Geo-information system of spread of tuberculosis based on inversion and prediction

Attempting to understand the spread of SARS-CoV-2: Associations between the number of cases in regions and selected economic and geographic parameters

We present in this work some results from analysing the spread of Covid-19 in different countries and regions around the world and the potential relations with climate, geographical location, and GDP. While the situation remains dynamic, we believe this analysis has the potential to uncover certain underlying trends. We primarily intend the results to drive further, more detailed analysis of the relevant data by other researchers that would help us gather a better understanding of the situation, aiding our preparedness. Our observations show that countries in high latitudes, with temperate and…

The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts futu…

SIRNet: Understanding Social Distancing Measures with Hybrid Neural Network Model for COVID-19 Infectious Spread

The SARS-CoV-2 infectious outbreak has rapidly spread across the globe and precipitated varying policies to effectuate physical distancing to ameliorate its impact. In this study, we propose a new hybrid machine learning model, SIRNet, for forecasting the spread of the COVID-19 pandemic that couples with the epidemiological models. We use categorized spatiotemporally explicit cellphone mobility data as surrogate markers for physical distancing, along with population weighted density and other local data points. We demonstrate at varying geographical granularity that the spectrum of physical di…

Topological data analysis model for the spread of the coronavirus

We apply topological data analysis, specifically the Mapper algorithm, to the U.S. COVID-19 data. The resulting Mapper graphs provide visualizations of the pandemic that are more complete than those supplied by other, more standard methods. They encode a variety of geometric features of the data cloud created from geographic information, time progression, and the number of COVID-19 cases. They reflect the development of the pandemic across all of the U.S. and capture the growth rates as well as the regional prominence of hot-spots. The Mapper graphs allow for easy comparisons across time and s…

Scalable Computational Algorithms for Geo-spatial Covid-19 Spread in High Performance Computing

A nonlinear partial differential equation (PDE) based compartmental model of COVID-19 provides a continuous trace of infection over space and time. Finer resolutions in the spatial discretization, the inclusion of additional model compartments and model stratifications based on clinically relevant categories contribute to an increase in the number of unknowns to the order of millions. We adopt a parallel scalable solver allowing faster solutions for these high fidelity models. The solver combines domain decomposition and algebraic multigrid preconditioners at multiple levels to achieve the des…

Limiting the spread of disease through altered migration patterns

We consider a model for an epidemic in a population that occupies geographically distinct locations. The disease is spread within subpopulations by contacts between infective and susceptible individuals, and is spread between subpopulations by the migration of infected individuals. We show how susceptible individuals can act collectively to limit the spread of disease during the initial phase of an epidemic, by specifying the distribution that minimises the growth rate of the epidemic when the infectives are migrating so as to maximise the growth rate. We also give an explicit strategy that mi…

Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic

The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic. Social (physical) distancing is a key non-pharmacologic control measure to reduce the transmission rate of SARS-COV-2, but high-level adherence is needed. Using daily travel distance and stay-at-home time derived from large-scale anonymous mobile phone location data provided by Descartes Labs and SafeGraph, we quantify the degree to which social distancing mandates have been followed in the U.S. and its effect on growth of COVID-19 cases. The correlation between the COVID-19 growth rate and tra…

On the heterogeneous spread of COVID-19 in Chile

Non-pharmaceutical interventions (NPIs) have played a crucial role in controlling the spread of COVID-19. Nevertheless, NPI efficacy varies enormously between and within countries, mainly because of population and behavioural heterogeneity. In this work, we adapted a multi-group SEIRA model to study the spreading dynamics of COVID-19 in Chile, representing geographically separated regions of the country by different groups. We use national mobilization statistics to estimate the connectivity between regions and data from governmental repositories to obtain COVID-19 spreading and death rates in…

Simulating the spread of COVID-19 via spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion

We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitati…

含重叠社区的随机图上的流行病传播、参数敏感性与疫苗接种策略

Epidemic spread, parameter sensitivity and vaccination strategies on a random graph with overlapping communities

该研究构建了含代表家庭、工作场所等的重叠完全图的几何随机图模型，分析其参数敏感性以辅助社交距离策略制定，还定量比较不同疫苗接种策略，评估保护效果及感染者网络位置信息的价值。