Estimation of the reproduction number of influenza A(H1N1)pdm09 in South Korea using heterogeneous models

Quantifying social contact dynamics in South Korea in the post-COVID-19 endemic phase: A cross-sectional survey for infectious disease transmission

BACKGROUND

Understanding the dynamics of social contact patterns is crucial for modeling the spread of infectious diseases. Although previous studies have examined them globally, how relevant they are to social patterns in the endemic phase is still unclear. South Korea's distinct cultural norms, aging population, and declining fertility highlight the critical need for new, context-specific data. Here, we aim to conduct a contact survey in South Korea, providing age and location-specific contact estimates and quantifying patterns related to infectious disease transmission.

METHODS

We conducted a cross-sectional survey in South Korea from July 6 to September 5, 2023. Participants of all ages were recruited using quota sampling based on the 2023 census, with only one individual per household included. Social contacts, both physical and non-physical, were recorded through a 24-hour diary. The data were analyzed with a negative binomial regression model. Age-specific contact matrices were constructed using census-based sampling weights, with uncertainty assessed through bootstrapping.

RESULTS

A total of 1493 participants (740 males and 753 females) reported 14,187 contacts, with an average of 9.2 daily contacts (95 % confidence interval [CI]: 8.7-9.8). Contacts peaked at 17.8 (95 % CI: 14.6-21.5) among children aged 5-9 and were lowest at 3.5 (95 % CI: 2.6-4.5) for those aged 75 and above. Children and teenagers had a higher rate of peer-group contact, with most contacts occurring in workplaces or educational institutions. The level of family contact increased across all age groups.

CONCLUSION

We identified distinct social contact patterns in South Korea, such as high interactions within peer groups, which suggest the need for school-based vaccination programs, family-centered strategies, and workplace infection control measures. These findings provide critical baseline data to enhance disease modeling and inform professional public health policy development in South Korea and other cultural contexts.

A region-structured model for early warning of Plasmodium vivax malaria transmission risk in the Republic of Korea

BACKGROUND

Malaria cases in the Republic of Korea decreased during the coronavirus disease 2019 pandemic but surged in 2023. Current models inadequately address spatial heterogeneity in transmission dynamics. This study aimed to address this by designing a region-structured model considering spatial heterogeneity based on regional malaria data from high-risk areas.

METHODS

Malaria-risk areas were identified using data from the Korea Disease Control and Prevention Agency (KDCA), with eight regions designated as hotspots. The population heterogeneity of the model by region was represented using the "Who Acquires Infection From Whom" matrix. The model was calibrated using 2014-2018 KDCA civilian malaria-case data. The reproduction number (Rt) of each region was then calculated using the estimated parameters and predicted malaria dynamics.

RESULTS

In the hotspots, the value of Rt rose along with the number of long-latency patients, followed by an increase in short-latency patients. The points where Rt exceeded and fell below one varied by region. Ganghwa-gun exhibited the longest period (Rt>1), whereas Deokyang-gu had the shortest. Maximum Rt values ranged from 1.1 in Deokyang-gu to 2.7 in Ganghwa-gun. A criterion was established to estimate the timing of Rt>1 based on the weekly cumulative incidence per 100,000 people.

CONCLUSION

This study constructed a region-structured model reflecting spatial heterogeneity using actual data. By estimating Rt and an easily accessible index for each region, the model provides an indicator that assists in implementing effective malaria management policies at the regional level.

Time scale theory on stability of explicit and implicit discrete epidemic models: applications to Swine flu outbreak

Time scales theory has been in use since the 1980s with many applications. Only very recently, it has been used to describe within-host and between-hosts dynamics of infectious diseases. In this study, we present explicit and implicit discrete epidemic models motivated by the time scales modeling approach. We use these models to formulate the basic reproduction number, which determines whether an outbreak occurs or the disease dies out. We discuss the stability of the disease-free and endemic equilibrium points using the linearization method and Lyapunov function. Furthermore, we apply our models to swine flu outbreak data to demonstrate that the discrete models can accurately describe the epidemic dynamics. Our comparison analysis shows that the implicit discrete model can best describe the data regardless of the data frequency. In addition, we perform the sensitivity analysis on the key parameters of the models to study how these parameters impact the basic reproduction number.

Clinical Features of COVID-19 and Differentiation from Other Causes of CAP

Community-acquired pneumonia (CAP) is a significant cause of morbidity and mortality, one of the most common reasons for infection-related death worldwide. Causes of CAP include numerous viral, bacterial, and fungal pathogens, though frequently no specific organism is found. Beginning in 2019, the COVID-19 pandemic has caused incredible morbidity and mortality. COVID-19 has many features typical of CAP such as fever, respiratory distress, and cough, and can be difficult to distinguish from other types of CAP. Here, we highlight unique clinical features of COVID-19 pneumonia such as olfactory and gustatory dysfunction, lymphopenia, and distinct imaging appearance.