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Objective Compared with other regions in the world, the transmission characteristics of the COVID-19 epidemic in Africa are more obvious, has a unique transmission mode in this region; At the same time, the data related to the COVID-19 epidemic in Africa is characterized by low data quality and incomplete data coverage, which makes the prediction method of COVID-19 epidemic suitable for other regions unable to achieve good results in Africa. In order to solve the above problems, this paper proposes a prediction method that nests the in-depth learning method in the mechanism model. From the experimental results, it can better solve the above problems and better adapt to the transmission characteristics of the COVID-19 epidemic in African countries. Methods Based on the SIRV model, the COVID-19 transmission rate and trend from September 2021 to January 2022 of the top 15 African countries (South Africa, Morocco, Tunisia, Libya, Egypt, Ethiopia, Kenya, Zambia, Algeria, Botswana, Nigeria, Zimbabwe, Mozambique, Uganda, and Ghana) in the accumulative number of COVID-19 confirmed cases was fitted by using the data from Worldometer. Non-autoregressive (NAR), Long-short term memory (LSTM), Autoregressive integrated moving average (ARIMA) models, Gaussian and polynomial functions were used to predict the transmission rate β in the next 7, 14, and 21 days. Then, the predicted transmission rate βs were substituted into the SIRV model to predict the number of the COVID-19 active cases. The error analysis was conducted using root-mean-square error (RMSE) and mean absolute percentage error (MAPE). Results The fitting curves of the 7, 14, and 21 days were consistent with and higher than the original curves of daily active cases (DAC). The MAPE between the fitted and original 7-day DAC was only 1.15% and increased with the longer of predict days. Both the predicted β and DAC of the next 7, 14, and 21 days by NAR and LSTM nested models were closer to the real ones than other three ones. The minimum RMSEs for the predicted number of COVID-19 active cases in the next 7, 14, and 21 days were 12,974, 14,152, and 12,211 people, respectively when the order of magnitude for was 10 6 , with the minimum MAPE being 1.79%, 1.97%, and 1.64%, respectively. Conclusion Nesting the SIRV model with NAR, LSTM, ARIMA methods etc. through functionalizing β respectively could obtain more accurate fitting and predicting results than these models/methods alone for the number of confirmed COVID-19 cases in Africa in which nesting with NAR had the highest accuracy for the 14-day and 21-day predictions. The nested model was of high significance for early understanding of the COVID-19 disease burden and preparedness for the response.

In the summer of 2024, COVID-19 positive cases spiked in many countries, but it is no longer a deadly pandemic thanks to global herd immunity to the SARS-CoV-2 viruses. In our physical chemistry lab in spring 2024, students practice kinetic models, SIR (Susceptible, Infected, and Recovered) and SIRV (Susceptible, Infected, Recovered, Vaccinated) using COVID-19 positive cases and vaccination data from World Health Organization (WHO). In this report, we further introduce virus breakthrough to the existing model updating it the SIRVB (Susceptible, Infectious, Recovered, Vaccinated, Breakthrough) model. We believe this is the simplest model possible to explain the COVID-19 kinetics/dynamics in all countries in the past four years. Parameters obtained from such practice correlate with many indices of different countries. These models and parameters have significant value to researchers and policymakers in predicting the stages of future outbreaks of infectious diseases.

This paper deals with the problem of estimating the parameters of heavy-tailed sea clutter in high-resolution radar, when the clutter is modeled by the correlated Pareto type II distribution. Existing estimators based on the maximum likelihood (ML) approach, integer-order moments (IOM) approach, fractional-order moments (FOM), and log-moments (log-MoM) have shown to be sensitive to changes in data correlation. In this work, we resort to a deep learning (DL) approach based on a multi-headed architecture to overcome this problem. Offline training of the artificial neural networks (ANN) is carried out by using several combinations of the clutter parameters, with different correlation degrees. To assess the performance of the proposed estimator, we resort to Monte Carlo simulation, and we observed that it has superior performance over existing approaches in terms of estimation mean square error (MSE) and robustness to changes of the clutter correlation coefficient.

Vaccination is an important tool in disease control to suppress disease, and vaccine-influenced diseases no longer conform to the general pattern of transmission. In this paper, by assuming that the infection rate is affected by the Ornstein–Uhlenbeck process, we obtained a stochastic SIRV model. First, we prove the existence and uniqueness of the global positive solution. Sufficient conditions for the extinction and persistence of the disease are then obtained. Next, by creating an appropriate Lyapunov function, the existence of the stationary distribution for the model is proved. Further, the explicit expression for the probability density function of the model around the quasi-equilibrium point is obtained. Finally, the analytical outcomes are examined by numerical simulations.

A small subset of acidic hot springs sampled in Yellowstone National Park yielded rod-shaped viruses which lysed liquid host cultures and formed clear plaques on lawns of host cells. Three isolates chosen for detailed analysis were found to be genetically related to previously described isolates of the Sulfolobus islandicus rod-shaped virus (SIRV), but distinct from them and from each other. Functional stability of the new isolates was assessed in a series of inactivation experiments. UV-C radiation inactivated one of the isolates somewhat faster than bacteriophage λ, suggesting that encapsidation in the SIRV-like virion did not confer unusual protection of the DNA from UV damage. With respect to high temperature, the new isolates were extremely, but not equally, stable. Several chemical treatments were found to inactivate the virions and, in some cases, to reveal apparent differences in virion stability among the isolates. Screening a larger set of isolates identified greater variation of these stability properties but found few correlations among the resulting profiles. The majority of host cells infected by the new isolates were killed, but survivors exhibited heritable resistance, which could not be attributed to CRISPR spacer acquisition or the loss of the pilus-related genes identified by earlier studies. Virus-resistant host variants arose at high frequency and most were resistant to multiple viral strains; conversely, resistant host clones generated virus-sensitive variants, also at high frequency. Virus-resistant cells lacked the ability of virus-sensitive cells to bind virions in liquid suspensions. Rapid interconversion of sensitive and resistant forms of a host strain suggests the operation of a yet-unidentified mechanism that acts to allow both the lytic virus and its host to propagate in highly localized natural populations, whereas variation of virion-stability phenotypes among the new viral isolates suggests that multiple molecular features contribute to the biological durability of these viruses.

The ongoing spread of coronavirus disease 2019 (COVID-19) in most South and Southeast Asian countries has led to severe health and economic impacts. Evaluating the performance of nonpharmaceutical interventions in reducing the number of daily new cases is essential for policy designs. Analysis of the growth rate of daily new cases indicates that the value (5.47%) decreased significantly after nonpharmaceutical interventions were adopted (1.85%). Vaccinations failed to significantly reduce the growth rates, which were 0.67% before vaccination and 2.44% and 2.05% after 14 and 28 d of vaccination, respectively. Stringent nonpharmaceutical interventions have been loosened after vaccination drives in most countries. To predict the spread of COVID-19 and clarify the implications to adjust nonpharmaceutical interventions, we build a susceptible–infected–recovered–vaccinated (SIRV) model with a nonpharmaceutical intervention module and Metropolis–Hastings sampling in three scenarios (optimistic, neutral, and pessimistic). The daily new cases are expected to decrease rapidly or increase with a flatter curve with stronger nonpharmaceutical interventions, and the peak date is expected to occur earlier (5–20 d) with minimum infections. These findings demonstrate that adopting stringent nonpharmaceutical interventions is the key to alleviating the spread of COVID-19 before attaining worldwide herd immunity.

This paper mainly deals with target detecting problem using polarimetric Multiple Input Multiple Output (MIMO) radar against Spherically Invariant Random Vector (SIRV) clutter. First, we develop the MIMO signal model to two polarimetric channels and SIRV clutter-dominated scenario, and then the Generalized Likelihood Ratio Test (GLRT) is derived with known covariance structure. Meanwhile, three estimation strategies of covariance, such that Sampled Covariance Matrix (SCM), Normalized Sampled Covariance Matrix (NSCM) and Fixed Point Estimation (FPE) matrix, are introduced to make derived receiver fully adaptive. A thorough performance assessment is given by several numerical examples, and the results show that the polarimetric diversity and the spatial diversity can be exploited to improve the detection performance, and it outperforms the conventional polarimetric phased-array counterpart. Meanwhile, the FPE strategy is more suitable to implement the adaptive detection algorithm, the adaptive loss of which is completely acceptable in practical applications.

A family of discrete nonautonomous SIRVS models with general incidence is obtained from a continuous family of models by applying Mickens nonstandard discretization method. Conditions for the permanence and extinction of the disease and the stability of disease-free solutions are determined. Concerning extinction and persistence, the consistency of those discrete models with the corresponding continuous model is discussed: if the time step is sufficiently small, when we have extinction (permanence) for the continuous model, we also have extinction (permanence) for the corresponding discrete model. Some numerical simulations are carried out to compare the different possible discretizations of our continuous model using real data.

A generalized adaptive subspace detector for range-Doppler spread target （RDST-GASD） in the non-Gaussian clutter is derived in this paper. The subspace model of multi-pulse wideband radar target returns is established in the frequency-slow time domain. The clutters are modeled as nonhomogeneous spherically invariant random vectors （SIRVs）; that is, the power of the clutter is different from one range cell to another. The clutter covariance matrix is estimated with the secondary data. The constant false alarm rate （CFAR） property of RDST-GASD with respect to both the power and the covariance matrix of the clutter is demonstrated theoretically. Considering that there is target range walking across range ceils during a coherent processing interval （CPI） for wideband radar, the RDST-GASD does range alignment to the multiple returns of the target in a CPI. As a result, the coherent integration is implemented and the detection performance is improved.

Por lo pronto, coincidieron en que el humor se decantó en sus vidas sin que lo hubieran previsto. \"Yo ni estudié, apenas terminé el bachillerato\", dijo Urtizberea. Y agregó que \"ni soñaba con escribir en la página de Opinión de LA NACION\". Liniers estudiaba para abogado. Su padre, un letrado orgulloso de su profesión, esperaba que el gen hubiera prendido en el hijo. Pero un día Liniers pidió hablar con él. Y fue tanta la angustia del padre \"pensando que había embarazado a mi novia, que cuando le dije que dejaba Derecho y que quería dibujar se alegró mucho. Eso sí, cuando vio que me pasaba el tiempo dibujando pingüinos, volvió a preocuparse\".