Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend.

BACKGROUND: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. METHODS: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. RESULTS: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. CONCLUSION: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

Biomechanical Loading Evaluation of Unsintered Hydroxyapatite/poly-l-lactide Plate System in Bilateral Sagittal Split Ramus Osteotomy.

OSTEOTRANS MX® (Takiron Co., Ltd., Osaka, Japan) is a bioactive resorbable maxillofacial osteosynthetic material composed of an unsintered hydroxyapatite/poly-l-lactide composite, and its effective osteoconductive capacity has been previously documented. However, the mechanical strength of this plate system is unclear. Thus, the aim of this in vitro study was to assess its tensile and shear strength and evaluate the biomechanical intensity of different osteosynthesis plate designs after sagittal split ramus osteotomy by simulating masticatory forces in a clinical setting. For tensile and shear strength analyses, three mechanical strength measurement samples were prepared by fixing unsintered hydroxyapatite/poly-l-lactide composed plates to polycarbonate skeletal models. Regarding biomechanical loading evaluation, 12 mandibular replicas were used and divided into four groups for sagittal split ramus osteotomy fixation. Each sample was secured in a jig and subjected to vertical load on the first molar teeth. Regarding shear strength, the novel-shaped unsintered hydroxyapatite/poly-l-lactide plate had significantly high intensity. Upon biomechanical loading evaluation, this plate system also displayed significantly high stability in addition to bioactivity, with no observed plate fracture. Thus, we have clearly demonstrated the efficacy of this plate system using an in vitro model of bilateral sagittal split ramus osteotomy of the mandible.