Who Should Get Vaccinated First? An effective Network Information-driven Priority Vaccination Strategy (preprint)

Approval of emergency use of the Novel Coronavirus Disease 2019 (COVID-19) vaccines in many countries has brought hope to ending the COVID-19 pandemic sooner. Considering the limited vaccine supply in the early stage of COVID-19 vaccination programs in most countries, a highly relevant question to ask is: who should get vaccinated first? In this article we propose a network information- driven vaccination strategy where a small number of people in a network (population) are categorized, according to a few key network properties, into priority groups. Using a network-based SEIR model for simulating the pandemic progression, the network information-driven vaccination strategy is compared with a random vaccination strategy. Results for both large-scale synthesized networks and real social networks have demonstrated that the network information-driven vaccination strategy can significantly reduce the cumulative number of infected individuals and lead to a more rapid containment of the pandemic. The results provide insight for policymakers in designing an effective early-stage vaccination plan.

Who should get vaccinated first? An effective network information-driven priority vaccination strategy (preprint)

Approval of emergency use of the Novel Coronavirus Disease 2019 (COVID-19) vaccines in many countries has brought hope to ending the COVID-19 pandemic sooner. Considering the limited vaccine supply in the early stage of COVID-19 vaccination programs in most countries, a highly relevant question to ask is: who should get vaccinated first? In this article we propose a network information-driven vaccination strategy where a small number of people in a network (population) are categorized, according to a few key network properties, into priority groups. Using a network-based SEIR model for simulating the pandemic progression, the network information-driven vaccination strategy is compared with a random vaccination strategy. Results for both large-scale synthesized networks and real social networks have demonstrated that the network information-driven vaccination strategy can significantly reduce the cumulative number of infected individuals and lead to a more rapid containment of the pandemic. The results provide insight for policymakers in designing an effective early-stage vaccination plan.

Who Should Get Vaccinated First? An Effective Network Information-Driven Priority Vaccination Strategy (preprint)

Approval of emergency use of the Novel Coronavirus Disease 2019 (COVID-19) vaccines in many countries has brought hope to ending the COVID-19 pandemic sooner. Considering the limited vaccine supply in the early stage of COVID-19 vaccination programs in most countries, a highly relevant question to ask is: {\em who should get vaccinated first?} In this article we propose a network information-driven vaccination strategy where a small number of people in a network (population) are categorized, according to a few key network properties, into priority groups. Using a network-based SEIR model for simulating the pandemic progression, the network information-driven vaccination strategy is compared with a random vaccination strategy. Results for both large-scale synthesized networks and real social networks have demonstrated that the network information-driven vaccination strategy can significantly reduce the cumulative number of infected individuals and lead to a more rapid containment of the pandemic. The results provide insight for policymakers in designing an effective early-stage vaccination plan.Funding Statement: This work is the results of a research project funded by City University of Hong Kong (Project Number 9229031).Declaration of Interests: The author declares no conflict of interest.

General Model for COVID-19 Spreading with Consideration of Intercity Migration, Insufficient Testing and Active Intervention: Application to Study of Pandemic Progression in Japan and USA (preprint)

A new Susceptible-Exposed-Infected-Confirmed-Removed (SEICR) model with consideration of intercity travel and active intervention is proposed for predicting the spreading progression of the 2019 New Coronavirus Disease (COVID-19). The model takes into account the known or reported number of infected cases being fewer than the actual number of infected individuals due to insufficient testing. The model integrates intercity travel data to track the movement of exposed and infected individuals among cities, and allows different levels of active intervention to be considered so that realistic prediction of the number of infected individuals can be performed. The data of the COVID-19 infection cases and the intercity travel data for Japan (January 15 to March 20, 2020) and the USA (February 20 to March 20, 2020) are used to illustrate the prediction of the pandemic progression in 47 regions of Japan and 50 states (plus a federal district) in the USA. By fitting the model with the data, we reveal that, as of March 19, 2020, the number of infected individuals in Japan and the USA could be twenty-fold and five-fold as many as the number of confirmed cases, respectively. Moreover, the model generates future progression profiles for different levels of intervention by setting the parameters relative to the values found from the data fitting. Results show that without tightening the implementation of active intervention, Japan and the USA will see about 6.55% and 18.2% of the population eventually infected, and with drastic ten-fold elevated active intervention, the number of people eventually infected can be reduced by up to 95% in Japan and 70% in the USA. Finally, an assessment of the relative effectiveness of active intervention and personal protective measures is discussed. With a highly vigilant public maintaining personal hygiene and exercising strict protective measures, the percentage of population infected can be further reduced to 0.23% in Japan and 2.7% in the USA.

Prediction of COVID-19 Spreading Profiles in South Korea, Italy and Iran by Data-Driven Coding (preprint)

This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before the end of March 2020, and that the percentage of population infected in each city will be less than 0.01%, 0.05% and 0.02%, for South Korea, Italy and Iran, respectively.

Modeling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data (preprint)

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China are collected from Baidu Migration, a mobile-app based human migration tracking data system. Historical data of infected, recovered and death cases from official source are used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimization procedure is used for estimation of the dynamics of epidemic spreading in the coming weeks. Our results show that the number of infections in most cities in China will peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively.