Effectiveness of Intervention Strategies on MERS-CoV Transmission Dynamics in South Korea, 2015: Simulations on the Network Based on the Real-World Contact Data

Mucosal immunity in upper and lower respiratory tract to MERS-CoV

Introduction

Middle East respiratory syndrome coronavirus (MERS-CoV) has emerged as a deadly pathogen with a mortality rate of up to 36.2%. MERS-CoV can cause severe respiratory tract disease and multiorgan failure. Therefore, therapeutic vaccines are urgently needed. This intensive review explores the human immune responses and their immunological mechanisms during MERS-CoV infection in the mucosa of the upper and lower respiratory tracts (URT and LRT, respectively).

Objective

The aim of this study is to provide a valuable, informative, and critical summary of the protective immune mechanisms against MERS-CoV infection in the URT/LRT for the purpose of preventing and controlling MERS-CoV disease and designing effective therapeutic vaccines.

Methods

In this review, we focus on the immune potential of the respiratory tract following MERS-CoV infection. We searched PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar using the following terms: "MERS-CoV", "B cells", "T cells", "cytokines", "chemokines", "cytotoxic", and "upper and lower respiratory tracts".

Results

We found and included 152 studies in this review. We report that the cellular innate immune response, including macrophages, dendritic cells, and natural killer cells, produces antiviral substances such as interferons and interleukins to prevent the virus from spreading. In the adaptive and humoral immune responses, CD4+ helper T cells, CD8+ cytotoxic T cells, B cells, and plasma cells protect against MERS-CoV infection in URT and LRT.

Conclusion

The human nasopharynx-associated lymphoid tissue (NALT) and bronchus-associated lymphoid tissue (BALT) could successfully limit the spread of several respiratory pathogens. However, in the case of MERS-CoV infection, limited research has been conducted in humans with regard to immunopathogenesis and mucosal immune responses due to the lack of relevant tissues. A better understanding of the immune mechanisms of the URT and LRT is vital for the design and development of effective MERS-CoV vaccines.

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data

Introduction

Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach.

Methods

The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation).

Results

Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods.

Discussion

This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

Heterogeneity is a key factor describing the initial outbreak of COVID-19

Assessing the transmission potential of emerging infectious diseases, such as COVID-19, is crucial for implementing prompt and effective intervention policies. The basic reproduction number is widely used to measure the severity of the early stages of disease outbreaks. The basic reproduction number of standard ordinary differential equation models is computed for homogeneous contact patterns; however, realistic contact patterns are far from homogeneous, specifically during the early stages of disease transmission. Heterogeneity of contact patterns can lead to superspreading events that show a significantly high level of heterogeneity in generating secondary infections. This is primarily due to the large variance in the contact patterns of complex human behaviours. Hence, in this work, we investigate the impacts of heterogeneity in contact patterns on the basic reproduction number by developing two distinct model frameworks: 1) an SEIR-Erlang ordinary differential equation model and 2) an SEIR stochastic agent-based model. Furthermore, we estimated the transmission probability of both models in the context of COVID-19 in South Korea. Our results highlighted the importance of heterogeneity in contact patterns and indicated that there should be more information than one quantity (the basic reproduction number as the mean quantity), such as a degree-specific basic reproduction number in the distributional sense when the contact pattern is highly heterogeneous.

Discovering spatiotemporal patterns of COVID-19 pandemic in South Korea

A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial-temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial-temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.