Analysis of individual strategies for artificial and natural immunity with imperfectness and durability of protection

Evolutionary analysis of a coupled epidemic-voluntary vaccination behavior model with immunity waning on complex networks

Vaccination is the most effective method of preventing and controlling the transmission of infectious diseases within populations. However, the phenomenon of waning immunity can induce periodic fluctuations in epidemic spreading. This study proposes a coupled epidemic-vaccination dynamic model to analyze the influence of immunity waning on the epidemic spreading within the context of voluntary vaccination. First, we establish an SIRSV (susceptible-infected-recovered-susceptible-vaccinated) compartment model to describe the transmission mechanism of infectious diseases based on the mean-field theory. Within this model, we incorporate a nonlinear infection rate with network topology and consider the waning natural and vaccine-induced immunity at the individual level. The evolutionary model of voluntary vaccination strategy is integrated into the SIRSV model to characterize the impact of vaccination behavior on the infectious disease transmission. We also consider two individual risk assessment methods, namely, the individual-based risk assessment (IB-RA) method and the society-based risk assessment (SB-RA) method, originating from local and global perspectives, respectively. Then, utilizing the next-generation matrix method, we derive the time-varying effective reproduction numbers of the model. Also, the theoretical analysis of optimal strategy thresholds in the individual decision-making process is also conducted. The results indicate that the thresholds obtained from the agent-based model (ABM) simulation method are consistent with the theoretical analysis, demonstrating the effectiveness of our model. Finally, we apply the coupled model to the COVID-19 pandemic in France, Germany, Italy, and the United Kingdom. This study analyzes the impact of waning immunity and provides early warning for the outbreak of the epidemics.

Assessing the instantaneous social dilemma on social distancing attitudes and vaccine behavior in disease control

In the face of infectious disease outbreaks, the collective behavior of a society can has a profound impact on the course of the epidemic. This study investigates the instantaneous social dilemma presented by individuals' attitudes toward vaccine behavior and its influence on social distancing as a critical component in disease control strategies. The research employs a multifaceted approach, combining modeling techniques and simulation to comprehensively assess the dynamics between social distancing attitudes and vaccine uptake during disease outbreaks. With respect to modeling, we introduce a new vaccination game (VG) where, unlike conventional VG models, a 2-player and 2-strategy payoff structure is aptly embedded in the individual behavior dynamics. Individuals' willingness to adhere to social distancing measures, such as mask-wearing and physical distancing, is strongly associated with their inclination to receive vaccines. The study reveals that a positive attitude towards social distancing tends to align with a higher likelihood of vaccine acceptance, ultimately contributing to more effective disease control. As the COVID-19 pandemic has demonstrated, swift and coordinated public health measures are essential to curbing the spread of infectious diseases. This study underscores the urgency of addressing the instantaneous social dilemma posed by individuals' attitudes. By understanding the intricate relationship between these factors, policymakers, and healthcare professionals can develop tailored strategies to promote both social distancing compliance and vaccine acceptance, thereby enhancing our ability to control and mitigate the impact of disease outbreaks in the future.

Policy choices for Shanghai responding to challenges of Omicron

Background

A new wave of Coronavirus disease 2019 (COVID-19) infection driven by Omicron BA.2 subvariant hit Shanghai end of February 2020. With higher transmissibility and milder symptoms, the daily new confirmed cases have soared to more than 20 K within one and a half months. The greatest challenge of Omicron spreading is that the rapidly surging number of infected populations overwhelming the healthcare system. What policy is effective for huge cities to fight against fast-spreading COVID-19 new variant remains a question.

Methods

A system dynamics model of the Shanghai Omicron epidemic was developed as an extension of the traditional susceptible-exposed-infected-susceptible recovered (SEIR) model to incorporate the policies, such as contact tracing and quarantine, COVID-19 testing, isolation of areas concerned, and vaccination. Epidemic data from Shanghai Municipal Health Commission were collected for model validation.

Results

Three policies were tested with the model: COVID-19 testing, isolation of areas concerned, and vaccination. Maintaining a high level of COVID-19 testing and transfer rate of the infected population can prevent the number of daily new confirmed cases from recurring growth. In the scenario that 50% of the infected population could be transferred for quarantine on daily bases, the daily confirmed asymptomatic cases and symptomatic cases remained at a low level under 100. For isolation of areas concerned, in the scenario with most isolation scope, the peak of daily confirmed asymptomatic and symptomatic cases dropped 18 and 16%, respectively, compared with that in the scenario with least isolation. Regarding vaccination, increasing the vaccination rate from 75 to 95% only slightly reduced the peak of the confirmed cases, but it can reduce the severe cases and death by 170%.

Conclusions

The effective policies for Omicron include high level of testing capacity with a combination of RAT and PCR testing to identify and quarantine the infected cases, especially the asymptomatic cases. Immediate home-isolation and fast transfer to centralized quarantine location could help control the spread of the virus. Moreover, to promote the vaccination in vulnerable population could significantly reduce the severe cases and death. These policies could be applicable to all metropolises with huge population facing high transmissible low severity epidemic.

Overlapping Delta and Omicron Outbreaks During the COVID-19 Pandemic: Dynamic Panel Data Estimates

BACKGROUND

The Omicron variant of SARS-CoV-2 is more transmissible than prior variants of concern (VOCs). It has caused the largest outbreaks in the pandemic, with increases in mortality and hospitalizations. Early data on the spread of Omicron were captured in countries with relatively low case counts, so it was unclear how the arrival of Omicron would impact the trajectory of the pandemic in countries already experiencing high levels of community transmission of Delta.

OBJECTIVE

The objective of this study is to quantify and explain the impact of Omicron on pandemic trajectories and how they differ between countries that were or were not in a Delta outbreak at the time Omicron occurred.

METHODS

We used SARS-CoV-2 surveillance and genetic sequence data to classify countries into 2 groups: those that were in a Delta outbreak (defined by at least 10 novel daily transmissions per 100,000 population) when Omicron was first sequenced in the country and those that were not. We used trend analysis, survival curves, and dynamic panel regression models to compare outbreaks in the 2 groups over the period from November 1, 2021, to February 11, 2022. We summarized the outbreaks in terms of their peak rate of SARS-CoV-2 infections and the duration of time the outbreaks took to reach the peak rate.

RESULTS

Countries that were already in an outbreak with predominantly Delta lineages when Omicron arrived took longer to reach their peak rate and saw greater than a twofold increase (2.04) in the average apex of the Omicron outbreak compared to countries that were not yet in an outbreak.

CONCLUSIONS

These results suggest that high community transmission of Delta at the time of the first detection of Omicron was not protective, but rather preluded larger outbreaks in those countries. Outbreak status may reflect a generally susceptible population, due to overlapping factors, including climate, policy, and individual behavior. In the absence of strong mitigation measures, arrival of a new, more transmissible variant in these countries is therefore more likely to lead to larger outbreaks. Alternately, countries with enhanced surveillance programs and incentives may be more likely to both exist in an outbreak status and detect more cases during an outbreak, resulting in a spurious relationship. Either way, these data argue against herd immunity mitigating future outbreaks with variants that have undergone significant antigenic shifts.

Effects of void nodes on epidemic spreads in networks

We present the pair approximation models for susceptible–infected–recovered (SIR) epidemic dynamics in a sparse network based on a regular network. Two processes are considered, namely, a Markovian process with a constant recovery rate and a non-Markovian process with a fixed recovery time. We derive the implicit analytical expression for the final epidemic size and explicitly show the epidemic threshold in both Markovian and non-Markovian processes. As the connection rate decreases from the original network connection, the epidemic threshold in which epidemic phase transits from disease-free to endemic increases, and the final epidemic size decreases. Additionally, for comparison with sparse and heterogeneous networks, the pair approximation models were applied to a heterogeneous network with a degree distribution. The obtained phase diagram reveals that, upon increasing the degree of the original random regular networks and decreasing the effective connections by introducing void nodes accordingly, the final epidemic size of the sparse network is close to that of the random network with average degree of 4. Thus, introducing the void nodes in the network leads to more heterogeneous network and reduces the final epidemic size.

Optimal Voluntary Vaccination of Adults and Adolescents Can Help Eradicate Hepatitis B in China

Hepatitis B (HBV) is one of the most common infectious diseases, with a worldwide annual incidence of over 250 million people. About one-third of the cases are in China. While China made significant efforts to implement a nationwide HBV vaccination program for newborns, a significant number of susceptible adults and teens remain. In this paper, we analyze a game-theoretical model of HBV dynamics that incorporates government-provided vaccination at birth coupled with voluntary vaccinations of susceptible adults and teens. We show that the optimal voluntary vaccination brings the disease incidence to very low levels. This result is robust and, in particular, due to a high HBV treatment cost, essentially independent from the vaccine cost.

Prosocial behavior of wearing a mask during an epidemic: an evolutionary explanation

In the midst of the COVID-19 pandemic, with limited or no supplies of vaccines and treatments, people and policymakers seek easy to implement and cost-effective alternatives to combat the spread of infection during the pandemic. The practice of wearing a mask, which requires change in people's usual behavior, may reduce disease transmission by preventing the virus spread from infectious to susceptible individuals. Wearing a mask may result in a public good game structure, where an individual does not want to wear a mask but desires that others wear it. This study develops and analyzes a new intervention game model that combines the mathematical models of epidemiology with evolutionary game theory. This approach quantifies how people use mask-wearing and related protecting behaviors that directly benefit the wearer and bring some advantage to other people during an epidemic. At each time-step, a suspected susceptible individual decides whether to wear a facemask, or not, due to a social learning process that accounts for the risk of infection and mask cost. Numerical results reveal a diverse and rich social dilemma structure that is hidden behind this mask-wearing dilemma. Our results highlight the sociological dimension of mask-wearing policy.