A spatial vaccination strategy to reduce the risk of vaccine-resistant variants

Overcoming Health Inequities: Spatial Analysis of Seroprevalence and Vaccination Against COVID-19 in Chile

Background

In unequal economies, the spread of the first waves of the COVID-19 was usually associated with low socioeconomic status of individuals and their families. Chile exemplified this. By mid-2020, Chile had one of the highest SARS-CoV-2 infection rates in the world predominantly in poorer areas. A year later, the country launched a universal vaccination campaign based on the national strategy of immunization established in 1975. By 2022, Chile presented one of the highest COVID-19 vaccination coverages globally, reaching 94.3% of the population with the primary scheme by the end of 2022.

Objective

This study analyzes the spatial distribution of SARS-CoV-2 seroprevalence at the beginning of the pandemic (2020) compared with the seroprevalence after 2 years of ongoing epidemic and COVID-19 vaccination campaigns (2022).

Methods

Two population-based random samples of individuals aged 7 years and older from two Chilean cities were studied. Utilizing an enzyme-linked immunosorbent assay test, IgG antibodies were measured in serum of 1061 participants in 2020, and 853 in 2022.

Results

Using the Global Moran's Index, the seroprevalence distribution pattern for the year 2020 showed clustering in the two cities. Conversely, seroprevalence and vaccinations were homogeneously distributed in 2022. These results show the success of the vaccination campaign in Chile, not only in coverage but also because it widely reached all individuals.

Conclusions

The uptake of this preventive measure is high, regardless of the social and economic factors, achieving broad population immunity. The extensive deployment of the primary health care network contributed to reducing health inequities and promoting to universal health access.

Assessing the impacts of vaccination and viral evolution in contact networks

A key lesson learned with COVID-19 is that public health measures were very different from country to country. In this study, we provide an analysis of epidemic dynamics using three well-known stochastic network models-small-world networks (Watts-Strogatz), random networks (Erdös-Rényi), and scale-free networks (Barabási-Albert)-to assess the impact of different viral strains, lockdown strategies, and vaccination campaigns. We highlight the significant role of highly connected nodes in the spread of infections, particularly within Barabási-Albert networks. These networks experienced earlier and higher peaks in infection rates, but ultimately had the lowest total number of infections, indicating their rapid transmission dynamics. We also found that intermittent lockdown strategies, particularly those with 7-day intervals, effectively reduce the total number of infections, serving as viable alternatives to prolonged continuous lockdowns. When simulating vaccination campaigns, we observed a bimodal distribution leading to two distinct outcomes: pandemic contraction and pandemic expansion. For WS and ER networks, rapid mass vaccination campaigns significantly reduced infection rates compared to slower campaigns; however, for BA networks, differences between vaccination strategies were minimal. To account for the evolution of a virus into a more transmissible strain, we modeled vaccination scenarios that varied vaccine efficacy against the wild-type virus and noted a decline in this efficacy over time against a second variant. Our results showed that vaccination coverage above 40% significantly flattened infection peaks for the wild-type virus, while at least 80% coverage was required to similarly reduce peaks for variant 2. Furthermore, the effect of vaccine efficacy on reducing the peak of variant 2 infection was minimal. Although vaccination strategies targeting hub nodes in scale-free networks did not substantially reduce the total number of infections, they were effective in increasing the probability of preventing pandemic outbreaks. These findings underscore the need to consider the network structure for effective pandemic control.

Targeting epidermal growth factor receptor signalling pathway: A promising therapeutic option for COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously producing new variants, necessitating effective therapeutics. Patients are not only confronted by the immediate symptoms of infection but also by the long-term health issues linked to long COVID-19. Activation of epidermal growth factor receptor (EGFR) signalling during SARS-CoV-2 infection promotes virus propagation, mucus hyperproduction, and pulmonary fibrosis, and suppresses the host's antiviral response. Over the long term, EGFR activation in COVID-19, particularly in COVID-19-induced pulmonary fibrosis, may be linked to the development of lung cancer. In this review, we have summarised the significance of EGFR signalling in the context of SARS-CoV-2 infection. We also discussed the targeting of EGFR signalling as a promising strategy for COVID-19 treatment and highlighted erlotinib as a superior option among EGFR inhibitors. Erlotinib effectively blocks EGFR and AAK1, thereby preventing SARS-CoV-2 replication, reducing mucus hyperproduction, TNF-α expression, and enhancing the host's antiviral response. Nevertheless, to evaluate the antiviral efficacy of erlotinib, relevant clinical trials involving an appropriate patient population should be designed.

Correction: A spatial vaccination strategy to reduce the risk of vaccine-resistant variants

[This corrects the article DOI: 10.1371/journal.pcbi.1010391.].

Time series analysis of daily reported number of new positive cases of COVID-19 in Japan from January 2020 to February 2023

This study investigated temporal variations of the COVID-19 pandemic in Japan using a time series analysis incorporating maximum entropy method (MEM) spectral analysis, which produces power spectral densities (PSDs). This method was applied to daily data of COVID-19 cases in Japan from January 2020 to February 2023. The analyses confirmed that the PSDs for data in both the pre- and post-Tokyo Olympics periods show exponential characteristics, which are universally observed in PSDs for time series generated from nonlinear dynamical systems, including the so-called susceptible/exposed/infectious/recovered (SEIR) model, well-established as a mathematical model of temporal variations of infectious disease outbreaks. The magnitude of the gradient of exponential PSD for the pre-Olympics period was smaller than that of the post-Olympics period, because of the relatively high complex variations of the data in the pre-Olympics period caused by a deterministic, nonlinear dynamical system and/or undeterministic noise. A 3-dimensional spectral array obtained by segment time series analysis indicates that temporal changes in the periodic structures of the COVID-19 data are already observable before the commencement of the Tokyo Olympics and immediately after the introduction of mass and workplace vaccination programs. Additionally, the possibility of applying theoretical studies for measles control programs to COVID-19 is discussed.

Evolutionary safety of lethal mutagenesis driven by antiviral treatment

Nucleoside analogs are a major class of antiviral drugs. Some act by increasing the viral mutation rate causing lethal mutagenesis of the virus. Their mutagenic capacity, however, may lead to an evolutionary safety concern. We define evolutionary safety as a probabilistic assurance that the treatment will not generate an increased number of mutants. We develop a mathematical framework to estimate the total mutant load produced with and without mutagenic treatment. We predict rates of appearance of such virus mutants as a function of the timing of treatment and the immune competence of patients, employing realistic assumptions about the vulnerability of the viral genome and its potential to generate viable mutants. We focus on the case study of Molnupiravir, which is an FDA-approved treatment against Coronavirus Disease-2019 (COVID-19). We estimate that Molnupiravir is narrowly evolutionarily safe, subject to the current estimate of parameters. Evolutionary safety can be improved by restricting treatment with this drug to individuals with a low immunological clearance rate and, in future, by designing treatments that lead to a greater increase in mutation rate. We report a simple mathematical rule to determine the fold increase in mutation rate required to obtain evolutionary safety that is also applicable to other pathogen-treatment combinations.

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.