SEIHCRD Model for COVID-19 Spread Scenarios,Disease Predictions and Estimates the Basic Reproduction Number,Case Fatality Rate,Hospital,and ICU Beds Requirement

We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and results.We have studiedCOVID-19 cases of six countries,where the impact of this disease in the highest are Brazil,India,Italy,Spain,the United Kingdom,and the United States.After estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic evolution.Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category scenario.Themodel calculates two types of Case fatality rate one is CFR daily,and the other is total CFR.The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection.The SEIHCRD model outperforms the classic ARXmodel and the ARIMA model.RMSE,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target diagrams.The result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.

EWMA control chart based on its first hitting time and coronavirus alert levels for monitoring symmetric COVID-19 cases

Objective:To define the alert levels for the total number of COVID-19 cases derived by using quantile functions to monitor COVID-19 outbreaks via an exponentially weighted moving-average(EWMA)control chart based on the first hitting time of the total number of COVID-19 cases following a symmetric logistic growth curve.Methods:The cumulative distribution function of the time for the total number of COVID-19 cases was used to construct a quantile function for classifying COVID-19 alert levels.The EWMA control chart control limits for monitoring a COVID-19 outbreak were formulated by applying the delta method and the sample mean and variance method.Samples were selected from countries and region including Thailand,Singapore,Vietnam,and Hong Kong to generate the total number of COVID-19 cases from February 15,2020 to December 16,2020,all of which followed symmetric patterns.A comparison of the two methods was made by applying them to a EWMA control chart based on the first hitting time for monitoring the COVID-19 outbreak in the sampled countries and region.Results:The optimal first hitting times for the EWMA control chart for monitoring COVID-19 outbreaks in Thailand,Singapore,Vietnam,and Hong Kong were approximately 280,208,286,and 298 days,respectively.Conclusions:The findings show that the sample mean and variance method can detect the first hitting time better than the delta method.Moreover,the COVID-19 alert levels can be defined into four stages for monitoring COVID-19 situation,which help the authorities to enact policies that monitor,control,and protect the population from a COVID-19 outbreak.

Temporal Dynamics in COVID-19 Transmission: Case of Some African Countries

Coronavirus disease 2019 (COVID-19) has become a global threat to public health and economy. The potential burden of this pandemic in developing world, particularly the African countries, is much concerning. With the aim of providing supporting evidence for decision making, this paper studies the dynamics of COVID-19 transmission through time in selected African countries. Time-dependent reproduction number (Rt) is one of the tools employed to quantify temporal dynamics of the disease. Pattern of the estimated reproduction numbers showed that transmissibility of the disease has been fluctuating through time in most of the countries included in this study. In few countries such as South Africa and Democratic Republic of Congo (DRC), these estimates dropped quickly and stayed stable, but greater than 1, for months. Regardless of their variability through time, the estimated reproduction numbers remain greater than or nearly equal to 1 in all countries. Another Statistical model used in this study, namely Autoregressive Conditional Poisson (ACP) model, showed that expected (mean) number of new cases is significantly dependent on short range change in new cases in all countries. In countries where there is no persistent trend in new cases, current mean number of new cases (on day t) depend on both previous observation and previous mean (day t - 1). In countries where there is continued trend in new cases, current mean is more affected by number of new cases on preceding day.

How does Covid-19 affect global equity markets?

This study applies OLS,panel regression and Granger causality test to investigate the impact of the Coronavirus disease 2019(Covid-19)outbreak on the global equity markets during the early stage of the pandemic.We find that the Covid-19 outbreak has a significant negative impact on the overall equity index return of the eight economies even at 0.1%significance level.Furthermore,the pandemic has a more significant impact on the European countries than on the East Asian economies.The results have three main implications.Firstly,policy makers should react fast to mitigate the impact of a crisis.Secondly,investors should be aware of an outbreak of disease or other risks and adjust their investments accordingly.Furthermore,the Covid-19 outbreak results in a shift of power from the west to the east.

Mathematical Model of the Spread of the Coronavirus Disease 2019 (COVID-19) in Burkina Faso

In this paper, we develop a mathematical model of the COVID-19 pandemic in Burkina Faso. We use real data from Burkina Faso National Health Commission against COVID-19 to predict the dynamic of the disease and also the cumulative number of reported cases. We use public policies in model in order to reduce the contact rate, this allows to show how the reduction of the daily report of infectious cases goes, so we would like to draw the attention of decision makers for a rapid treatment of reported cases.

Suspected Close Contacts as the Pilot Indicator of the Growth Trend of Confirmed Population During the COVID-19 Pandemic: A Simulation Approach

Regarding to the actual situation of the new coronavirus disease 2019 epidemic,social factors should be taken into account and the increasing growth trend of confirmed populations needs to be explained.A proper model needs to be established,not only to simulate the epidemic,but also to evaluate the future epidemic situation and find a pilot indicator for the outbreak.The original susceptible-infectious-recover model is modified into the susceptible-infectious-quarantine-confirm-recover combined with social factors(SIDCRL)model,which combines the natural transmission with social factors such as external interventions and isolation.The numerical simulation method is used to imitate the change curve of the cumulative number of the confirmed cases and the number of cured patients.Furthermore,we investigate the relationship between the suspected close contacts(SCC)and the final outcome of the growth trend of confirmed cases with a simulation approach.This article selects four representative countries,that is,China,South Korea,Italy,and the United States,and gives separate numerical simulations.The simulation results of the model fit the actual situation of the epidemic development and reasonable predictions are made.In addition,it is analyzed that the increasing number of SCC contributes to the epidemic outbreak and the prediction of the United States based on the population of the SCC highlights the importance of external intervention and active prevention measures.The simulation of the model verifies its reliability and stresses that observable variable SCC can be taken as a pilot indicator of the coronavirus disease 2019 pandemic.

Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan

Background The ongoing coronavirus disease 2019(COVID-19)pandemic caused by the severe acute respiratory syndrome-coronavirus 2(SARS-CoV-2)and the Omicron variant presents a formidable challenge for control and prevention worldwide,especially for low-and middle-income countries(LMICs).Hence,taking Kazakhstan and Pakistan as examples,this study aims to explore COVID-19 transmission with the Omicron variant at different contact,quarantine and test rates.Methods A disease dynamic model was applied,the population was segmented,and three time stages for Omicron transmission were established:the initial outbreak,a period of stabilization,and a second outbreak.The impact of population contact,quarantine and testing on the disease are analyzed in five scenarios to analysis their impacts on the disease.Four statistical metrics are employed to quantify the model’s performance,including the correlation coefficient(CC),normalized absolute error,normalized root mean square error and distance between indices of simulation and observation(DISO).Results Our model has high performance in simulating COVID-19 transmission in Kazakhstan and Pakistan with high CC values greater than 0.9 and DISO values less than 0.5.Compared with the present measures(baseline),decreasing(increasing)the contact rates or increasing(decreasing)the quarantined rates can reduce(increase)the peak values of daily new cases and forward(delay)the peak value times(decreasing 842 and forward 2 days for Kazakhstan).The impact of the test rates on the disease are weak.When the start time of stage Ⅱ is 6 days,the daily new cases are more than 8 and 5 times the rate for Kazakhstan and Pakistan,respectively(29,573 vs.3259;7398 vs.1108).The impact of the start times of stageⅢon the disease are contradictory to those of stageⅡ.Conclusions For the two LMICs,Kazakhstan and Pakistan,stronger control and prevention measures can be more effective in combating COVID-19.Therefore,to reduce Omicron transmission,strict management of population movement should be employed.Moreover,the timely application of these strategies also plays a key role in disease control.

粒子群可拓的新冠肺炎建模与仿真

本文提出了一种基于粒子群可拓神经网络预测模型。根据国外近段时间每日新增新冠肺炎确诊人数,利用可拓神经网络模型对国外日新增新冠肺炎确诊人数进行预测,并利用粒子群算法(PSO)对权值进行优化,最后与LSSVM、ABC-LSSVM及PSO-LSSVM模型进行比较。结果表明:采用文中提出的粒子群可拓神经网络模型拟合效果较好,精度较高,性能优于其他三种模型,适用于COVID-19的疫情研究。

An evaluation of mathematical models for the outbreak of COVID-19

Mathematical modelling performs a vital part in estimating and controlling the recent outbreak of coronavirus disease 2019(COVID-19).In this epidemic,most countries impose severe intervention measures to contain the spread of COVID-19.The policymakers are forced to make difficult decisions to leverage between health and economic development.How and when tomake clinical and public health decisions in an epidemic situation is a challenging question.The most appropriate solution is based on scientific evidence,which is mainly dependent on data and models.So one of the most critical problems during this crisis is whether we can develop reliable epidemiological models to forecast the evolution of the virus and estimate the effectiveness of various intervention measures and their impacts on the economy.There are numerous types of mathematical model for epidemiological diseases.In this paper,we present some critical reviews on mathematical models for the outbreak of COVID-19.Some elementary models are presented as an initial formulation for an epidemic.We give some basic concepts,notations,and foundation for epidemiological modelling.More related works are also introduced and evaluated by considering epidemiological features such as disease tendency,latent effects,susceptibility,basic reproduction numbers,asymptomatic infections,herd immunity,and impact of the interventions.

湖北省新冠肺炎确诊人数的建模与预测分析

为了建立湖北省新型冠状病毒肺炎(简称“新冠肺炎”)疫情确诊人数的时间序列模型,预测确诊人数的变化趋势,为政府制定相关防疫政策提供依据。本文分别收集2020年1月20日—2020年3月4日的湖北省新冠肺炎确诊人数的每日数据,经过数据预处理、模型识别、参数估计、模型诊断和优化等分析手段,建立相应的时间序列模型,并对模型给出合理解释。利用构建的最优模型对湖北省新冠肺炎确诊人数进行6期预测分析,并提出相应建议。结果显示,湖北省新冠肺炎确诊人数可用ARIMA(1,1,1)模型进行拟合。由此得出,利用所得模型能合理地解释数据,并预测短期内湖北省新冠肺炎确诊人数,为制定相关防疫政策提供建议和依据。

Power-law growth of the COVID-19 fatality incidents in Europe

We report on the dynamic scaling of the diffusion growth phase of the COVID-19 epidemic in Europe.During this initial diffusion stage,the European countries implemented unprecedented mitigation polices to delay and suppress the disease contagion,although not in a uniform way or timing.Despite this diversity,we find that the reported fatality cases grow following a power law in all European countries we studied.The difference among countries is the value of the power-law exponent 3.5<α<8.0.This common attribute can prove a practical diagnostic tool,allowing reasonable predictions for the growth rate from very early data at a country level.We propose a model for the disease-causing interactions,based on a mechanism of human decisions and risk taking in interpersonal associations.The model describes the observed statistical distribution and contributes to the discussion on basic assumptions for homogeneous mixing or for a network perspective in epidemiological studies of COVID-19.

自回归求和滑动平均(ARIMA)模型在全球新型冠状病毒肺炎发病人数预测中的应用

目的应用自回归求和滑动平均(autoregressive integrated moving average,ARIMA)模型对全球新型冠状病毒肺炎(coronavirus disease 2019,COVID-19)发病人数进行预测,为各国提出的防控策略与措施提供参考和评价依据。方法收集2020年2月22日―3月19日各国(意大利、西班牙、德国、法国等)COVID-19每日累计确诊人数,用SPSS 17.0和R 3.6.1软件拟合ARIMA模型,对5日前数据进行回带评价拟合效果,同时利用该模型预测各国后10日数据。结果 ARIMA模型预测值和实际值动态趋势基本一致,实际值在预测值的95%CI内。结论 ARIMA模型能够较好的对全球COVID-19发病人数进行预测,在指导疫情防控方面有实际意义。