Risk estimation and prediction of the transmission of coronavirus disease-2019 (COVID-19) in the mainland of China excluding Hubei province

Analysis and prediction of COVID-19 epidemic in South Africa

The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively; the virus shall cease spreading on April 28, 2021; and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction.

A fractional-order model of coronavirus disease 2019 (COVID-19) with governmental action and individual reaction

The deadly coronavirus disease 2019 (COVID-19) has recently affected each corner of the world. Many governments of different countries have imposed strict measures in order to reduce the severity of the infection. In this present paper, we will study a mathematical model describing COVID-19 dynamics taking into account the government action and the individuals reaction. To this end, we will suggest a system of seven fractional deferential equations (FDEs) that describe the interaction between the classical susceptible, exposed, infectious, and removed (SEIR) individuals along with the government action and individual reaction involvement. Both human-to-human and zoonotic transmissions are considered in the model. The well-posedness of the FDEs model is established in terms of existence, positivity, and boundedness. The basic reproduction number (BRN) is found via the new generation matrix method. Different numerical simulations were carried out by taking into account real reported data from Wuhan, China. It was shown that the governmental action and the individuals' risk awareness reduce effectively the infection spread. Moreover, it was established that with the fractional derivative, the infection converges more quickly to its steady state.

Merits and Limitations of Mathematical Modeling and Computational Simulations in Mitigation of COVID-19 Pandemic: A Comprehensive Review

Mathematical models have assisted in describing the transmission and propagation dynamics of various viral diseases like MERS, measles, SARS, and Influenza; while the advanced computational technique is utilized in the epidemiology of viral diseases to examine and estimate the influences of interventions and vaccinations. In March 2020, the World Health Organization (WHO) has declared the COVID-19 as a global pandemic and the rate of morbidity and mortality triggers unprecedented public health crises throughout the world. The mathematical models can assist in improving the interventions, key transmission parameters, public health agencies, and countermeasures to mitigate this pandemic. Besides, the mathematical models were also used to examine the characteristics of epidemiological and the understanding of the complex transmission mechanism. Our literature study found that there were still some challenges in mathematical modeling for the case of ecology, genetics, microbiology, and pathology pose; also, some aspects like political and societal issues and cultural and ethical standards are hard to be characterized. Here, the recent mathematical models about COVID-19 and their prominent features, applications, limitations, and future perspective are discussed and reviewed. This review can assist in further improvement of mathematical models that will consider the current challenges of viral diseases.

Mathematical analysis and simulation of a stochastic COVID-19 Lévy jump model with isolation strategy

This paper investigates the dynamics of a COVID-19 stochastic model with isolation strategy. The white noise as well as the Lévy jump perturbations are incorporated in all compartments of the suggested model. First, the existence and uniqueness of a global positive solution are proven. Next, the stochastic dynamic properties of the stochastic solution around the deterministic model equilibria are investigated. Finally, the theoretical results are reinforced by some numerical simulations.

Fractional order mathematical modeling of novel corona virus (COVID-19)

In this manuscript, the mathematical model of COVID-19 is considered with eight different classes under the fractional-order derivative in Caputo sense. A couple of results regarding the existence and uniqueness of the solution for the proposed model is presented. Furthermore, the fractional-order Taylor's method is used for the approximation of the solution of the concerned problem. Finally, we simulate the results for 50 days with the help of some available data for fractional differential order to display the excellency of the proposed model.

Emanating challenges in urban and healthcare waste management in Isfahan, Iran after the outbreak of COVID-19

This study aims to investigate the emanating challenges in urban and healthcare waste management in Isfahan, after the new Coronavirus outbreak. Production of 'dry wastes' in Isfahan, with a population of more than 1.9 million, has increased during the pandemic. Personal protective equipments (PPEs) including face masks, single-use gloves and face shields are an emerging group of materials in Isfahan's waste stream. On average, Isfahanian residents have daily discarded over 1.49 and 2.98 million pieces of facemasks and plastic gloves, respectively, after the new Coronavirus pandemic. Overall production of hospital wastes in Isfahan slightly decreased during the pandemic. COVID-19 related wastes in hospitals are mainly treated by wet autoclave method, then collected and transferred to the Sejzy site to be landfilled. Waste separation, recycling and composting programs have been banned due to the COVID-19 pandemic, and all collected MSW are directly disposed of at Sejzy disposal site. Composting as the main disposal method for 60%-70% of the MSW in Isfahan has been terminated; therefore landfilling of urban wastes has dramatically escalated by 3.6 times. Appropriate urban waste management protocols must be provided to restart safe separation, recycling and composting programs to reduce pressure on land for disposal, while practicing safe waste management measures to minimize the possibility of the spread of the viral disease.

TW-SIR: time-window based SIR for COVID-19 forecasts

Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries---China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.

Environmental impacts of new Coronavirus outbreak in Iran with an emphasis on waste management sector

The COVID-19 pandemic have brought several environmental problems worldwide, among which management of municipal solid wastes (MSW) is of great importance due to the effects of solid wastes on soil, air and water resources. This research focused on the emerging challenges in MSW management in Tehran, the capital of Iran, during the COVID-19 pandemic. Tehran has been experiencing higher generation of MSW during the pandemic. Extensive use of personal protective equipments increased textile and plastic wastes considerably. On average, more than 4.5 million pieces of facemasks have been daily discarded by Tehranian residents during the COVID-19 pandemic. Generation of hospital wastes in Tehran also increased by 17.6-61.8% during the pandemic. Legal source separation and recycling programs are still suspended, though waste collection procedure in Tehran has not been subjected to significant changes after the outbreak of the Coronavirus. Hospital wastes, which had been partially separated and treated, are now being collected altogether and landfilled. Waste incineration and composting have been ceased completely; therefore landfilling of MSW has increased by 35% and opted as the only disposal option in Tehran during the COVID-19 outbreak. This research represents a baseline to devise proper urban waste management strategies in developing countries during the COVID-19 pandemic.

A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19

In the past few months, several works were published in regards to the dynamics and early detection of COVID-19 via mathematical modeling and Artificial intelligence (AI). The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-19. In all, 61 journal articles, reports, fact sheets, and websites dealing with COVID-19 were studied and reviewed. It was found that most mathematical modeling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models while most of the AI implementations were Convolutional Neural Network (CNN) on X-ray and CT images. In terms of available datasets, they include aggregated case reports, medical images, management strategies, healthcare workforce, demography, and mobility during the outbreak. Both Mathematical modeling and AI have both shown to be reliable tools in the fight against this pandemic. Several datasets concerning the COVID-19 have also been collected and shared open source. However, much work is needed to be done in the diversification of the datasets. Other AI and modeling applications in healthcare should be explored in regards to this COVID-19.

Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China

Background: Coronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the countries suffering the most with the COVID-19 epidemic. It is important to predict the epidemic trend of the COVID-19 epidemic in Italy to help develop public health strategies. Methods: We used time-series data of COVID-19 from Jan 22 2020 to Apr 02 2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with a similar total population number to Italy, was used as a comparative item. Results: In the eSIR model, we estimated that the mean of basic reproductive number for COVID-19 was 4.34 (95% CI, 3.04-6.00) in Italy and 3.16 (95% CI, 1.73-5.25) in Hunan. There would be a total of 182 051 infected cases (95%CI:116 114-274 378) under the current country blockade and the endpoint would be Aug 05 in Italy. Conclusion: Italy's current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures should be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.

The Exponentially Increasing Rate of Patients Infected with COVID-19 in Iran

BACKGROUND

Coronavirus, the cause of severe acute respiratory syndrome (COVID-19), is rapidly spreading around the world. Since the number of corona positive patients is increasing sharply in Iran, this study aimed to forecast the number of newly infected patients in the coming days in Iran.

METHODS

The data used in this study were obtained from daily reports of the Iranian Ministry of Health and the datasets provided by the Johns Hopkins University including the number of new infected cases from February 19, 2020 to March 21, 2020. The autoregressive integrated moving average (ARIMA) model was applied to predict the number of patients during the next thirty days.

RESULTS

The ARIMA model forecasted an exponential increase in the number of newly detected patients. The result of this study also show that if the spreading pattern continues the same as before, the number of daily new cases would be 3574 by April 20.

CONCLUSION

Since this disease is highly contagious, health politicians need to make decisions to prevent its spread; otherwise, even the most advanced and capable health care systems would face problems for treating all infected patients and a substantial number of deaths will become inevitable.

Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China

Background: Coronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the countries suffering the most with the COVID-19 epidemic. It is important to predict the epidemic trend of the COVID-19 epidemic in Italy to help develop public health strategies. Methods: We used time-series data of COVID-19 from Jan 22 2020 to Apr 02 2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with a similar total population number to Italy, was used as a comparative item. Results: In the eSIR model, we estimated that the mean of basic reproductive number for COVID-19 was 4.34 (95% CI, 3.04-6.00) in Italy and 3.16 (95% CI, 1.73-5.25) in Hunan. There would be a total of 182 051 infected cases (95%CI:116 114-274 378) under the current country blockade and the endpoint would be Aug 05 in Italy. Conclusion: Italy's current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures should be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.