A comparison of spatial-based targeted disease mitigation strategies using mobile phone data

Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment and mitigation processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a hypothetical infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals’ spatial behaviour and its relationship with the risk of infectious diseases’ contagion. In particular, we show that CDRs-based indicators of individuals’ spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing mitigation strategies to support decision-making during country-level epidemics

A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data

Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a realistic infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals' spatial behaviour and its relationship with the risk of infectious diseases' contagion. In particular, we show that CDRs-based indicators of individuals' spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing containment strategies to support decision-making during country-level pandemics

Robust modeling of human contact networks across different scales and proximity-sensing techniques

The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks

The impact of COVID-19 on daily lives of transnational people based on smartphone data : Estonians in Finland

Global crises such as the COVID-19 pandemic affect both the functioning of our societies and the daily lives of people. Yet the impact of the crisis and its mitigation measures have exerted disproportionate influence on different population groups. In March – May 2020, COVID-19 mitigation measures such as closures of national borders affected transnational people who cross borders frequently for work, shopping, services, family reasons and socialising. We have examined the influence of the COVID-19 pandemic on the daily lives of transnational Estonians residing in Finland, based on a unique longitudinal smartphone tracking survey. Findings show that besides a drastic but expected decrease in trans-nationals’ spatial mobility, the pandemic has especially affected their cross-border mobility patterns to and time spent in Estonia. Interestingly, during the lockdown, some transnationals decided to stay not in their primary home in Finland, but in Estonia. Mobile phone communication activity followed moderately the downward trend of spatial mobility, but the crisis changed the division of communication partners by country: Finnish contacts diminished, whereas Estonian partners remained active. We reflect on our findings for future research and discuss the applicability of the smart-phone tracking approach for capturing the socio-spatial interactions of transnational people.Peer reviewe

A high-resolution flux-matrix model describes the spread of diseases in a spatial network and the effect of mitigation strategies

Propagation of an epidemic across a spatial network of communities is described by a variant of the SIR model accompanied by an intercommunity infectivity matrix. This matrix is estimated from fluxes between communities, obtained from cell-phone tracking data recorded in the USA between March 2020 and February 2021. We apply this model to the SARS-CoV-2 pandemic by fitting just one global parameter representing the frequency of interaction between individuals. We find that the predicted infections agree reasonably well with the reported cases. We clearly see the effect of “shelter-in-place” policies introduced at the onset of the pandemic. Interestingly, a model with uniform transmission rates produces similar results, suggesting that the epidemic transmission was deeply influenced by air travel. We then study the effect of alternative mitigation policies, in particular restricting long-range travel. We find that this policy is successful in decreasing the epidemic size and slowing down the spread, but less effective than the shelter-in-place policy. This policy can result in a pulled wave of infections. We express its velocity and characterize the shape of the traveling front as a function of the epidemiological parameters. Finally, we discuss a policy of selectively constraining travel based on an edge-betweenness criterion.journal articl

Agriculture and climate change: An agenda for negotiation in Copenhagen

Table of Contents: •Overview by Gerald C. Nelson •Agricultural Science and Technology Needs for Climate Change Adaptation and Mitigation by Rudy Rabbinge •Reducing Methane Emissions from Irrigated Rice by Reiner Wassmann, Yasukazu Hosen, and Kay Sumfleth •Direct and Indirect Mitigation Through Tree and Soil Management by Brent M. Swallow and Meine van Noordwijk •The Potential for Soil Carbon Sequestration by Rattan Lal •Mitigating Greenhouse Gas Emissions from Livestock Systems by M. Herrero and P. K. Thornton •The Role of Nutrient Management in Mitigation by Helen C. Flynn •Monitoring, Reporting, and Verification Methodologies for Agriculture, Forestry, and Other Land Use by Sean Smukler and Cheryl Palm •Synergies Among Mitigation, Adaptation, and Sustainable Development by Pete Smith •The Importance of Property Rights in Climate Change Mitigation by Helen Markelova and Ruth Meinzen-Dick •The Important Role of Extension Systems by Kristin E. Davis •Adaptation to Climate Change: Household Impacts and Institutional Responses by Futoshi Yamauchi and Agnes Quisumbing •The Constructive Role of International Trade by Franz FischlerClimate change, Copenhagen, Science and technology, rice, Soil fertility management, Greenhouse gas, Nutrients, Forestry resources, Land use, Sustainable development, International trade, extension activities, Household behavior, Institutional Impacts,

Data needs for integrated economic-epidemiological models of pandemic mitigation policies

The COVID-19 pandemic and the mitigation policies implemented in response to it have resulted in economic losses worldwide. Attempts to understand the relationship between economics and epidemiology has lead to a new generation of integrated mathematical models. The data needs for these models transcend those of the individual fields, especially where human interaction patterns are closely linked with economic activity. In this article, we reflect upon modelling efforts to date, discussing the data needs that they have identified, both for understanding the consequences of the pandemic and policy responses to it through analysis of historic data and for the further development of this new and exciting interdisciplinary field