2022 Proceeding
The 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi'2022) focuses on all aspects of data science and simulation to better understand the spatial processes and patterns of infectious diseases, to predict disease outcomes, and to develop tools that support and guide policy interventions.
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Understanding the spatiotemporal heterogeneities in the associations between COVID-19 infections and both human mobility and close contacts in the United States
It has been well-established that human mobility has an inseparable relationship with COVID-19 infections. As social-distancing and stay-at-home orders lifted and data availability increased, our knowledge on how human behaviors including mobility and ...
Microscopic modeling of spatiotemporal epidemic dynamics
Conventional techniques of epidemic modeling are based on compartmental models, where population groups are transitioning from one compartment to another - for example, S, I, or R, (Susceptible, Infectious, or Recovered). Then, they focus on learning ...
MultiMaps: a tool for decision-making support in the analyzes of multiple epidemics
- Gesiel Rios Lopes,
- Alexandre C. B. Delbem,
- Roberto Fray da Silva,
- Cláudio Bielenki Júnior,
- Sérgio Henrique Vannucchi Leme de Mattos,
- Denise Scatolini,
- Filippo Ghiglieno,
- Antonio Mauro Saraiva
The decision-making process for complex problems based on heterogeneous and multiple data sources requires structuring information with adequate representation for the phenomenon under analysis. This is specifically true for temporal and spatial ...
Spatiotemporal disease case prediction using contrastive predictive coding
Time series prediction models have played a vital role in guiding effective policymaking and response during the COVID-19 pandemic by predicting future cases and deaths at the country, state, and county levels. However, for emerging diseases, there is ...
Using mobile network data to color epidemic risk maps
In this paper we propose a method for using mobile network data to detect potential COVID-19 hospitalizations and derive corresponding epidemic risk maps. We apply our methods to a dataset from more than 2 million cellphones, collected over the months ...
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