A Model for COVID-19 Prediction in Iran Based on China Parameters

Impact of COVID-19 pandemic on road traffic injuries in Iran: An interrupted time-series analysis

INTRODUCTION

Globally, the COVID-19 pandemic has affected the number of road accidents and deaths caused by them. The present study aimed to identify the effect of this epidemic on traffic accidents and their casualties in Iran.

METHODS

In this study, Interrupted Time Series Analysis (ITSA) was used in a semi-experimental design to measure the impact of the restrictive policies of COVID-19 on road accidents. Data were collected retrospectively from the Iran Red Crescent Society data set for 31 provinces from March 2017 to February 2022. The information related to the number of road accidents, injuries, deaths, and deaths in the hospital was collected. The Newey-West method is used for estimation. Statistical analyses were carried out using R software version 3.6.1.

RESULTS

Since February 2020 in Iran, the reduction in the number of road accidents and the number of injuries and deaths in these accidents was significant at 5% but the reduction of deaths in the scene and hospital was significant at 10%. In general, for all variables, the reduction trend was established only in the first months, and then it had an upward trend.

CONCLUSION

In the early months of the COVID-19 epidemic in Iran, the number of road accidents and their casualties decreased. Policies restricting traffic, quarantine, and fines for violators can be reasons for changing people's behavior and travel patterns and also lead to a reduction in traffic accidents and fatalities. Such studies can explain the importance of the policies in changing behavioural patterns and can be used as a guide in future policies.

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

How do the smart travel ban policy and intercity travel pattern affect COVID-19 trends? Lessons learned from Iran

COVID-19, as the most significant epidemic of the century, infected 467 million people and took the lives of more than 6 million individuals as of March 19, 2022. Due to the rapid transmission of the disease and the lack of definitive treatment, countries have employed nonpharmaceutical interventions. This study aimed to investigate the effectiveness of the smart travel ban policy, which has been implemented for non-commercial vehicles in the intercity highways of Iran since November 21, 2020. The other goal was to suggest efficient COVID-19 forecasting tools and to examine the association of intercity travel patterns and COVID-19 trends in Iran. To this end, weekly confirmed cases and deaths due to COVID-19 and the intercity traffic flow reported by loop detectors were aggregated at the country's level. The Box-Jenkins methodology was employed to evaluate the policy's effectiveness, using the interrupted time series analysis. The results indicated that the autoregressive integrated moving average with explanatory variable (ARIMAX) model outperformed the univariate ARIMA model in predicting the disease trends based on the MAPE criterion. The weekly intercity traffic and its lagged variables were entered as covariates in both models of the disease cases and deaths. The results indicated that the weekly intercity traffic increases the new weekly COVID-19 cases and deaths with a time lag of two and five weeks, respectively. Besides, the interrupted time series analysis indicated that the smart travel ban policy had decreased intercity travel by around 29%. Nonetheless, it had no significant direct effect on COVID-19 trends. This study suggests that the travel ban policy would not be efficient lonely unless it is coupled with active measures and adherence to health protocols by the people.

A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction

Currently, the novel COVID-19 coronavirus has been widely spread as a global pandemic. The COVID-19 pandemic has a major influence on human life, healthcare systems, and the economy. There are a large number of methods available for predicting the incidence of the virus. A complex and non-stationary problem such as the COVID-19 pandemic is characterized by high levels of uncertainty in its behavior during the pandemic time. The fuzzy logic, especially Type-2 Fuzzy Logic, is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. The main objective of the current study is to present a novel Deep Interval Type-2 Fuzzy LSTM (DIT2FLSTM) model for prediction of the COVID-19 incidence, including new cases, recovery cases, and mortality rate in both short and long time series. The proposed model was evaluated on real datasets produced by the world health organization (WHO) on top highly risked countries, including the USA, Brazil, Russia, India, Peru, Spain, Italy, Iran, Germany, and the U.K. The results confirm the superiority of the DIT2FLSTM model with an average area under the ROC curve (AUC) of 96% and a 95% confidence interval of [92-97] % in the short-term and long-term. The DIT2FLSTM was applied to a well-known standard benchmark, the Mackey-Glass time-series, to show the robustness and proficiency of the proposed model in uncertain and chaotic time series problems. The results were evaluated using a 10-fold cross-validation technique and statistically validated through the t-test method. The proposed DIT2FLSTM model is promising for the prediction of complex problems such as the COVID-19 pandemic and making strategic prevention decisions to save more lives.

Prediction of asymptomatic COVID-19 infections based on complex network

Novel coronavirus pneumonia (COVID-19) epidemic outbreak at the end of 2019 and threaten global public health, social stability, and economic development, which is characterized by highly contagious and asymptomatic infections. At present, governments around the world are taking decisive action to limit the human and economic impact of COVID-19, but very few interventions have been made to target the transmission of asymptomatic infected individuals. Thus, it is a quite crucial and complex problem to make accurate forecasts of epidemic trends, which many types of research dedicated to deal with it. In this article, we set up a novel COVID-19 transmission model by introducing traditional SEIR (susceptible-exposed-infected-removed) disease transmission models into complex network and propose an effective prediction algorithm based on the traditional machine learning algorithm TrustRank, which can predict asymptomatic infected individuals in a population contact network. Our simulation results show that our method largely outperforms the graph neural network algorithm for new coronary pneumonia prediction and our method is also robust and gives good results even if the network information is incomplete.

Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

BACKGROUND

Public health policies with varying degrees of restriction have been imposed around the world to prevent the spread of coronavirus disease 2019 (COVID-19). In this study, we aimed to evaluate the effects of the implementation of government policies and the Nowruz holidays on the containment of the COVID-19 pandemic in Iran, using an intervention time series analysis.

METHODS

Daily data on COVID-19 cases registered between February 19 and May 2, 2020 were collected from the World Health Organization (WHO)'s website. Using an intervention time series modeling, the effect of two government policies on the number of confirmed cases were evaluated, namely the closing of schools and universities, and the implementation of social distancing measures. Furthermore, the effect of the Nowruz holidays as a non-intervention factor for the spread of COVID-19 was also analyzed.

RESULTS

The results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of new confirmed cases. The Nowruz holidays was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; p<0.001)), while the implementation of social distancing measures was followed by a significant decrease in such cases (2,182.80; 95% CI, 1,556.56 to 2,809.04; p<0.001).

CONCLUSION

The Nowruz holidays and the implementation of social distancing measures in Iran were related to a significant increase and decrease in COVID-19 cases, respectively. These results highlight the necessity of measuring the effect of health and social interventions for their future implementations.

Systematic review of predictive mathematical models of COVID-19 epidemic

BACKGROUND

Various mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models.

METHODS

Articles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail.

RESULTS

Among 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions.

CONCLUSION

There was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.

Mathematical modelling for decision making of lockdown during COVID-19

Due to the recent worldwide outbreak of COVID-19, there has been an enormous change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel, tourism, economy, etc., in all the affected countries. In this scenario, people must be careful and cautious about the symptoms and should act accordingly. Accurate predictions of different factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the pandemic and precautions can be taken accordingly. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level. The novelty of the proposed work lies here. In this paper, first, authors have analysed spreading of the said disease using data collected from various platforms and then, have presented a predictive mathematical model for fifteen countries from first, second and third world for probable future projections of this pandemic. The prediction can be used by planning commission, healthcare organizations and the government agencies as well for creating suitable arrangements against this pandemic.

Forecasting the COVID-19 Pandemic in Saudi Arabia Using a Modified Singular Spectrum Analysis Approach: Model Development and Data Analysis

Background

Infectious disease is one of the main issues that threatens human health worldwide. The 2019 outbreak of the new coronavirus SARS-CoV-2, which causes the disease COVID-19, has become a serious global pandemic. Many attempts have been made to forecast the spread of the disease using various methods, including time series models. Among the attempts to model the pandemic, to the best of our knowledge, no studies have used the singular spectrum analysis (SSA) technique to forecast confirmed cases.

Objective

The primary objective of this paper is to construct a reliable, robust, and interpretable model for describing, decomposing, and forecasting the number of confirmed cases of COVID-19 and predicting the peak of the pandemic in Saudi Arabia.

Methods

A modified singular spectrum analysis (SSA) approach was applied for the analysis of the COVID-19 pandemic in Saudi Arabia. We proposed this approach and developed it in our previous studies regarding the separability and grouping steps in SSA, which play important roles in reconstruction and forecasting. The modified SSA approach mainly enables us to identify the number of interpretable components required for separability, signal extraction, and noise reduction. The approach was examined using different levels of simulated and real data with different structures and signal-to-noise ratios. In this study, we examined the capability of the approach to analyze COVID-19 data. We then used vector SSA to predict new data points and the peak of the pandemic in Saudi Arabia.

Results

In the first stage, the confirmed daily cases on the first 42 days (March 02 to April 12, 2020) were used and analyzed to identify the value of the number of required eigenvalues (r) for separability between noise and signal. After obtaining the value of r, which was 2, and extracting the signals, vector SSA was used to predict and determine the pandemic peak. In the second stage, we updated the data and included 81 daily case values. We used the same window length and number of eigenvalues for reconstruction and forecasting of the points 90 days ahead. The results of both forecasting scenarios indicated that the peak would occur around the end of May or June 2020 and that the crisis would end between the end of June and the middle of August 2020, with a total number of infected people of approximately 330,000.

Conclusions

Our results confirm the impressive performance of modified SSA in analyzing COVID-19 data and selecting the value of r for identifying the signal subspace from a noisy time series and then making a reliable prediction of daily confirmed cases using the vector SSA method.

Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan

The multifaceted destructions caused by COVID-19 have been compared to that of World War II. What makes the situation even more complicated is the ambiguity about the duration and ultimate spread of the pandemic. It is especially critical for the governments, healthcare systems, and economic sectors to have an estimate of the future of this disaster. By using different mathematical approaches, including the classical susceptible-infected-recovered (SIR) model and its derivatives, many investigators have tried to predict the outbreak of COVID-19. In this study, we simulated the epidemic in Isfahan province of Iran for the period from Feb 14th to April 11th and also forecasted the remaining course with three scenarios that differed in terms of the stringency level of social distancing. Despite the prediction of disease course in short-term intervals, the constructed SIR model was unable to forecast the actual spread and pattern of epidemic in the long term. Remarkably, most of the published SIR models developed to predict COVID-19 for other communities, suffered from the same inconformity. The SIR models are based on assumptions that seem not to be true in the case of the COVID-19 epidemic. Hence, more sophisticated modeling strategies and detailed knowledge of the biomedical and epidemiological aspects of the disease are needed to forecast the pandemic.

Supply chain design to tackle coronavirus pandemic crisis by tourism management

The rapid growth of the COVID-19 pandemic in the world and the importance of controlling it in all regions have made managing this crisis a great challenge for all countries. In addition to imposing various monetary costs on countries, this pandemic has left many serious damages and casualties. Proper control of this crisis will provide better medical services. Controlling travel and tourists in this crisis is also an effective factor. Hence, the proposed model wants to control the crisis by controlling the volume of incoming tourists to each city and region by closing the entry points of that region, which reduces the inpatients. The proposed multi-objective model is designed to aim at minimizing total costs, minimizing the tourist patients, and maximizing the number of city patients. The Improved Multi-choice Goal programming (IMCGP) method has been used to solve the multi-objective problem. The model examines the results by considering a case study. Sensitivity analyses and managerial insight are also provided. According to the results obtained from the model and case study, two medical centers with the capacity of 300 and 700 should be opened if the entry points are not closed.

Rapid review of COVID-19 epidemic estimation studies for Iran

BACKGROUND

To inform researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review.

METHODS

We searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 131 studies and included 29 of them.

RESULTS

The included studies provided outputs for a total of 84 study-model/scenario combinations. Sixteen studies used 3-4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 1,777 (388,951) for cumulative deaths, 20,588 (2,310,161) for cumulative cases, and at the end of month four (2020-06-20), were 3,590 (1,819,392) for cumulative deaths, and 144,305 (4,266,964) for cumulative cases. Highest estimates of cumulative deaths (and cases) for latest date available in 2020 were 418,834 on 2020-12-19 (and 41,475,792 on 2020-12-31). Model estimates predict an ominous course of epidemic progress in Iran. Increase in percent population using masks from the current situation to 95% might prevent 26,790 additional deaths (95% confidence interval 19,925-35,208) by the end of year 2020.

CONCLUSIONS

Meticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed. Not accounting for under-reporting drives the models' results misleading.

Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models

BACKGROUND

The short term forecasts regarding different parameters of the COVID-19 are very important to make informed decisions. However, majority of the earlier contributions have used classical time series models, such as auto regressive integrated moving average (ARIMA) models, to obtain the said forecasts for Iran and its neighbors. In addition, the impacts of lifting the lockdowns in the said countries have not been studied. The aim of this paper is to propose more flexible Bayesian structural time series (BSTS) models for forecasting the future trends of the COVID-19 in Iran and its neighbors, and to compare the predictive power of the BSTS models with frequently used ARIMA models. The paper also aims to investigate the casual impacts of lifting the lockdown in the targeted countries using proposed models.

METHODS

We have proposed BSTS models to forecast the patterns of this pandemic in Iran and its neighbors. The predictive power of the proposed models has been compared with ARIMA models using different forecast accuracy criteria. We have also studied the causal impacts of resuming commercial/social activities in these countries using intervention analysis under BSTS models. The forecasts for next thirty days were obtained by using the data from March 16 to July 22, 2020. These data have been obtained from Our World in Data and Humanitarian Data Exchange (HDX). All the numerical results have been obtained using R software.

RESULTS

Different measures of forecast accuracy advocated that forecasts under BSTS models were better than those under ARIMA models. Our forecasts suggested that the active numbers of cases are expected to decrease in Iran and its neighbors, except Afghanistan. However, the death toll is expected to increase at more pace in majority of these countries. The resuming of commercial/social activities in these countries has accelerated the surges in number of positive cases.

CONCLUSIONS

The serious efforts would be needed to make sure that these expected figures regarding active number of cases come true. Iran and its neighbors need to improve their extensive healthcare infrastructure to cut down the higher expected death toll. Finally, these countries should develop and implement the strict SOPs for the commercial activities in order to prevent the expected second wave of the pandemic.

Assessing Iran's health system according to the COVID-19 strategic preparedness and response plan of the World Health Organization: health policy and historical implications

Background

The role of health systems in the management of disasters, including natural hazards like outbreaks and pandemics, is crucial and vital. Healthcare systems which are unprepared to properly deal with crises are much more likely to expose their public health workers and health personnel to harm and will not be able to deliver healthcare provisions in critical situations. This can lead to a drammatic toll of deaths, even in developed countries. The possible occurrence of global crises has prompted the World Health Organization (WHO) to devise instruments, guidelines and tools to assess the capacity of countries to deal with disasters. Iran's health system has been hit hardly by the COVID-19 pandemic. In this study, we aimed to assess its preparedness and response to the outbreak.

Methods

The present investigation was designed as a qualitative study. We utilized the "COVID-19 Strategic Preparedness and Response Plan" devised by WHO as a conceptual framework.

Results

The dimension/pillar which scored the highest was national laboratories, followed by surveillance, rapid response teams and case investigations. Risk communication and community engagement was another pillar receiving a high score, followed by infection prevention and control and by country-level coordination, planning and monitoring. The pillars/dimensions receiving the lowest scores were operational support and logistics; case management; and points of entry.

Discussion

The COVID-19 pandemic has represented an unprecedent event that has challenged healthcare systems and facilities worldwide, highlighting their weaknesses and the need for inter-sectoral cooperation and collaboration during the crisis. Analyzing these experiences and capitalizing on them, by strengthening them,will help countries to be more prepared to face possible future crises.

Nonlinear models: a case of the COVID-19 confirmed rates in top 8 worst affected countries

Over the last 9 months, the most prominent global health threat has been COVID-19. It first appeared in Wuhan, China, and then rapidly spread throughout the world. Since no treatment or preventative strategy has been identified until this time, millions of people across the world have been seriously affected by COVID-19. The modelling and prediction of confirmed COVID-19 cases have been given much attention by government policymakers for the purpose of combating it more effectively. For this purpose, the modelling and prediction performances of the linear model (LM), generalized additive model(GAM) and the time-varying linear model (Tv-LM) via Kalman filter are compared. This has never yet been undertaken in the literature. This comparative analysis also evaluates the linear relationship between the confirmed cases of COVID-19 in individual countries with the world. The analysis is implemented using daily COVID-19 confirmed rates of the top 8 most heavily affected countries and that of the world between 11 March and 21 December 2020 and 14-day forward predictions. The empirical findings show that the Tv-LM outperforms others in terms of model fit and predictability, suggesting that the relationship between each country's rates with the world's should be locally linear, not globally linear.

Forecasting COVID-19 pandemic using optimal singular spectrum analysis

Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries in the world. The aim of this study is to examine the potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19, which are the three main variables of interest. This paper contributes to the literature on forecasting COVID-19 pandemic in several ways. Firstly, an algorithm is proposed to calculate the optimal parameters of SSA including window length and the number of leading components. Secondly, the results of two forecasting approaches in the SSA, namely vector and recurrent forecasting, are compared to those from other commonly used time series forecasting techniques. These include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Exponential Smoothing, TBATS, and Neural Network Autoregression (NNAR). Thirdly, the best forecasting model is chosen based on the accuracy measure Root Mean Squared Error (RMSE), and it is applied to forecast 40 days ahead. These forecasts can help us to predict the future behaviour of this disease and make better decisions. The dataset of Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is adopted to forecast the number of daily confirmed cases, deaths, and recoveries for top ten affected countries until October 29, 2020. The findings of this investigation show that no single model can provide the best model for any of the countries and forecasting horizons considered here. However, the SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models.

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%.

The Impact of Control Measures and Holiday Seasons on Incidence and Mortality Rate of COVID-19 in Iran

BACKGROUND

Preventive measures on the COVID-19 pandemic is an effective way to control its spread. We aimed to investigate the effect of control measures and holiday seasons on the incidence and mortality rate of COVID-19 in Iran.

STUDY DESIGN

An observational study.

METHODS

The daily data of confirmed new cases and deaths in Iran were taken from the Johns Hopkins University COVID-19 database. We calculated weekly data from 19 Feb to 6 Oct 2020. To estimate the impact of control measures and holiday seasons on the incidence rate of new cases and deaths, an autoregressive hidden Markov model (ARHMM) with two hidden states fitted the data. The hidden states of the fitted model can distinguish the peak period from the non-peak period.

RESULTS

The control measures with a delay of one-week and two-week had a decreasing effect on the new cases in the peak and non-peak periods, respectively (P=0.005). The holiday season with a two-week delay increased the total number of new cases in the peak periods (P=0.031). The peak period for the occurrence of COVID-19 was estimated at 3 weeks. In the peak period of mortality, the control measures with a three-week delay decreased the COVID-19 mortality (P=0.010). The expected duration of staying in the peak period of mortality was around 6 weeks.

CONCLUSION

When an increasing trend was seen in the country, the control measures could decline the incidence and mortality related to COVID-19. Implementation of official restrictions on holiday seasons could prevent an upward trend of incidence for COVID-19 during the peak period.

Reverse logistics network design for medical waste management in the epidemic outbreak of the novel coronavirus (COVID-19)

The recent pandemic triggered by the outbreak of the novel coronavirus boosted the demand for medical services and protective equipment, causing the generation rate of infectious medical waste (IMW) to increase rapidly. Designing an efficient and reliable IMW reverse logistics network in this situation can help to control the spread of the virus. Studies on this issue are limited, and minimization of costs and the risks associated with the operations of this network consisting of different types of medical waste generation centers (MWGC) are rarely considered. In this research, a linear programming model with three objective functions is developed to minimize the total costs, the risk associated with the transportation and treatment of IMW, and the maximum amount of uncollected waste in MWGCs. Also, multiple functions that calculate the amount of generated waste according to the parameters of the current epidemic outbreak are proposed. Revised Multi-Choice Goal Programming method is employed to solve the multi-objective model, and a real case study from Iran is examined to illustrate the validation of the proposed model. The final results show that the model can create a balance between three considered objectives by determining the flow between centers, deciding to install two new temporary treatment centers, and allowing the network to only have uncollected waste in the first two periods in some MWGCs. Also, managerial insights for health organization authorities extracted from the final results and sensitivity analyses are presented for adequately handling the IMW network.

SEIAQRDT model for the spread of novel coronavirus (COVID-19): A case study in India

COVID-19 is a global pandemic declared by WHO. This pandemic requires the execution of planned control strategies, incorporating quarantine, self-isolation, and tracing of asymptomatic cases. Mathematical modeling is one of the prominent techniques for predicting and controlling the spread of COVID-19. The predictions of earlier proposed epidemiological models (e.g. SIR, SEIR, SIRD, SEIRD, etc.) are not much accurate due to lack of consideration for transmission of the epidemic during the latent period. Moreover, it is important to classify infected individuals to control this pandemic. Therefore, a new mathematical model is proposed to incorporate infected individuals based on whether they have symptoms or not. This model forecasts the number of cases more accurately, which may help in better planning of control strategies. The model consists of eight compartments: susceptible (S), exposed (E), infected (I), asymptomatic (A), quarantined (Q), recovered (R), deaths (D), and insusceptible (T), accumulatively named as SEIAQRDT. This model is employed to predict the pandemic results for India and its majorly affected states. The estimated number of cases using the SEIAQRDT model is compared with SIRD, SEIR, and LSTM models. The relative error square analysis is used to verify the accuracy of the proposed model. The simulation is done on real datasets and results show the effectiveness of the proposed approach. These results may help the government and individuals to make the planning in this pandemic situation.

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.

Development of minimal basic data set to report COVID-19

Background: Effective surveillance of COVID-19 highlights the importance of rapid, valid, and standardized information to crisis monitoring and prompts clinical interventions. Minimal basic data set (MBDS) is a set of metrics to be collated in a standard approach to allow aggregated use of data for clinical purposes and research. Data standardization enables accurate comparability of collected data, and accordingly, enhanced generalization of findings. The aim of this study is to establish a core set of data to characterize COVID-19 to consolidate clinical practice. Methods: A 3-step sequential approach was used in this study: (1) an elementary list of data were collected from the existing information systems and data sets; (2) a systematic literature review was conducted to extract evidence supporting the development of MBDS; and (3) a 2-round Delphi survey was done for reaching consensus on data elements to include in COVID-19 MBDS and for its robust validation. Results: In total, 643 studies were identified, of which 38 met the inclusion criteria, where a total of 149 items were identified in the data sources. The data elements were classified by 3 experts and validated via a 2-round Delphi procedure. Finally, 125 data elements were confirmed as the MBDS. Conclusion: The development of COVID-19 MBDS could provide a basis for meaningful evaluations, reporting, and benchmarking COVID-19 disease across regions and countries. It could also provide scientific collaboration for care providers in the field, which may lead to improved quality of documentation, clinical care, and research outcomes.

Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.

COVID-19 mortality rates in the European Union, Switzerland, and the UK: effect of timeliness, lockdown rigidity, and population density

BACKGROUND

To date, the European experience with COVID-19 mortality has been different to that observed in China and Asia. We aimed to forecast mortality trends in the 27 countries of the European Union (EU), plus Switzerland and the UK, where lockdown dates and confinement interventions have been heterogeneous, and to explore its determinants.

METHODS

We have adapted our predictive model of COVID-19-related mortality, which rested on the observed mortality within the first weeks of the outbreak and the date of the respective lockdown in each country. It was applied in a training set of three countries (Italy, Germany and Spain), and then applied to the EU plus the UK and Switzerland. In addition, we explored the effects of timeliness and rigidity of the lockdown (on a five-step scale) and population density in our forecasts. We report r², and percent variation of expected versus observed deaths, all following TRIPOD guidance.

RESULTS

We identified a homogeneous distribution of deaths, and found a median of 24 days after lockdown adoption to reach the maximum daily deaths. Strikingly, cumulative deaths up to April 25^th, 2020 observed in Europe separated countries in three waves, according to the time lockdown measures were adopted following the onset of the outbreak: after a week, within a week, or even prior to the outbreak (r²=0.876). In contrast, no correlation neither with lockdown rigidity nor population density were observed.

CONCLUSIONS

The European experience confirms that early, effective interventions of lockdown are fundamental to minimizing the COVID-19 death toll.