ABSTRACT
OBJECTIVES@#Amid the spread of coronavirus disease 2019 (COVID-19), with its high infectivity, we have relied on mathematical models to predict the temporal evolution of the disease. This paper aims to show that, due to active behavioral changes of individuals and the inherent nature of infectious diseases, it is complicated and challenging to predict the temporal evolution of epidemics. @*METHODS@#A modified susceptible-exposed-infectious-hospitalized-removed (SEIHR) compartment model with a discrete feedback-controlled transmission rate was proposed to incorporate individuals’ behavioral changes into the model. To figure out relative uncertainties in the infection peak time and the fraction of the infected population at the peak, a deterministic method and 2 stochastic methods were applied. @*RESULTS@#A relatively small behavioral change of individuals with a feedback constant of 0.02 in the modified SEIHR model resulted in a peak time delay of up to 50% using the deterministic method. Incorporating stochastic methods into the modified model with a feedback constant of 0.04 suggested that the relative random uncertainty of the maximum fraction of infections and that of the peak time for a population of 1 million reached 29% and 9%, respectively. Even without feedback, the relative uncertainty of the peak time increased by up to 20% for a population of 100,000. @*CONCLUSIONS@#It is shown that uncertainty originates from stochastic properties of infections. Without a proper selection of the evolution scenario, active behavioral changes of individuals could serve as an additional source of uncertainty.
ABSTRACT
OBJECTIVES@#Amid the spread of coronavirus disease 2019 (COVID-19), with its high infectivity, we have relied on mathematical models to predict the temporal evolution of the disease. This paper aims to show that, due to active behavioral changes of individuals and the inherent nature of infectious diseases, it is complicated and challenging to predict the temporal evolution of epidemics. @*METHODS@#A modified susceptible-exposed-infectious-hospitalized-removed (SEIHR) compartment model with a discrete feedback-controlled transmission rate was proposed to incorporate individuals’ behavioral changes into the model. To figure out relative uncertainties in the infection peak time and the fraction of the infected population at the peak, a deterministic method and 2 stochastic methods were applied. @*RESULTS@#A relatively small behavioral change of individuals with a feedback constant of 0.02 in the modified SEIHR model resulted in a peak time delay of up to 50% using the deterministic method. Incorporating stochastic methods into the modified model with a feedback constant of 0.04 suggested that the relative random uncertainty of the maximum fraction of infections and that of the peak time for a population of 1 million reached 29% and 9%, respectively. Even without feedback, the relative uncertainty of the peak time increased by up to 20% for a population of 100,000. @*CONCLUSIONS@#It is shown that uncertainty originates from stochastic properties of infections. Without a proper selection of the evolution scenario, active behavioral changes of individuals could serve as an additional source of uncertainty.
ABSTRACT
PURPOSE: The clinical significance of Y. pseudotuberculosis infection has recently recognizd in various part of the world, because it can cause a wide range of clinical problems such as mesenteric lymphadenitis, septicemia, reactive arthritis, terminal ileitis, erythema nodosum, and a cute renal failure. We have experienced 19 children with Y. pseudotuberculosis infection confirmed by stool culture. Our aim in this study was to evaluate clinical charactieristics, age and sex distribution, and source of infection. METHODS: Stools were inoculated on CIN(Cefsulodin-Irgasan-Novobiosin) agar (Difco, USA) and incubated for 48hr at 22 degrees C for isolation of Y. pseudotuberculosis. API 20E and VITEC were used for identification of the isolates. The antimicrobial sensitivity tests were performed by GN S(gram negative sensitive) card. Clinical characteristics were analyzed retrospectively. RESULTS: Retrospective analysis of 19 children with Y. pseudotuberculosis infection who visited our hospital between Jun.1993 and Dec.1993 was performed. The most prevalent age group was 6 to 8 years(42%) and monthly distribution showed November, December, June, and July in order of frequency, respectively. The common symptoms and signs were fever(100%), abdominal pain(100%), rash(74%), s trawberry tongue(53%), vomiting(53%), diarrhea(37%), and desquamation(32%), respectively. Four cases among 9 cases showed multiple mesenteric lymph node enlargements on the abdominal ultrasonogaphy. Serogroups of the isolates from stool specimens were type 5(15/19, 79%), and type 4(4/19, 21%), respectively. Y. pseudotuberculosis was also isolated from 3 samples of untreated drinking water which was thought to be the source of infection. There were no resistance strains against Amikacin, Carbenidlin, Gentamicin, and Trimethoprim/Sulfamethoxazole in the antibiotic susceptibility tests. CONCLUSIONS: In this study, the antibiotic susceptibility against Y. pseudotuberculosis was excellent, although the clinical characteristics were various. We have found that untreated drinking water was an important source of this infection. Further epidemiologic study for this infection should be needed.