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

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.

Spatiotemporal tracing of pandemic spread from infection data.

COVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection during future outbreaks of the same or variants of the virus. The reliance on existing prediction and contact tracing approaches on prior knowledge of inter- or intra-zone mobility renders them impracticable. We present a spatiotemporal approach that employs a network inference approach with sliding time windows solely on the date and number of daily infection numbers of zones within a geographical region to generate temporal networks capturing the influence of each zone on another. It helps analyze the spatial interaction among the hotspot or spreader zones and highly affected zones based on the flow of network contagion traffic. We apply the proposed approach to the daily infection counts of New York State as well as the states of USA to show that it effectively measures the phase shifts in the pandemic timeline. It identifies the spreaders and affected zones at different time points and helps infer the trajectory of the pandemic spread across the country. A small set of zones periodically exhibit a very high outflow of contagion traffic over time, suggesting that they act as the key spreaders of infection. Moreover, the strong influence between the majority of non-neighbor regions suggests that the overall spread of infection is a result of the unavoidable long-distance trips by a large number of people as opposed to the shorter trips at a county level, thereby informing future mitigation measures and public policies.