Using the SIR Model to Analyze the Epidemiological Situation in Kazakhstan and Neighboring Countries

In this paper, we study the epidemic situation in Kazakhstan and neighboring countries, taking into account territorial features in emergency situations. As you know, the excessive concentration of the population in large cities and the transition to a world without borders created ideal conditions for a global pandemic. The article also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by types of models (basic SIR model, modified SEIR models) and the practical application of the SIR model using an example (Kazakhstan, Russia, Kyrgyzstan, Uzbekistan and other neighboring countries). The obtained processing results are based on statistical data from open sources on the development of the COVID-19 epidemic. The result obtained is a general solution of the SIR-model of the spread of the epidemic according to the fourth-order Runge-Kutta method. The parameters β, γ, which are indicators of infection, recovery, respectively, were calculated using data at the initial phase of the Covid 2019 epidemic. An analysis of anti-epidemic measures in neighboring countries is given. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Research on the propagation model of COVID-19 based on virus dynamics

At the end of 2019, a new kind of coronavirus spread in Wuhan city of Hubei province and other places, seriously endangering people's health. Scientific prediction of the spread trend of the novel coronavirus makes a big difference in epidemic prevention, treatment, and relevant health decisions. The COVID-19 transmission model based on virus dynamics was established. We propose a data-driven dynamic modeling method for infectious disease transmission, which is a sparse identification method for nonlinear dynamic models. Sparse regression and parameter identification are used to accurately find the control equation from the potential dynamic models to simulate the dynamic transmission process of the novel coronavirus in Wuhan at the end of 2019. Through the experiment, we get the results that the model can well describe the spread of COVID-19 in Wuhan and also prove that the model is practical and can be extended to the prediction of related epidemic situations. © 2023 SPIE.

Predicting COVID-19 using lioness optimization algorithm and graph convolution network.

In this paper, a graph convolution network prediction model based on the lioness optimization algorithm (LsOA-GCN) is proposed to predict the cumulative number of confirmed COVID-19 cases in 17 regions of Hubei Province from March 23 to March 29, 2020, according to the transmission characteristics of COVID-19. On the one hand, Spearman correlation analysis with delay days and LsOA are used to capture the dynamic changes of feature information to obtain the temporal features. On the other hand, the graph convolutional network is used to capture the topological structure of the city network, so as to obtain spatial information and finally realize the prediction task. Then, we evaluate this model through performance evaluation indicators and statistical test methods and compare the results of LsOA-GCN with 10 representative prediction methods in the current epidemic prediction study. The experimental results show that the LsOA-GCN prediction model is significantly better than other prediction methods in all indicators and can successfully capture spatio-temporal information from feature data, thereby achieving accurate prediction of epidemic trends in different regions of Hubei Province.

Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement

State and local governments have imposed health policies to contain the spread of COVID-19 since it had a serious impact on human daily life. However, the public stance on these measures may be time-varying. It is likely to escalate the infection in the area where the public is negative or resistant. To take advantage of the correlation between public stance on health policies and the COVID-19 statistics, we propose a novel framework, Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement (MP3), which is composed of three modules: (1) Stance awareness module to make stance detection on health policies from users' tweets in social media and convert them into a stance time series. (2) Temporal feature extraction module that applies Convolution Neural Network and Recurrent Neural Network to extract and fuse local patterns and long-term correlations from COVID-19 statistics. Moreover, a Stance Latency-aware Attention is proposed to capture dynamic social effects and fuse them with temporal features. (3) Multi-task prediction module to adopt Graph Convolution Network to model the spread of pandemic and employ multi-task learning to simultaneously predict COVID-19 statistics and the trend of public stance on health policies. The proposed framework outperforms state-of-the-art baselines on both confirmed cases and deaths prediction tasks. © 2022 IEEE.

Parameter Adaptive SEIRD model for Epidemic Prediction

The classical infectious disease diffusion model has a deficiency of static parameters, which will lead to server prediction error. Therefore, this article used three different parameter fitting methods to construct a dynamic update mechanism of outbreak spread parameters and reversed fitting through the actual data of the epidemic. The best epidemic transmission parameters can effectively predict the growth of the outbreak in the next cycle. Then, we take the second wave of the outbreak in India as an example, the dynamic update mechanism of the epidemic spread parameters can effectively improve the accuracy of the prediction of the evolution of the novel coronavirus epidemic. According to the test results,we believe it can help the government make correct decisions, implement effective control and realize the reasonable allocation of emergency resources. © 2022 IEEE.

Spatio-temporal Graph Learning for Epidemic Prediction

The COVID-19 pandemic has posed great challenges to public health services, government agencies, and policymakers, raising huge social conflicts between public health and economic resilience. Policies such as reopening or closure of business activities are formulated based on scientific projections of infection risks obtained from infection dynamics models. Though most parameters in epidemic prediction service models can be set with domain knowledge of COVID-19, a key parameter, namely, human mobility, is often challenging to estimate due to complex spatio-temporal correlations and social contexts under escalating COVID-19 facilities. Moreover, how to integrate the various implicit features to accurately predict infectious cases is still an open issue. To address this challenge, we formulate the problem as a spatio-temporal network representation problem and propose STEP, a Spatio-Temporal Epidemic Prediction framework, to estimate pandemic infection risk of a city by integrating various real-world conditions (e.g., City Risk Index, climate, and medical conditions) into graph-structured data. We also employ a multi-head attention mechanism in representation learning to extract implicit features for a given city. Extensive experiments have been conducted upon the real-world dataset for 51 states (50 states and Washington, D.C.) of the USA. Experimental results show that STEP can yield more accurate pandemic infection risk estimation than baseline methods. Moreover, STEP outperforms other methods in both short-term and long-term prediction. [ABSTRACT FROM AUTHOR] Copyright of ACM Transactions on Intelligent Systems & Technology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Covid-19 Epidemic Trend Prediction Based on CNN-StackBiLSTM

In late 2019, the novel coronavirus (COVID-19) became a major health hazard around the world. Recently, the COVID-19 has spread widely in most countries and regions and the number of infected people continues to grow rapidly. Therefore, it is essential to research the development trend of the epidemic. The prediction of the number of infections and deaths is critical and helpful for developing health and epidemic prevention strategies and even curbing the epidemic. In this paper, a one-dimensional convolutional neural network combined with the stacked long-short-term-memory network model (CNN-StackBiLSTM) is proposed for the time-series prediction of cumulative cases and daily new cases. The local feature is extracted by CNN. The stacked BiLSTM captures the deeper characteristics of the time-series data. By combining the two networks, the proposed method simultaneously considers the information of temporal and spatial domains and can achieve accurate prediction results. Examples in Taiwan and Italy demonstrate the effectiveness of the proposed method. The proposed method is compared with LSTM, BiLSTM, and GRU. The mean absolute error, mean square error, R2 score, and root mean square error are calculated to quantificationally measure the different models. The results indicate the proposed method performs well in the prediction of both new daily confirmed cases and cumulative confirmed cases. © 2022 IEEE.

Limits of epidemic prediction using SIR models.

The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In practice, it is of substantial interest to estimate the model parameters based on noisy observations early in the outbreak, well before the epidemic reaches its peak. This allows prediction of the subsequent course of the epidemic and design of appropriate interventions. However, accurately inferring SIR model parameters in such scenarios is problematic. This article provides novel, theoretical insight on this issue of practical identifiability of the SIR model. Our theory provides new understanding of the inferential limits of routinely used epidemic models and provides a valuable addition to current simulate-and-check methods. We illustrate some practical implications through application to a real-world epidemic data set.

Intervention-Aware Epidemic Prediction by Enhanced Whale Optimization

In recent decades, new epidemics have seriously endangered people’s lives and are now the leading cause of death in the world. The prevention of pandemic diseases has therefore become a top priority today. However, effective prevention remains a difficult challenge due to factors such as transmission mechanisms, lack of documentation of clinical outcomes, and population control. To this end, this paper proposes a susceptible-exposed-infected-quarantined (hospital or home)-recovered (SEIQHR) model based on human intervention strategies to simulate and predict recent outbreak transmission trends and peaks in Changchun, China. In this study, we introduce Levy operator and random mutation mechanism to reduce the possibility of the algorithm falling into a local optimum. The algorithm is then used to identify the parameters of the model optimally. The validity and adaptability of the proposed model are verified by fitting experiments to the number of infections in cities in China that had COVID-19 outbreaks in previous periods (Nanjing, Wuhan, and Xi’an), where the peaks and trends obtained from the experiments largely match the actual situation. Finally, the model is used to predict the direction of the disease in Changchun, China, for the coming period. The results indicated that the number of COVID-19 infections in Changchun would peak around April 3 and continue to decrease until the end of the outbreak. These predictions can help the government plan countermeasures to reduce the expansion of the epidemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

DEVELOPMENT OF ENSEMBLE MACHINE LEARNING MODEL TO IMPROVE COVID-19 OUTBREAK FORECASTING

The world is currently facing the coronavirus disease 2019 (COVID-19 pandemic). Forecasting the progression of that pandemic is integral to planning the necessary next steps by governments and organizations. Recent studies have examined the factors that may impact COVID-19 forecasting and others have built models for predicting the numbers of active cases, recovered cases and deaths. The aim of this study was to improve the forecasting predictions by developing an ensemble machine-learning model that can be utilized in addition to the Naïve Bayes classifier, which is one of the simplest and fastest probabilistic classifiers. The first ensemble model combined gradient boosting and random forest classifiers and the second combined support vector machine and random-forest classifiers. The numbers of confirmed, recovered and death cases will be predicted for a period of 10 days. The results will be compared to the findings of previous studies. The results showed that the ensemble algorithm that combined gradient boosting and random-forest classifiers achieved the best performance, with 99% accuracy in all cases. © 2022, Scientific Research Support Fund of Jordan. All rights reserved.

An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19.

At present, the Corona Virus Disease 2019 (COVID-19) is ravaging the world, bringing great impact on people's life safety and health as well as the healthy development of economy and society, so the research on the prediction of the development trend of the epidemic is crucial. In this paper, we focus on the prevention and control of epidemic using the relevant technologies in the field of artificial intelligence and signal analysis. With the unknown principle of epidemic transmission, we first smooth out the complex and variable epidemic data through the empirical mode decomposition model to obtain the change trends of epidemic data at different time scales. On this basis, the change trends under different time scales are trained using an extreme learning machine to obtain the corresponding prediction values, and finally the epidemic prediction results are obtained by fitting through Adaptive Network-based Fuzzy Inference System. The experimental results show that the algorithm has good learning ability, especially in the prediction of time-series sequences can guarantee the accuracy rate while having low time complexity. Therefore, this paper not only plays a theoretical support for epidemic prevention and control, but also plays an important role in the construction of public emergency health system in the long run.

The impact of crowd gatherings on the spread of COVID-19.

Crowd gatherings are an important cause of COVID-19 outbreaks. However, how the scale, scene and other factors of gatherings affect the spread of the epidemic remains unclear. A total of 184 gathering events worldwide were collected to construct a database, and 99 of them with a clear gathering scale were used for statistical analysis of the impact of these factors on the disease incidence among the crowd in the study. The results showed that the impact of small-scale (less than 100 people) gathering events on the spread of COVID-19 in the city is also not to be underestimated due to their characteristics of more frequent occurrence and less detection and control. In our dataset, 22.22% of small-scale events have an incidence of more than 0.8. In contrast, the incidence of most large-scale events is less than 0.4. Gathering scenes such as "Meal" and "Family" occur in densely populated private or small public places have the highest incidence. We further designed a model of epidemic transmission triggered by crowd gathering events and simulated the impact of crowd gathering events on the overall epidemic situation in the city. The simulation results showed that the number of patients will be drastically reduced if the scale and the density of crowds gathering are halved. It indicated that crowd gatherings should be strictly controlled on a small scale. In addition, it showed that the model well reproduce the epidemic spread after crowd gathering events better than does the original SIER model and could be applied to epidemic prediction after sudden gathering events.

Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations.

BACKGROUND: With the spread of COVID-19, the time-series prediction of COVID-19 has become a research hotspot. Unlike previous epidemics, COVID-19 has a new pattern of long-time series, large fluctuations, and multiple peaks. Traditional dynamical models are limited to curves with short-time series, single peak, smoothness, and symmetry. Secondly, most of these models have unknown parameters, which bring greater ambiguity and uncertainty. There are still major shortcomings in the integration of multiple factors, such as human interventions, environmental factors, and transmission mechanisms. METHODS: A dynamical model with only infected humans and removed humans was established. Then the process of COVID-19 spread was segmented using a local smoother. The change of infection rate at different stages was quantified using the continuous and periodic Logistic growth function to quantitatively describe the comprehensive effects of natural and human factors. Then, a non-linear variable and NO2 concentrations were introduced to qualify the number of people who have been prevented from infection through human interventions. RESULTS: The experiments and analysis showed the R2 of fitting for the US, UK, India, Brazil, Russia, and Germany was 0.841, 0.977, 0.974, 0.659, 0.992, and 0.753, respectively. The prediction accuracy of the US, UK, India, Brazil, Russia, and Germany in October was 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively. CONCLUSION: The model can not only better describe the effects of human interventions but also better simulate the temporal evolution of COVID-19 with local fluctuations and multiple peaks, which can provide valuable assistant decision-making information.

Prediction and Analysis of COVID-19 Epidemic Situation via Modified SEIR Model with Asymptomatic Infection

This paper deals with the prediction and analysis of COVID-19 epidemic situation based on a modified SEIR model with asymptomatic infection. First, by considering the self-isolation and asymptomatic infection, a modified SEIR model is proposed to predict and evaluate the epidemic situation of COVID-19 in Hubei Province, China. Then, based on the daily data reported by the Health Commission of Hubei Province, the modified SEIR model is solved numerically, and the parameters of the modified model are inverted by the least square method. Third, based on the modified model, the epidemic situation of COVID-19 in Hubei Province is predicted and verified. The simulation results show that the modified SEIR model is significant and reliable to describe the spread property of the COVID-19, thereby providing a potential theoretical support for the decision-making of epidemic prevention and control in the future. © 2021 IEEE.

Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis.

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics.

Using Mathematical Model to Analyze COVID-19 Spreading

Since the first case of Coronavirus Disease 2019 (COVID-19) was discovered in Wuhan, Hubei, China, on December 31, 2019, the disease has spread globally at an unimaginable speed. COVID-19 has taken a huge toll on the society and the economy, and everyone is looking forward to its end. In this work, we established a mathematical model of COVID-19 epidemic development. First, we obtained a differential equation to describe the spreading of COVID-19: , in which is the total number of patients who are infected by COVID-19 at time . There are three parameters in this equation: the spreading coefficient , which is the average number of people infected by an unquarantined patient in a unit time;the average quarantine ratio , which is the number of quarantined patients divided by the total number of patients;and the incubation period , which is the time lapse between infection and exhibition of symptoms. In addition, we have written a Python program according to our equation, and have further used our program to analyze the COVID-19 epidemic development in various places around the world, including China, Western Europe, Latin America and Caribbean, Southern Asia, and the entire world. Through numerical fitting, we have obtained the values of the spreading coefficient and the isolation ratio for these places around the world, and predicted the development of the epidemic using these parameters we obtained. In order to ensure data consistency, we have used the data from COVID-19 case reports from Johns Hopkins University. We found that using the parameters we obtained, our calculated curves of fit the actually reported values very well, and we were able to accurately predict the values of in the near future. Lastly, we calculated the value (the number of infected persons per patient at the beginning of the epidemic) to be 2.94 1/45.88, which is consistent with the current estimated value of . In summary, our results serve as a reliable guideline to understand the spreading of COVID-19 and to predict the future outcome of this epidemic, and can be provided as a reference for the government to formulate policies. © 2021 ACM.

Using prediction polling to harness collective intelligence for disease forecasting.

BACKGROUND: The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking. METHODS: We report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 volunteer participants competed over 15 months to make forecasts on 61 questions with a total of 217 possible answers regarding 19 diseases. RESULTS: Consistent with the "wisdom of crowds" phenomenon, we found that crowd forecasts aggregated using best-practice adaptive algorithms are well-calibrated, accurate, timely, and outperform all individual forecasters. CONCLUSIONS: Crowd forecasting efforts in public health may be a useful addition to traditional disease surveillance, modeling, and other approaches to evidence-based decision making for infectious disease outbreaks.

Artificial Intelligence for COVID-19: A Systematic Review.

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

Analysis and Prediction of COVID-19 Pandemic in Bangladesh by Using ANFIS and LSTM Network.

The dangerously contagious virus named "COVID-19" has struck the world strong and has locked down billions of people in their homes to stop the further spread. All the researchers and scientists in various fields are continually developing a vaccine and prevention methods to aid the world from this challenging situation. However, a reliable prediction of the epidemic may help control this contiguous disease until the cure is available. The machine learning techniques are one of the frontiers in predicting this outbreak's future trend and behavior. Our research is focused on finding a suitable machine learning algorithm that can predict the COVID-19 daily new cases with higher accuracy. This research has used the adaptive neuro-fuzzy inference system (ANFIS) and the long short-term memory (LSTM) to foresee the newly infected cases in Bangladesh. We have compared both the experiments' results, and it can be forenamed that LSTM has shown more satisfactory results. Upon study and testing on several models, we have shown that LSTM works better on a scenario-based model for Bangladesh with mean absolute percentage error (MAPE)-4.51, root-mean-square error (RMSE)-6.55, and correlation coefficient-0.75. This study is expected to shed light on COVID-19 prediction models for researchers working with machine learning techniques and avoid proven failures, especially for small imprecise datasets.

Surveillance of Wastewater for Early Epidemic Prediction (SWEEP): Environmental and health security perspectives in the post COVID-19 Anthropocene.

The present work summarizes the major research findings related to wastewater-based epidemiology (WBE) study of COVID-19 and puts forward a conceptual framework, termed as "Surveillance of Wastewater for Early Epidemic Prediction (SWEEP)" for implementation of WBE. SWEEP framework is likely to tackle few practical issues related to WBE and simultaneously proposes refinements to the approach for better outcome and efficiency to save precious lives around the globe. It is observed that the present pandemic offers an opportunity for SWEEP to get included in routine urban water management to put the humankind at front to stop such pandemic in future or at least be prepared to fight against it. With global collaboration, SWEEP can be fine-tuned to meet diverse needs, making the present and future generations resilient to future viral outbreaks. Recent WBE studies conducted to check for the presence of SARS-CoV-2 in wastewater revealed that raw sewage samples tested positive to PCR-based assays while the treated samples showed absence of viral titers. Moreover, the lockdown had a positive impact on decreasing the viral loading in sewage. The proposed SWEEP protocol has an advantage over testifying individuals for predicting the stage of pandemic.