Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints

Venkatramanan, Srinivasan; Chen, Jiangzhuo; Fadikar, Arindam; Gupta, Sandeep; Higdon, Dave; Lewis, Bryan; Marathe, Madhav V.; Mortveit, Henning S.; Vullikanti, Anil

doi:10.1371/journal.pcbi.1007111

Cited by 52 publications

(56 citation statements)

References 27 publications

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“…Model structure. Here, we summarize GLEaM 15 (Northeastern model) and PatchSim 18 (UVA model). GLEaM uses two classes of datasets—population estimates and mobility.…”

Section: Spatial Metapopulation Models: Northeastern and Uva Models (mentioning

confidence: 99%

“…The PatchSim 18 model has a similar structure, except that it uses administrative boundaries (e.g., counties), instead of a Voronoi tesselation, which are connected using a mobility network. The mobility network is derived by combining commuter and airline networks, to model time spent per day by individuals of region (patch) i in region (patch) j .…”

Section: Spatial Metapopulation Models: Northeastern and Uva Models (mentioning

confidence: 99%

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Mathematical Models for COVID-19 Pandemic: A Comparative Analysis

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COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article, we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.

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“…Model structure. Here, we summarize GLEaM 15 (Northeastern model) and PatchSim 18 (UVA model). GLEaM uses two classes of datasets—population estimates and mobility.…”

Section: Spatial Metapopulation Models: Northeastern and Uva Models (mentioning

confidence: 99%

Section: Spatial Metapopulation Models: Northeastern and Uva Models (mentioning

confidence: 99%

Mathematical Models for COVID-19 Pandemic: A Comparative Analysis

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“…where Π C (c, t) = number of new infections in c at t population of county c and the numerator is determined using the number of infections obtained through PatchSim simulation engine [20]. The es u (t) is used as the seeding of cases in our compartmental SEIR model simulation (described in the above subsection).…”

Section: University Reopening Simulationsmentioning

confidence: 99%

Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

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Sadilek

et al. 2020

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This work quantifies the impact of interventions to curtail mobility and social interactions in order to control the COVID-19 pandemic. We analyze the change in world-wide mobility at multiple spatio-temporal resolutions -- county, state, country -- using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km2. We show that human mobility underwent an abrupt and significant change, partly in response to the interventions, resulting in 87% reduction of international travel and up to 75% reduction of domestic travel. Taking two very different countries sampled from the global spectrum, we observe a maximum reduction of 42% in mobility across different states of the United States (US), and a 68% reduction across the states of India between late March and late April. Since then, there has been an uptick in flows, with the US seeing 53% increase and India up to 38% increase with respect to flows seen during the lockdown. As we overlay this global map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell -- often before stay-at-home orders were issued. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility. We find that population mixing has decreased considerably as the pandemic has progressed and are able to measure this effect across the world. Finally, we carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

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“…Modeling approaches: Different kinds of structured metapopulation models 8,[42][43][44][45] and agentbased models [46][47][48] have been used in the past to model the sub-national spread; we refer to Refs. 13,49,50 for surveys on different modeling approaches.…”

Section: Data Needs: Some Of the Common Datasets Needed By Most Modelmentioning

confidence: 99%

Data-Driven Modeling for Different Stages of Pandemic Response

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Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who are at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision-making. As different countries and regions go through phases of the pandemic, the questions and data availability also change. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.

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Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints

Cited by 52 publications

References 27 publications

Mathematical Models for COVID-19 Pandemic: A Comparative Analysis

Mathematical Models for COVID-19 Pandemic: A Comparative Analysis

Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

Data-Driven Modeling for Different Stages of Pandemic Response

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