Quantifying Mobility and Mixing Propensity in the Spatiotemporal Context of a Pandemic Spread

Abstract

COVID-19 is the most acute global public health crisis of this century. Current trends in the global infected and death numbers suggest that human mobility leading to high social mixing are key players in infection spread, making it imperative to incorporate the spatiotemporal and mobility contexts to future prediction models. In this work, we present a generalized spatiotemporal model that quantifies the role of human social mixing propensity and mobility in pandemic spread through a composite latent factor. The proposed model calculates the exposed population count by utilizing a nonlinear least-squares optimization that exploits the intrinsic linearity in SEIR (Susceptible, Exposed, Infectious, or Recovered). We also present inverse coefficient of variation of the daily exposed curve as a measure for infection duration and spread. We carry out experiments on the mobility and COVID-19 infected and death curves of New York City to show that boroughs with high inter-zone mobility indeed exhibit synchronicity in peaks of the daily exposed curve as well as similar social mixing patterns. Furthermore, we demonstrate that several nations with high inverse coefficient of variations in daily exposed numbers are amongst the worst COVID-19 affected places. Our insights on the effects of lockdown on human mobility motivate future research in the identification of hotspots, design of intelligent mobility strategies and quarantine procedures to curb infection spread.

Mobility-as-a-Service Challenges and Opportunities in the Post-Pandemic

“…The mobility of citizens and the way it promotes high social mixing are decisive factors in the spread of the COVID-19 virus, and which directly impacts the number of people infected and the number of deaths. In [15], the authors use a generalized spatiotemporal model that aims to quantify the role of high social mixing and mobility in the spread of the pandemic through a composite latent factor. The study presents tests done on mobility, the number of infected and deaths in New York City in order to prove that places with high interzone mobility effectively have a sync in peaks of the daily exposed curve as well as similar social mixing patterns.…”

Determining the rate of infectious disease testing through contagion potential

“…The heterogeneity in the presentation of the symptoms makes the knowledge of the extent of viral load shedding by asymptomatic carriers an imperative [34]. The research community at large is coping with this uncertainty through the design of epidemiological models that account for the spread effected by the asymptomatic [35], the use of contact tracing mobile applications to track the route of contagion [36][37][38], and the incentivization of self-quarantine and home-testing kits [39]. These efforts are still hindered by the dearth of knowledge of virus shedding by the carriers and the limitations in the assumptions to model it [40].…”

Spatiotemporal tracing of pandemic spread from infection data

“…As evidenced by the existing literature, the daily infection count over time is considered to be a comprehensive measure of the extent of infection spread. This is because it is difficult to quantify the real asymptomatic (or exposed) count, while the mortality rates are contingent on several socioeconomic and demographic covariates 14 , 26 . The proposed approach generates a complete network of zones (viz., counties, boroughs, states, etc.…”