Purely Data-driven Exploration of COVID-19 Pandemic After Three Months of the Outbreak

Many research studies have been carried out to understand the epidemiological characteristics of the COVID-19 pandemic in its early phase. The current study is yet another contribution to better understand the disease properties by parameter estimation based on mathematical SIR epidemic modeling. The authors used Johns Hopkins University’s dataset to estimate the basic reproduction number of COVID-19 for five representative countries (Japan, Germany, Italy, France, and the Netherlands) that were selected using cluster analysis. As byproducts, the authors estimated the transmission, recovery, and death rates for each selected country and carried out statistical tests to see if there were any significant differences.

COVID-19 Vaccination Strategies Considering Hesitancy Using Particle-Based Epidemic Simulation

Vaccine hesitancy is one of the critical factors in achieving herd immunity and suppressing the COVID-19 epidemic. Many countries face this as an acute public health issue that diminishes the efficacy of their vaccination campaigns. Epidemic modeling and simulation can be used to predict the effects of different vaccination strategies. In this work, we present an open-source particle-based COVID-19 simulator with a vaccination module capable of taking into account the vaccine hesitancy of the population. To demonstrate the efficacy of the simulator, we conducted extensive simulations for the province of Lecco, Italy. The results indicate that the combination of both high vaccination rate and low hesitancy leads to faster epidemic suppression.

COVID-19 and Networks

Ongoing COVID-19 pandemic poses many challenges to the research of artificial intelligence. Epidemics are important in network science for modeling disease spread over networks of contacts between individuals. To prevent disease spread, it is desirable to introduce prioritized isolation of the individuals contacting many and unspecified others, or connecting different groups. Finding such influential individuals in social networks, and simulating the speed and extent of the disease spread are what we need for combating COVID-19. This article focuses on the following topics, and discusses some of the traditional and emerging research attempts: (1) topics related to epidemics in network science, such as epidemic modeling, influence maximization and temporal networks, (2) recent research of network science for COVID-19 and (3) datasets and resources for COVID-19 research.

Age-specific rate of severe and critical SARS-CoV-2 infections estimated with multi-country seroprevalence studies

Knowing the age-specific rates at which individuals infected with SARS-CoV-2 develop severe and critical disease is essential for designing public policy, for epidemic modeling, and for individual risk evaluation. In this study, we present the first estimates of these rates using multi-country serology studies, together with public data on hospital admissions and mortality. Our results show that the risk of severe and critical disease increases exponentially with age, but much less steeply than the risk of fatal illness. Importantly, the estimated rate of severe disease outcome in adolescents is an order of magnitude larger than the reported rate of vaccine side-effects; thus, showing how these estimates are relevant for health policy. Finally, we validate our results by showing that they are in close agreement with the estimates obtained from an indirect method that uses reported infection fatality rates estimates and hospital mortality data.

Epidemic Modeling of Multiple Virus Strains: A Case Study of SARS-CoV-2 B.1.1.7 In Moscow

During a long-running pandemic a pathogen can mutate, producing new strains with different epidemiological parameters. Existing approaches to epidemic modeling only consider one virus strain. We have developed a modified Susceptible-Exposed-Infected-Recovered model to simulate multiple virus strains within the same population. As a case study, we investigate the potential effects of SARS-CoV-2 strain B.1.1.7 on the city of Moscow. Our analysis indicates a high risk of a new wave of infections in September-October 2021 with up to 35 000 daily infections at peak. We open-source our code and data.

UTLDR: an agent-based framework for modeling infectious diseases and public interventions

Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate “what if” epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns…). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

Epidemic modeling: Diffusion approximation vs. stochastic differential equations allowing reflection

A birth–death process is considered as an epidemic model with recovery and transmittance from outside. The fraction of infected individuals is for huge population sizes approximated by a solution of an ordinary differential equation taking values in [Formula: see text]. For intermediate size or semilarge populations, the fraction of infected individuals is approximated by a diffusion formulated as a stochastic differential equation. That diffusion approximation however needs to be killed at the boundary [Formula: see text]. An alternative stochastic differential equation model is investigated which instead allows a more natural reflection at the boundary.