Mutation induced infection waves in diseases like COVID-19

Rational social distancing policy during epidemics with limited healthcare capacity

Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During recent epidemics there has been much debate about policy, including how and when to impose restrictions on behaviour. Policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Whether health services might be 'overwhelmed' has emerged as a key consideration. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. In order to achieve this, we develop a nested optimisation algorithm of both the government intervention strategy and the resulting equilibrium behaviour of individuals. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. We find an extremely sharp drop in peak infections at a critical maximum infection cost in the government's objective function. This is in marked contrast to the gradual reduction of infections if individuals make decisions without government intervention. We find optimal interventions vary less strongly in time when interventions are costly to the government and that the critical cost of the policy switch depends on how costly interventions are.

Occupational risks associated with severe COVID-19 disease and SARS-CoV-2 infection - a Swedish national case-control study conducted from October 2020 to December 2021

OBJECTIVE

This study aimed to investigate whether workplace factors and occupations are associated with SARS-CoV-2 infection or severe COVID-19 in the later waves of the pandemic.

METHODS

We studied 552 562 cases with a positive test for SARS-CoV-2 in the Swedish registry of communicable diseases, and 5985 cases with severe COVID-19 based on hospital admissions from October 2020 to December 2021. Four population controls were assigned the index dates of their corresponding cases. We linked job histories to job-exposure matrices to assess the odds for different transmission dimensions and different occupations. We used adjusted conditional logistic analyses to estimate odds ratios (OR) for severe COVID-19 and SARS-CoV-2 with 95% confidence intervals (CI).

RESULTS

The highest OR for severe COVID-19 were for: regular contact with infected patients, (OR 1.37, 95% CI 1.23-1.54), close physical proximity (OR 1.47, 95% CI 1.34-1.61), and high exposure to diseases or infections (OR 1.72, 95% CI 1.52-1.96). Mostly working outside had lower OR (OR 0.77, 95% CI 0.57-1.06). The odds for SARS-CoV-2 when mostly working outside were similar (OR 0.83, 95% CI 0.80-0.86). The occupation with the highest OR for severe COVID-19 (compared with low-exposure occupations) was certified specialist physician (OR 2.05, 95% CI 1.31-3.21) among women and bus and tram drivers (OR 2.04, 95% CI 1.49-2.79) among men.

CONCLUSIONS

Contact with infected patients, close proximity and crowded workplaces increase the risks for severe COVID-19 and SARS-CoV-2 infection. Outdoor work is associated with decreased odds for SARS-CoV-2 infection and severe COVID-19.

Inactivation of Bacteriophage ɸ6 and SARS-CoV-2 in Antimicrobial Surface Tests

Due to the COVID-19 pandemic, researchers have focused on new preventive measures to limit the spread of SARS-CoV-2. One promising application is the usage of antimicrobial materials on often-touched surfaces to reduce the load of infectious virus particles. Since tests with in vitro-propagated SARS-CoV-2 require biosafety level 3 (BSL-3) laboratories with limited capacities and high costs, experiments with an appropriate surrogate like the bacteriophage ɸ6 are preferred in most studies. The aim of this study was to compare ɸ6 and SARS-CoV-2 within antiviral surface tests. Different concentrations of copper coatings on polyethylene terephthalate (PET) were used to determine their neutralizing activity against ɸ6 and SARS-CoV-2. The incubation on the different specimens led to similar inactivation of both SARS-CoV-2 and ɸ6. After 24 h, no infectious virus particles were evident on any of the tested samples. Shorter incubation periods on specimens with high copper concentrations also showed a complete inactivation. In contrast, the uncoated PET foils resulted only in a negligible reduced inactivation during the one-hour incubation. The similar reduction rate for ɸ6 and SARS-CoV-2 in our experiments provide further evidence that the bacteriophage ɸ6 is an adequate model organism for SARS-CoV-2 for this type of testing.

Rational social distancing in epidemics with uncertain vaccination timing

During epidemics people may reduce their social and economic activity to lower their risk of infection. Such social distancing strategies will depend on information about the course of the epidemic but also on when they expect the epidemic to end, for instance due to vaccination. Typically it is difficult to make optimal decisions, because the available information is incomplete and uncertain. Here, we show how optimal decision-making depends on information about vaccination timing in a differential game in which individual decision-making gives rise to Nash equilibria, and the arrival of the vaccine is described by a probability distribution. We predict stronger social distancing the earlier the vaccination is expected and also the more sharply peaked its probability distribution. In particular, equilibrium social distancing only meaningfully deviates from the no-vaccination equilibrium course if the vaccine is expected to arrive before the epidemic would have run its course. We demonstrate how the probability distribution of the vaccination time acts as a generalised form of discounting, with the special case of an exponential vaccination time distribution directly corresponding to regular exponential discounting.

A mathematical model for COVID-19 considering waning immunity, vaccination and control measures

In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people's increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios-in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program are successful measures in significantly controlling the size of infected population. We show that for a country like India, a reduction in contact rate by 50% compared to a reduction of 10% reduces death from 0.0268 to 0.0141% of population. Similarly, for a country like Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48 to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

Psychological Effect of Discrete Outbreak Events of COVID-19 on Health Information Search in China

Community transmission events occasionally happened in the long-term pandemic, which led to repeated outbreaks of COVID-19. In addition to potential physical threats, the outbreaks could also lead to psychological stress and influence their health behaviors, especially for vulnerable people. It poses a great challenge to both physical and mental health management. However, little is known about the impacts of discrete outbreak events of COVID-19 on people's reactions to health concerns in the long-term pandemic period. In the current study, we discussed the impact of discrete outbreak events of COVID-19 on health information search for specific symptoms in China from a perceptive of susceptibility. The empirical study was conducted after the first wave of outbreak events ended in China from June to October 2020. Three typical outbreak events happened during this period, and a total of 1800 search index data for 60 cities in China crawled from Baidu search engine were included in the data set. Using the real-world searching data, we conducted a panel data analysis to examine the psychological effect of discrete outbreak events on the symptom search and the moderation effect of the geographical distance. It was found that discrete outbreak events significantly increased the symptom search, and its impacts were various in different regions. More health information search caused by discrete outbreak events was found in cities which were closer to the outbreak area. Implications for healthcare were discussed.

Anti-Entry Activity of Natural Flavonoids against SARS-CoV-2 by Targeting Spike RBD

COVID-19 is still a global public health concern, and the SARS-CoV-2 mutations require more effective antiviral agents. In this study, the antiviral entry activity of thirty-one flavonoids was systematically evaluated by a SARS-CoV-2 pseudovirus model. Twenty-four flavonoids exhibited antiviral entry activity with IC50 values ranging from 10.27 to 172.63 µM and SI values ranging from 2.33 to 48.69. The structure-activity relationship of these flavonoids as SARS-CoV-2 entry inhibitors was comprehensively summarized. A subsequent biolayer interferometry assay indicated that flavonoids bind to viral spike RBD to block viral interaction with ACE2 receptor, and a molecular docking study also revealed that flavonols could bind to Pocket 3, the non-mutant regions of SARS-CoV-2 variants, suggesting that flavonols might be also active against virus variants. These natural flavonoids showed very low cytotoxic effects on human normal cell lines. Our findings suggested that natural flavonoids might be potential antiviral entry agents against SARS-CoV-2 via inactivating the viral spike. It is hoped that our study will provide some encouraging evidence for the use of natural flavonoids as disinfectants to prevent viral infections.

Pathogen diversity in meta-population networks

The pathogen diversity means that multiple strains coexist, and widely exist in the biology systems. The new mutation of SARS-CoV-2 leading to worldwide pathogen diversity is a typical example. What are the main factors of inducing the pathogen diversity? Previous studies indicated the pathogen mutation is the most important reason for inducing the pathogen diversity. The traffic network and gene network are crucial in shaping the dynamics of pathogen contagion, while their roles for the pathogen diversity still lacking a theoretical study. To this end, we propose a reaction-diffusion process of pathogens with mutations on meta-population networks, which includes population movement and strain mutation. We extend the Microscopic Markov Chain Approach (MMCA) to describe the model. Traffic networks make pathogen diversity more likely to occur in cities with lower infection densities. The likelihood of pathogen diversity is low in cities with short effective distances in the traffic network. Star-type gene network is more likely to lead to pathogen diversity than lattice-type and chain-type gene networks. When pathogen localization is present, infection is localized to strains that are at the endpoints of the gene network. Both the increased probability of movement and mutation promote pathogen diversity. The results also show that the population tends to move to cities with short effective distances, resulting in the infection density is high.

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model

In this work, we propose a new mathematical modeling of the spread of COVID-19 infection in an arbitrary population, by modifying the SIQRD model as m-SIQRD model, while taking into consideration the eight governmental interventions such as cancellation of events, closure of public places etc., as well as the influence of the asymptomatic cases on the states of the model. We introduce robustness and improved accuracy in predictions of these models by utilizing a novel deep learning scheme. This scheme comprises of attention based architecture, alongside with Generative Adversarial Network (GAN) based data augmentation, for robust estimation of time varying parameters of m-SIQRD model. In this regard, we also utilized a novel feature extraction methodology by employing noise removal operation by Spline interpolation and Savitzky-Golay filter, followed by Principal Component Analysis (PCA). These parameters are later directed towards two main tasks: forecasting of states to the next 15 days, and estimation of best policy encodings to control the infected and deceased number within the framework of data driven synergetic control theory. We validated the superiority of the forecasting performance of the proposed scheme over countries of South Korea and Germany and compared this performance with 7 benchmark forecasting models. We also showed the potential of this scheme to determine best policy encodings in South Korea for 15 day forecast horizon.

Strain-stream model of epidemic spread in application to COVID-19

ABSTRACT

The recently developed model of the epidemic spread of two virus strains in a closed population is generalized to the situation typical for the couple of strains delta and omicron, when there is a high probability of omicron infection soon enough after recovering from delta infection. This model can be considered as a kind of combination of SIR and SIS models for the case of competition of two strains of the same virus with different contagiousness in a population. The obtained equations and results can be directly implemented for practical calculations of the replacement of strains of the SARS-CoV-2 virus. A comparison between the estimated replacement time and the corresponding statistics shows reasonable agreement.