Global transmission of COVID-19 — A gravity model approach

This paper aims to describe the spatiotemporal transmission of COVID-19 and examine how various factors influence the global spread of COVID-19 using a modified gravity model. Log-linearizing the model, we run a negative binomial regression with observational data from 22 January 2020 to 31 December 2020. In the first model, population size and GDP per capita are positively related to the sum of newly confirmed COVID-19 cases within a 10-day window;the values for both variables are statistically significant throughout the study period. However, the significance of geographic distance varies. When a single geographic source exits in the early stage, the value is statistically significant. In the intermediate stage, when disease transmission is explosive between countries, the distance loses its statistical significance due to the emergence of multiple geographic transmission sources. In the containment stage, when the spread of disease is more likely to occur within a country, distance becomes statistically significant. According to the second model, the government's internal movement control and nonpharmaceutical intervention policy, percentage of the population over 70 years old, and population-weighted density are statistically significant and are positively related to the incidence of COVID-19. By contrast, average monthly temperature, international travel restriction policies, and political regimes are statistically significant and negatively associated with the dependent variable. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Modern Physics C: Computational Physics & Physical Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation.

OBJECTIVE: In 2020, the coronavirus disease 2019 (COVID-19) respiratory infection is spreading in Korea. In order to prevent the spread of an infectious disease, infected people must be quickly identified and isolated, and contact with the infected must be blocked early. This study attempted to verify the intervention effects on the spread of an infectious disease by using these measures in a mathematical model. METHODS: We used the susceptible-infectious-recovery (SIR) model for a virtual population group connected by a special structured network. In the model, the infected state (I) was divided into I in which the infection is undetected and Ix in which the infection is detected. The probability of transitioning from an I state to Ix can be viewed as the rate at which an infected person is found. We assumed that only those connected to each other in the network can cause infection. In addition, this study attempted to evaluate the effects of isolation by temporarily removing the connection among these people. RESULTS: In Scenario 1, only the infected are isolated; in Scenario 2, those who are connected to an infected person and are also found to be infected are isolated as well. In Scenario 3, everyone connected to an infected person are isolated. In Scenario 3, it was possible to effectively suppress the infectious disease even with a relatively slow rate of diagnosis and relatively high infection rate. CONCLUSION: During the epidemic, quick identification of the infected is helpful. In addition, it was possible to quantitatively show through a simulation evaluation that the management of infected individuals as well as those who are connected greatly helped to suppress the spread of infectious diseases.

Evidence of the importance of contact tracing in fighting COVID-19.

OBJECTIVES: We analyzed data to determine whether there are distinguishing characteristics depending on the success or failure of control for coronavirus disease 2019 (COVID-19) by country in the trend of the daily number of confirmed cases and the number of tests. METHODS: We obtained the number of confirmed cases and tests per day for almost every country in the world from Our World in Data. The Pearson correlation between the two time series was calculated according to the time delay to analyze the relationship between the number of tests and the number of cases with a lag. RESULTS: For each country, we obtained the time lag that makes the maximum correlation between the number of confirmed cases and the number of tests for COVID-19. It can be seen that countries whose time lag making maximum correlation lies in a special section between about 15 days and 20 days are generally been successful in controlling COVID-19. That section looks like a trench on the battlefield. CONCLUSIONS: We have seen the possibility that the success in mitigating COVID-19 can be expressed as a simple indicator of the time lag of the correlation between confirmed cases and tests. This time lag indicator is presumably reflected by efforts to actively trace the infected persons.

Clustering and link prediction for mesoscopic COVID-19 transmission networks in Republic of Korea.

We analyze the dataset of confirmed cases of severe acute respiratory syndrome coronavirus 2 (COVID-19) in the Republic of Korea, which contains transmission information on who infected whom as well as temporal information regarding when the infection possibly occurred. We derive time series of mesoscopic transmission networks using the location and age of each individual in the dataset to see how the structure of these networks changes over time in terms of clustering and link prediction. We find that the networks are clustered to a large extent, while those without weak links could be seen as having a tree structure. It is also found that triad-based link predictability using the network structure could be improved when combined with additional information on mobility and age-stratified contact patterns. Abundant triangles in the networks can help us better understand mixing patterns of people with different locations and age groups, hence the spreading dynamics of infectious disease.

Early detection of influenza outbreak using time derivative of incidence.

For mitigation strategies of an influenza outbreak, it can be helpful to understand the characteristics of regional and age-group-specific spread. In South Korea, however, there has been no official statistic related to it. In this study, we extract the time series of influenza incidence from National Health Insurance Service claims database, which consists of all medical and prescription drug-claim records for all South Korean population. The extracted time series contains the number of new patients by region (250 city-county-districts) and age-group (0-4, 5-19, 20-64, 65+) within a week. The number of cases of influenza (2009-2017) is 12,282,356. For computing an onset of influenza outbreak by region and age-group, we propose a novel method for early outbreak detection, in which the onset of outbreak is detected as a sudden change in the time derivative of incidence. The advantage of it over the cumulative sum and the exponentially weighted moving average control charts, which have been widely used for the early outbreak detection of infectious diseases, is that information on the previous non-epidemic periods are not necessary. Then, we show that the metro area and 5-19 age-group are earlier than the rural area and other age-groups for the start of the influenza outbreak. Also, the metro area and 5-19 age-group peak earlier than the rural area and other age-groups. These results would be helpful to design a surveillance system for timely early warning of an influenza outbreak in South Korea.