A Novel Point Process Model for COVID-19: Multivariate Recursive Hawkes Process

This chapter presents a novel point process model for COVID-19 transmission—the multivariate recursive Hawkes process, which is an extension of the recursive Hawkes model to the multivariate case. Equivalently the model can be viewed as an extension of the multivariate Hawkes model to allow for varying productivity as in the recursive model. Several theoretical properties of this process are stated and proved, including the existence of the multivariate recursive counting process and formulas for the mean and variance. EM-based algorithms are explored for estimating parameters of parametric and semi-parametric forms of the model. Additionally, an algorithm is presented to reconstruct the process from imprecise event times. The performance of the algorithms on both synthetic and real COVID-19 data sets is illustrated through several experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A MARTINGALE FORMULATION FOR STOCHASTIC COMPARTMENTAL SUSCEPTIBLE-INFECTED-RECOVERED (SIR) MODELS TO ANALYZE FINITE SIZE EFFECTS IN COVID-19 CASE STUDIES

Deterministic compartmental models for infectious diseases give the mean behaviour of stochastic agent-based models. These models work well for counterfactual studies in which a fully mixed large-scale population is relevant. However, with finite size populations, chance variations may lead to significant departures from the mean. In real-life applications, finite size effects arise from the variance of individual realizations of an epidemic course about its fluid limit. In this article, we consider the classical stochastic Susceptible-Infected-Recovered (SIR) model, and derive a martingale formulation consisting of a deterministic and a stochastic component. The deterministic part coincides with the classical deterministic SIR model and we provide an upper bound for the stochastic part. Through analysis of the stochastic component depending on varying population size, we provide a theoretical explanation of finite size effects. Our theory is supported by quantitative and direct numerical simulations of theoretical infinitesimal variance. Case studies of coronavirus disease 2019 (COVID-19) transmission in smaller populations illustrate that the theory provides an envelope of possible outcomes that includes the field data.

An Analysis of COVID-19 Knowledge Graph Construction and Applications

The construction and application of knowledge graphs have seen a rapid increase across many disciplines in re-cent years. Additionally, the problem of uncovering relationships between developments in the COVID-19 pandemic and social me-dia behavior is of great interest to researchers hoping to curb the spread of the disease. In this paper we present a knowledge graph constructed from COVID-19 related tweets in the Los Angeles area, supplemented with federal and state policy announcements and disease spread statistics. By incorporating dates, topics, and events as entities, we construct a knowledge graph that describes the connections between these useful information. We use natural language processing and change point analysis to extract tweet-topic, tweet-date, and event-date relations. Further analysis on the constructed knowledge graph provides insight into how tweets reflect public sentiments towards COVID-19 related topics and how changes in these sentiments correlate with real-world events. © 2021 IEEE.