ABSTRACT
Under the current African swine fever (ASF) epidemic situation, a science-based ASF-control strategy is required. An ASF transmission mechanistic model can be used to understand the disease transmission dynamics among susceptible epidemiological units and evaluate the effectiveness of an ASF-control strategy by simulating disease spread results with different control options. The force of infection, which is the probability that a susceptible epidemiological unit becomes infected, could be estimated by applying an ASF transmission mechanistic model. The government needs to plan an ASF-control strategy based on an ASF transmission mechanistic model.
ABSTRACT
South Korea is experiencing the community transmission of the SARS-CoV-2 Omicron variant (B.1.1.529). We estimated that the mean ({+/-} standard deviation) serial interval was 2.22 ({+/-} 1.62) days, and the basic reproduction number was 1.90 (95% Credible Interval, 1.50-2.43) for the Omicron variant outbreak in South Korea.
ABSTRACT
We estimated mean serial interval and superspreading potential for the predominant Delta variant of SARS-CoV-2. Mean serial intervals were similar with 3.7 and 3.5 days during early and latter periods, respectively. Furthermore, the risk of superspreading events was similar with 23% and 25% of cases seeded 80% of all transmissions.
ABSTRACT
In order to identify the temporal change in the possible risk of superspreading events (SSE), we estimated the overdispersion parameter in two different periods of COVID-19 pandemic. We identified the possible risk of SSE was reduced 34% during the second epidemic period in South Korea.
ABSTRACT
Background@#African swine fever (ASF) is a hemorrhagic fever occurring in wild boars (Sus scrofa) and domestic pigs. The epidemic situation of ASF in South Korean wild boars has increased the risk of ASF in domestic pig farms. Although basic reproduction number (R0) can be applied for control policies, it is challenging to estimate the R0 for ASF in wild boars due to surveillance bias, lack of wild boar population data, and the effect of ASF-positive wild boar carcass on disease dynamics. @*Objectives@#This study was undertaken to estimate the R0 of ASF in wild boars in South Korea, and subsequently analyze the spatiotemporal heterogeneity. @*Methods@#We detected the local transmission clusters using the spatiotemporal clustering algorithm, which was modified to incorporate the effect of ASF-positive wild boar carcass. With the assumption of exponential growth, R0 was estimated for each cluster. The temporal change of the estimates and its association with the habitat suitability of wild boar were analyzed. @*Results@#Totally, 22 local transmission clusters were detected, showing seasonal patterns occurring in winter and spring. Mean value of R0 of each cluster was 1.54. The estimates showed a temporal increasing trend and positive association with habitat suitability of wild boar. @*Conclusions@#The disease dynamics among wild boars seems to have worsened over time. Thus, in areas with a high elevation and suitable for wild boars, practical methods need to be contrived to ratify the control policies for wild boars.
ABSTRACT
Background@#African swine fever (ASF) is a hemorrhagic fever occurring in wild boars (Sus scrofa) and domestic pigs. The epidemic situation of ASF in South Korean wild boars has increased the risk of ASF in domestic pig farms. Although basic reproduction number (R0) can be applied for control policies, it is challenging to estimate the R0 for ASF in wild boars due to surveillance bias, lack of wild boar population data, and the effect of ASF-positive wild boar carcass on disease dynamics. @*Objectives@#This study was undertaken to estimate the R0 of ASF in wild boars in South Korea, and subsequently analyze the spatiotemporal heterogeneity. @*Methods@#We detected the local transmission clusters using the spatiotemporal clustering algorithm, which was modified to incorporate the effect of ASF-positive wild boar carcass. With the assumption of exponential growth, R0 was estimated for each cluster. The temporal change of the estimates and its association with the habitat suitability of wild boar were analyzed. @*Results@#Totally, 22 local transmission clusters were detected, showing seasonal patterns occurring in winter and spring. Mean value of R0 of each cluster was 1.54. The estimates showed a temporal increasing trend and positive association with habitat suitability of wild boar. @*Conclusions@#The disease dynamics among wild boars seems to have worsened over time. Thus, in areas with a high elevation and suitable for wild boars, practical methods need to be contrived to ratify the control policies for wild boars.
ABSTRACT
BackgroundIn March 2020, overall, 37,000 international students from the country at risk of the 2019-novel coronavirus (COVID-19) infection will arrive in Seoul, South Korea. Individuals from the country at risk of COVID-19 infection have been included in a home-quarantine program, but the efficacy of the program is uncertain. MethodsTo estimate the possible number of infected individuals within the large influx of international students, we used a deterministic compartmental model for epidemic and perform a simulation-based search of different rates of compliance with home-quarantine. ResultsUnder the home-quarantine program, the total number of the infected individuals would reach 24-53 from March 17-March 20, 50-86 from March 18- March 16, and 234- 343 from March 4- March 23 with the arrival of 0.1%, 0.2%, and 1% of pre-infectious individuals, in Seoul, South Korea, respectively. Our findings indicated when incoming international students showed strict compliance with quarantine, epidemics were less likely to occur in Seoul, South Korea. ConclusionTo mitigate possible epidemics, additional efforts to improve the compliance of home-quarantine are warranted along with other containment policies.
ABSTRACT
In epidemiology, the basic reproduction number (R0) is a term that describes the expected number of infections generated by 1 case in a susceptible population. At the beginning of the coronavirus disease 2019 (COVID-19) pandemic, R0 was frequently referenced by the public health community and the wider public. However, this metric is often misused or misinterpreted. Moreover, the complexity of the process of estimating R0 has caused difficulties for a substantial number of researchers. In this article, in order to increase the accessibility of this concept, we address several misconceptions related to the threshold characteristics of R0 and the effective reproduction number (Rt). Moreover, the appropriate interpretation of the metrics is discussed. R0 should be considered as a population-averaged value that pools the contact structure according to a stochastic transmission process. Furthermore, it is necessary to understand the unavoidable time lag for Rt due to the incubation period of the disease.
ABSTRACT
In epidemiology, the basic reproduction number (R0) is a term that describes the expected number of infections generated by 1 case in a susceptible population. At the beginning of the coronavirus disease 2019 (COVID-19) pandemic, R0 was frequently referenced by the public health community and the wider public. However, this metric is often misused or misinterpreted. Moreover, the complexity of the process of estimating R0 has caused difficulties for a substantial number of researchers. In this article, in order to increase the accessibility of this concept, we address several misconceptions related to the threshold characteristics of R0 and the effective reproduction number (Rt). Moreover, the appropriate interpretation of the metrics is discussed. R0 should be considered as a population-averaged value that pools the contact structure according to a stochastic transmission process. Furthermore, it is necessary to understand the unavoidable time lag for Rt due to the incubation period of the disease.
ABSTRACT
Antimicrobial resistance and emerging infectious diseases, including avian influenza, Ebola virus disease, and Zika virus disease have significantly affected humankind in recent years. In the premodern era, no distinction was made between animal and human medicine. However, as medical science developed, the gap between human and animal science grew deeper. Cooperation among human, animal, and environmental sciences to combat emerging public health threats has become an important issue under the One Health Initiative. Herein, we presented the history of One Health, reviewed current public health threats, and suggested opportunities for the field of public health through better understanding of the One Health paradigm.
