The resurgence risk of COVID-19 in the presence of immunity waning and ADE effect: a mathematical modelling study

Since the end of 2020, the mass vaccination has been actively promoted and seemed to be effective to bring the COVID-19 pandemic under control. However, the fact of immunity waning and the possible existence of antibody-dependent enhancement (ADE) make the situation uncertain. We developed a dynamic model of COVID-19 incorporating vaccination and immunity waning, which was calibrated by using the data of accumulative vaccine doses administered and the COVID-19 epidemic in 2020 in mainland China. We explored how long the current vaccination program can prevent China in a low risk of resurgence, and how ADE affects the long-term trajectory of COVID-19 epidemics. The prediction suggests that the vaccination coverage with at least one dose reach 95.87%, and with two-doses reach 77.92% on August 31, 2021. However, even with the mass vaccination, randomly introducing infected cases in the post-vaccination period can result in large outbreaks quickly in the presence of immunity waning, particularly for SARS-CoV-2 variants with higher transmission ability. The results showed that with the current vaccination program and a proportion of 50% population wearing masks, mainland China can be protected in a low risk of resurgence till 2023/01/18. However, ADE effect and higher transmission ability for variants would significantly shorten the protective period for more than 1 year. Furthermore, intermittent outbreaks can occur while the peak values of the subsequential outbreaks are decreasing, meaning that subsequential outbreaks boosted the immunity in the population level, which further indicating that catching-up vaccination program can help to mitigate the possible outbreaks, even avoid the outbreaks. The findings reveal that integrated effects of multiple factors, including immunity waning, ADE, relaxed interventions, and higher transmission ability of variants, make the control of COVID-19 much more difficult. We should get ready for a long struggle with COVID-19, and should not totally rely on COVID-19 vaccine.

Controlling multiple COVID-19 epidemic waves: an insight from a multi-scale model linking the behavior change dynamics to the disease transmission dynamics

COVID-19 epidemics exhibited multiple waves regionally and globally since 2020. It is important to understand the insight and underlying mechanisms of the multiple waves of COVID-19 epidemics in order to design more efficient non-pharmaceutical interventions (NPIs) and vaccination strategies to prevent future waves. We propose a multi-scale model by linking the behavior change dynamics to the disease transmission dynamics to investigate the effect of behavior dynamics on COVID-19 epidemics using the game theory. The proposed multi-scale model was calibrated and key parameters related to disease transmission dynamics and behavioral dynamics with/without vaccination were estimated based on COVID-19 epidemic data and vaccination data. Our modeling results demonstrate that the feedback loop between behavior changes and COVID-19 transmission dynamics plays an essential role in inducing multiple epidemic waves. We find that the long period of high-prevalence or persistent deterioration of COVID-19 epidemics could drive almost all population to change their behaviors and maintain the altered behaviors, however, the effect of behavior changes faded out gradually along the progress of epidemics. This suggests that it is essential not only to have persistent, but also effective behavior changes in order to avoid subsequent epidemic waves. In addition, our model also suggests the importance to maintain the effective altered behaviors during the initial stage of vaccination, and to counteract relaxation of NPIs, it requires quick and massive vaccination to avoid future epidemic waves.

Advance on theoretical epidemiology models research of prevention and control of COVID-19 / 中华预防医学杂志

COVID-19 has brought a significant impact to the global health system, and also opportunities and challenges to epidemiological researches. Theoretical epidemiological models can simulate the process of epidemic in scenarios under different conditions. Therefore, modeling researches can analyze the epidemical trend of COVID-19, predict epidemical risks, and evaluate effects of different control measures and vaccine policies. Theoretical epidemiological modeling researches provide scientific advice for the prevention and control of infectious diseases, and play a crucial role in containing COVID-19 over the past year. In this study, we review the theoretical epidemiological modeling researches on COVID-19 and summarize the role of theoretical epidemiological models in the prevention and control of COVID-19, in order to provide reference for the combination of mathematical modeling and epidemic control.

The challenges of the coming mass vaccination and exit strategy in prevention and control of COVID-19, a modelling study

With success in the development of COVID-19 vaccines, it is urgent and challenging to analyse how the coming large-scale vaccination in the population and the growing public desire of relaxation of non-pharmaceutical interventions (NPIs) interact to impact the prevention and control of the COVID-19 pandemic. Using mathematical models, we focus on two aspects: 1) how the vaccination program should be designed to balance the dynamic exit of NPIs; 2) how much the vaccination coverage is needed to avoid a second wave of the epidemics when the NPIs exit in stages. We address this issue globally, and take six countries--China, Brazil, Indonesia, Russia, UK, and US--in our case study. We showed that a dynamic vaccination program in three stages can be an effective approach to balance the dynamic exit of the NPIs in terms of mitigating the epidemics. The vaccination rates and the accumulative vaccination coverage in these countries are estimated by fitting the model to the real data. We observed that the required effective vaccination coverages are greatly different to balance the dynamic exit of NPIs in these countries, providing a quantitative criterion for the requirement of an integrative package of NPIs. We predicted the epidemics under different vaccination rates for these countries, and showed that the vaccination can significantly decrease the peak value of a future wave. Furthermore, we found that a lower vaccination coverage can result in a subsequent wave once the NPIs exit. Therefore, there is a critical (minimum) vaccination coverage, depending on effectiveness of NPIs to avoid a subsequent wave. We estimated the critical vaccination coverages for China, Brazil, and Indonesia under different scenarios. In conclusion, we quantitatively showed that the dynamic vaccination program can be the effective approach to supplement or even eventually replace NPIs in mitigating the epidemics and avoiding future waves, and we suggest that country level-based exit strategies of the NPIs should be considered, according to the possible quarantine rate and testing ability, and the accessibility, affordability and efficiency of the vaccines.

Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use

BackgroundMultiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon. MethodsWe developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration. ResultsWithout a vaccine, the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8-4 million infections and 15,000-240,000 deaths across these four states over the next 12 months. In this scenario, introducing a vaccine would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50%, a vaccine that is only 50% effective (weak vaccine) would require coverage of 55-94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32-57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely, a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48-78% or a strong vaccine (100% effective) with coverage of 33-58% would be required to suppress the epidemic. Delaying vaccination rollout for 1-2 months would not substantially alter the epidemic trend if the current interventions are maintained. ConclusionsThe degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic.

Effects of New York's Executive Order on Face Mask Use on COVID-19 Infections and Mortality: A Modeling Study

BackgroundNew York City (NYC) was the epicenter of the COVID-19 pandemic in the United States. On April 17, 2020, the State of New York implemented an Executive Order that requires all people in New York to wear a face mask or covering in public settings where social distancing cannot be maintained. It is unclear how this Executive Order has affected the spread of COVID-19 in NYC. MethodsA dynamic compartmental model of COVID-19 transmission among NYC residents was developed to assess the effect of the Executive Order on face mask use on infections and deaths due to COVID-19 in NYC. Data on daily and cumulative COVID-19 infections and deaths were obtained from the NYC Department of Health and Mental Hygiene. ResultsThe Executive Order on face mask use is estimated to avert 99,517 (95% CIs: 72,723-126,312) COVID-19 infections and 7,978 (5,692-10,265) deaths in NYC. If the Executive Order was implemented one week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593-141,356) and 9,017 (6,446-11,589), respectively. If the Executive Order was implemented two weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373-162,824) and 10,515 (7,540-13,489), respectively. ConclusionsNew Yorks Executive Order on face mask use is projected to have significantly reduced the spread of COVID-19 in NYC. Implementing the Executive Order at an earlier date would avert even more COVID-19 infections and deaths.

Effects of medical resource capacities and intensities of public mitigation measures on outcomes of COVID-19 outbreaks

The COVID-19 pandemic is complex and is developing in different ways according to the country involved. To identify the key parameters or processes that have the greatest effects on the pandemic and reveal the different progressions of epidemics in different countries, we quantified enhanced control measures and the dynamics of the production and provision of medical resources. We then nested these within a COVID-19 epidemic transmission model, which is parameterized by multi-source data. We obtained rate functions related to the intensity of mitigation measures, the effective reproduction numbers and the timings and durations of runs on medical resources, given differing control measures implemented in various countries. Increased detection rates may induce runs on medical resources and prolong their durations, depending on resource availability. Nevertheless, improving the detection rate can effectively and rapidly reduce the mortality rate, even after runs on medical resources. Combinations of multiple prevention and control strategies and timely improvement of abilities to supplement medical resources are key to effective control of the COVID-19 epidemic. A 50% reduction in comprehensive control measures would have led to the cumulative numbers of confirmed cases and deaths exceeding 590000 and 60000, respectively, by 27 March 2020 in mainland China. The proposed model can assist health authorities to predict when they will be most in need of hospital beds and equipment such as ventilators, personal protection equipment, drugs and staff. One sentence summaryMultiple data sources and cross validation of a COVID-19 epidemic model, coupled with a medical resource logistic model, reveal that the key factors that affect epidemic progressions and their outbreak patterns in different countries are the type of emergency medical response to avoid runs on medical resources, especially improved detection rates, the ability to promote public health measures, and the synergistic effects of combinations of multiple prevention and control strategies.

Lessons drawn from China and South Korea for managing COVID-19 epidemic: insights from a comparative modeling study

We conducted a comparative study of COVID-19 epidemic in three different settings: mainland China, the Guangdong province of China and South Korea, by formulating two disease transmission dynamics models incorporating epidemic characteristics and setting-specific interventions, and fitting the models to multi-source data to identify initial and effective reproduction numbers and evaluate effectiveness of interventions. We estimated the initial basic reproduction number for South Korea, the Guangdong province and mainland China as 2.6 (95% confidence interval (CI): (2.5, 2.7)), 3.0 (95%CI: (2.6, 3.3)) and 3.8 (95%CI: (3.5,4.2)), respectively, given a serial interval with mean of 5 days with standard deviation of 3 days. We found that the effective reproduction number for the Guangdong province and mainland China has fallen below the threshold 1 since February 8th and 18th respectively, while the effective reproduction number for South Korea remains high, suggesting that the interventions implemented need to be enhanced in order to halt further infections. We also project the epidemic trend in South Korea under different scenarios where a portion or the entirety of the integrated package of interventions in China is used. We show that a coherent and integrated approach with stringent public health interventions is the key to the success of containing the epidemic in China and specially its provinces outside its epicenter, and we show that this approach can also be effective to mitigate the burden of the COVID-19 epidemic in South Korea. The experience of outbreak control in mainland China should be a guiding reference for the rest of the world including South Korea.

Stochastic discrete epidemic modeling of COVID-19 transmission in the Province of Shaanxi incorporating public health intervention and case importation

Before the lock-down of Wuhan/Hubei/China, on January 23rd 2020, a large number of individuals infected by COVID-19 moved from the epicenter Wuhan and the Hubei province due to the Spring Festival, resulting in an epidemic in the other provinces including the Shaanxi province. The epidemic scale in Shaanxi was comparatively small and with half of cases being imported from the epicenter. Based on the complete epidemic data including the symptom onset time and transmission chains, we calculate the control reproduction number (1.48-1.69) in Xian. We could also compute the time transition, for each imported or local case, from the latent, to infected, to hospitalized compartment, as well as the effective reproduction number. This calculation enables us to revise our early deterministic transmission model to a stochastic discrete epidemic model with case importation and parameterize it. Our model-based analyses reveal that the newly generated infections decay to zero quickly; the cumulative number of case-driven quarantined individuals via contact tracing stabilize at a manageable level, indicating that the intervention strategies implemented in the Shaanxi province have been effective. Risk analyses, important for the consideration of "resumption of work", show that a large second outbreak is expected if the level of case importation remains at the same level as between January 10th and February 4th 2020. However, if the case importation decreases by 30%, 60% and 90%, the second outbreak if happening will be of small-scale assuming contact tracing and quarantine/isolation remain as effective as before. Finally, we consider the effects of intermittent inflow with a Poisson distribution on the likelihood of multiple outbreaks. We believe the developed methodology and stochastic model provide an important model framework for the evaluation of revising travel restriction rules in the consideration of resuming social-economic activities while managing the disease control with potential case importation.

Lockdown may partially halt the spread of 2019 novel coronavirus in Hubei province, China

We present a timely evaluation of the impact of lockdown on the 2019-nCov epidemic in Hubei province, China. The implementation appears to be effective in reducing about 60% of new infections and deaths, and its effect also appears to be sustainable even after its removal. Delaying its implementation reduces its effectiveness. However, the direct economic cost of such a lockdown remains to be seen and whether the model is replicable in other Chinese regions remains a matter of further investigation.

Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak / 中华流行病学杂志

Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes.

Prevalence status for smokers of undergraduates in Huaihua and the influential factors / 中南大学学报(医学版)

OBJECTIVE@#To investigate the prevalence status and the influential factors for smokers of undergraduates in Huaihua.@*METHODS@#The undergraduates from different grades and different professions in Huaihua were selected randomly and investigated using a self-developed questionnaire. The influential factors for smokers of students were analyzed using a Chi-square test and a logistic regression.@*RESULTS@#A total of 1 050 undergraduates were surveyed in our study. Among them, 973 were valid. The rate for students with a habit of smoking was 21.17%. 53.7% of smokers began to smoke at the stage of primary and high schools. The main reason for smoking was due to curious (47.1%) or depressive (45.2%). The male students were more likely to smoke than female students (OR=6.730). The attitudes of parents on smoking, such as no intervention, were important risk factors that affect the children's behavior on smoking (OR=1.866).@*CONCLUSION@#The periods in primary and high school are the most possible time for student to begin smoking. The curiosity and depression are the most risk factors, and the gender and family education are also the important factors that affect the behavior of undergraduates on smoking.