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Policy-makers require data-driven tools to assess the spread of COVID-19 and inform the public of their risk of infection on an ongoing basis. We propose a rigorous hybrid model-and-data-driven approach to risk scoring based on a time-varying SIR epidemic model that ultimately yields a simplified color-coded risk level for each community. The risk score Γ that we propose is proportional to the probability of someone currently healthy getting infected in the next 24 hours based on their locality. We show how this risk score can be estimated using another useful metric of infection spread, , the time-varying average reproduction number which indicates the average number of individuals an infected person would infect in turn. The proposed approach also allows for quantification of uncertainty in the estimates of and Γ in the form of confidence intervals. Code and data from our effort have been open-sourced and are being applied to assess and communicate the risk of infection in the City and County of Los Angeles. CCS CONCEPTS • Computing methodologies → Modeling methodologies.

Sustainable and practical personal mobility solutions for campus environments have traditionally revolved around the use of bicycles, or provision of pedestrian facilities. However many campus environments also experience traffic congestion, parking difficulties and pollution from fossil-fuelled vehicles. It appears that pedal power alone has not been sufficient to supplant the use of petrol and diesel vehicles to date, and therefore it is opportune to investigate both the reasons behind the continual use of environmentally unfriendly transport, and consider potential solutions. This paper presents the results from a year-long study into electric bicycle effectiveness for a large tropical campus, identifying barriers to bicycle use that can be overcome through the availability of public use electric bicycles

The displacement of people due to climatic changes (environmental migration) presents major societal and governance challenges. This article examines whether and how climate-induced rural-to-urban migration contributes to social-movement participation. We argue that the mainly forceful nature of relocation makes environmental migrants more likely to join and participate in social movements that promote migrant rights in urban areas. Using original survey data from Kenya, we find that individuals who had experienced several different types of severe climatic events at their previous location are more likely to join and participate in social movements. This finding has important policy implications. National and local authorities should not only provide immediate assistance and basic social services to environmental migrants in urban settings, but also facilitate permanent solutions by fostering their socio-economic and political integration in order to prevent urban conflict.