Estimation of the reproduction number of influenza A(H1N1)pdm09 in South Korea using heterogeneous models

Abstract

Background The reproduction number is one of the most crucial parameters in determining disease dynamics, providing a summary measure of the transmission potential. However, estimating this value is particularly challenging owing to the characteristics of epidemic data, including non-reproducibility and incompleteness. Methods In this study, we propose mathematical models with different population structures; each of these models can produce data on the number of cases of the influenza A(H1N1)pdm09 epidemic in South Korea. These structured models incorporating the heterogeneity of age and region are used to estimate the reproduction numbers at various terminal times. Subsequently, the age- and region-specific reproduction numbers are also computed to analyze the differences illustrated in the incidence data. Results Incorporation of the age-structure or region-structure allows for robust estimation of parameters, while the basic SIR model provides estimated values beyond the reasonable range with severe fluctuation. The estimated duration of infectious period using age-structured model is around 3.8 and the reproduction number was estimated to be 1.6. The estimated duration of infectious period using region-structured model is around 2.1 and the reproduction number was estimated to be 1.4. The estimated age- and region-specific reproduction numbers are consistent with cumulative incidence for corresponding groups. Conclusions Numerical results reveal that the introduction of heterogeneity into the population to represent the general characteristics of dynamics is essential for the robust estimation of parameters.

Analysis of meteorological factors influencing the incidence of influenza in Fujian Province based on a neural network model

“…There are also some problems with RNN itself: some information in the long-term memory of the RNN network will be covered up by the short-term memory. Gated Recurrent Unit (GRU) is a variant of RNN [10], which, together with LSTM, adopts the gate mechanism to solve the above problems, and GRU can be regarded as a simpli ed version of LSTM. Since GRU has fewer parameters, the convergence speed is faster, and the actual time is much less, which can greatly speed up the iterative process [21].…”

“…To meet the needs of time-series data analysis, the Recurrent Neural Network (RNN) was developed and in need to address the insensitivity of RNNs to long-term dependencies, Hochreiter S et al proposed and developed a long short-term memory (LSTM) unit [9]. Gated Recurrent Unit (GRU) is another variant of RNN [10], which uses a gate mechanism with LSTM to solve the above problems. Speci cally, GRU can be regarded as a simpli ed version of LSTM.…”

Clinical Features of COVID-19 and Differentiation from Other Causes of CAP

“…A prominent unique feature of COVID-19 66,71 Reported but appears less frequent and less prominent 68,69 Fever Frequently present 80,81 Frequently present 82 Respiratory symptoms Frequently present 80 Frequently present 82 Symptom duration Appears slower and longer 86,90 Shorter on average when compared with COVID-19 86,92 Vitals Sometimes demonstrates hypoxia without significant dyspnea 64,98 No significant differences in vital signs compared with COVID-19, cannot differentiate based on signs alone 84 Laboratory values Lymphopenia 81 Elevation in inflammatory markers 109 Leukocytosis 65 Elevation in inflammatory markers 114 BAL More lymphocytes and fewer neutrophils 138 Fewer monocytes, CD4þ T-cells, and CD8þ T-cells 138 Coinfection Variable rates reported [140][141][142][143] Variable rates reported 145,146 Thrombotic complications Variable rates of thrombosis reported 147,148 Variable rates of thrombosis reported 150,151 Imaging findings May have more peripheral infiltrates 153 May have more pneumothorax and pneumomediastinum 166 More focal lobar consolidation 157 More concurrent pleural effusions 156 median R 0 of 5. 11 The efficiency of SARS-CoV-2 transmission is partially attributed to the relatively long incubation period of the virus prior to symptom onset. Peak infectivity generally occurs 2 days before symptoms appear.…”

“…The R 0 for influenza is 0.9 to 1.2, while the R 0 for SARS-CoV-2 ranges from 1.5 to 6 with a median R 0 of 5. 11 The efficiency of SARS-CoV-2 transmission is partially attributed to the relatively long incubation period of the virus prior to symptom onset. Peak infectivity generally occurs 2 days before symptoms appear.…”

Response: Commentary: Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread

“…It is solely their imaginary creation. We have mentioned in the section that, R 0 is measured through the effective reproductive rate, denoted by R. Thus, we have mentioned, "effective reproductive number" R as "effective reproductive rate" since it depends on time (32)(33)(34)(35)(36)(37)(38). Although for minimizing the ambiguity, the use of consistent terminology throughout the literature is required, and, therefore, we appreciate the commentators.…”