminación del aire son muy escasos y de difícil acceso para ciudades en África. En este trabajo usamos datos geoespaciales abiertos para analizar cuantitativamente las relaciones entre la forma urbana, la presencia de espacios verdes y la calidad del aire. Los resultados del análisis indican que la presencia de espacios verdes se relaciona con mejor calidad del aire, pero que deben estar acompañados de otras políticas para que su presencia sea realmente efectiva

Reconciling Big Data and Thick Data to Advance the New Urban Science and Smart City Governance

Amid growing enthusiasm for a ”new urban science” and ”smart city” approaches to urban management, ”big data” is expected to create radical new opportunities for urban research and practice. Meanwhile, anthropologists, sociologists, and human geographers, among others, generate highly contextualized and nuanced data, sometimes referred to as ‘thick data,’ that can potentially complement, refine and calibrate big data analytics while generating new interpretations of the city through diverse forms of reasoning. While researchers in a range of fields have begun to consider such questions, scholars of urban affairs have not yet engaged in these discussions. The article explores how ethnographic research could be reconciled with big data-driven inquiry into urban phenomena. We orient our critical reflections around an illustrative example: road safety in Mexico City. We argue that big and thick data can be reconciled in and through three stages of the research process: research formulation, data collection and analysis, and research output and knowledge representation

The diaspora model for human migration

Migration's impact spans various social dimensions, including demography, sustainability, politics, economy and gender disparities. Yet, the decision-making process behind migrants choosing their destination remains elusive. Existing models primarily rely on population size and travel distance to explain flow fluctuations, overlooking significant population heterogeneities. Paradoxically, migrants often travel long distances and to smaller destinations if their diaspora is present in those locations. To address this gap, we propose the diaspora model of migration, incorporating intensity (the number of people moving to a country) and assortativity (the destination within the country). Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence. Despite its simplicity, our model accurately reproduces the observed stable flow and distribution of migration in Austria (postal code level) and US metropolitan areas, yielding precise estimates of migrant inflow at various geographic scales. Given the increase in international migrations due to recent natural and societal crises, this study enlightens our understanding of migration flow heterogeneities, helping design more inclusive, integrated cities.Comment: 19 pages, 9 figure