Systematic Comparison of Parameter Estimation Approaches Using the Generalized-growth Model for Prediction of Epidemic Outbreaks

Background- Many different mathematical models are used to assess and predict the outbreaks. The model is selected by the characteristics of the outbreaks. Here, we utilize the generalized growth model (GGM), one of the simplest mathematical models, with the real outbreaks to compare two parameter estimation methods. Materials and Methods- 25 outbreaks are used to analyze. We use GGM with the ascending phase of each outbreak and estimated the r and p parameters with both the least square (LSQ) and maximum likelihood estimation (MLE) methods. For both parameter estimation methods, we conduct the parametric bootstrap method to construct the confidence interval of parameters. We compare the two estimation methods by the RMSE, Anscombe residual, and prediction coverage. Results- The result shows that most outbreaks have similar r and p parameters, RMSE, Anscombe, and prediction coverage for LSQ and MLE. Although Anscombe values for LSQ are higher than the values for MLE, the difference between results of the two methods are minimal for the most outbreaks. Conclusion- The study is shown that LSQ and MLE do not result in different values of the parameter estimation, RMSE, Anscombe, and prediction coverage with GGM

Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Potential, Iran, 2020

To determine the transmission potential of severe acute respiratory syndrome coronavirus 2 in Iran in 2020, we estimated the reproduction number as 4.4 (95% CI 3.9–4.9) by using a generalized growth model and 3.5 (95% CI 1.3–8.1) by using epidemic doubling time. The reproduction number decreased to 1.55 after social distancing interventions were implemented

Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020

The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic’s epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65–81 cases (upper bounds: 169–507) in Guangdong and an additional 44–354 (upper bounds: 141–875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down

Optical Images and Source Catalog of AKARI North Ecliptic Pole Wide Survey Field

We present the source catalog and the properties of the $B-, R-$, and $I-$band images obtained to support the {\it AKARI} North Ecliptic Pole Wide (NEP-Wide) survey. The NEP-Wide is an {\it AKARI} infrared imaging survey of the north ecliptic pole covering a 5.8 deg$^2$ area over 2.5 -- 6 \micron wavelengths. The optical imaging data were obtained at the Maidanak Observatory in Uzbekistan using the Seoul National University 4k $\times$ 4k Camera on the 1.5m telescope. These images cover 4.9 deg$^2$ where no deep optical imaging data are available. Our $B-, R-$, and $I-$band data reach the depths of $\sim$23.4, $\sim$23.1, and $\sim$22.3 mag (AB) at 5$\sigma$, respectively. The source catalog contains 96,460 objects in the $R-$band, and the astrometric accuracy is about 0.15\arcsec at 1$\sigma$ in each RA and Dec direction. These photometric data will be useful for many studies including identification of optical counterparts of the infrared sources detected by {\it AKARI}, analysis of their spectral energy distributions from optical through infrared, and the selection of interesting objects to understand the obscured galaxy evolution.Comment: 39 pages, 12 figure