Number Of Infected Cases Research Articles

A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks

COVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than 4%. The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2.

The effect of aerobic exercise on immune biomarkers and symptoms severity and progression in patients with COVID-19: A randomized control trial

BackgroundThe World Health Organization in March 2020 has announced that COVID-19 is a world pandemic because the number of infected cases increases rapidly. however, there are several available vaccines, their protection is limited to a certain period. Thus, the role of modalities that improve immune functions should be performed to counter COVID-19 viral load and decrease mortality rates. ObjectiveTo investigate the effect of aerobic exercise on immune biomarkers, disease severity, and progression in patients with COVID-19. DesignA randomized controlled study. ParticipantsThirty patients with COVID-19 participated in this study. Participants’ age ranged from 24 to 45 years old. Participants had a mild or moderate COVID-19. Participants were assigned randomly into two groups, exercise and control groups. There were two main dependent variables including blood immune markers and severity of respiratory symptoms. InterventionsAll participants performed 2 weeks of moderate-intensity aerobic exercise for 40 min/session, 3 sessions/week. The measurements were performed at baseline, and after 2-weeks. ResultsAt baseline measurements, there were non-significant differences between both groups in the Wisconsin scale total score, Leucocytes, Lymphocytes, Interleukin-6, Interleukin-10, Immunoglobulin-A, and TNF-α (P > .05). After the intervention, the Wisconsin scale (patient-oriented illness-specific quality-of-life) total score significantly decreased in the intervention group (P < .05); while, Leucocytes, Lymphocytes, and Immunoglobulin-A significantly increased in the intervention group (P < .05). ConclusionThe current study indicated that 2 weeks of moderate-intensity aerobic exercise decreased the severity and progression of COVID-19 associated disorders and quality of life. Also, a 2-weeks of aerobic exercise positively affected immune function by increasing the amounts of Leucocytes, Lymphocytes, Immunoglobulin A.

COVID-19 Prevention: Role of Activated Carbon

Recently, Coronavirus Disease 2019 (COVID-19) has brought the whole world into a pandemic condition, where the number of infected cases and deaths is exponentially high. A number of vaccines are available for this novel virus, but these are in the preliminary stage and are also not available to everyone. As the virus is very contagious, protection and prevention are the best way to survive and get rid of this disease. The virus affects the human body by entering through the nose, mouth, and eyes, so face protection with an appropriate mask is highly advisable. Combined masks made with activated carbon (AC) can effectively adsorb the virus because of its high surface area and broad functional groups. Such combined masks can also control coronavirus transmission by capturing harmful gases and smoke as they help in decreasing the spread of the virus

Market volatility and illiquidity during the COVID-19 outbreak: Evidence from the Saudi stock exchange through the wavelet coherence approaches

The aim of this paper is to examine the explanatory power of realized volatility on the illiquidity in Saudi stock market during the COVID-19outbreak. To achieve this objective, we consider the Wavelet Coherence approaches as empirical tools to investigate the combined effect of realized volatility and COVID-19 counts on the market illiquidity across frequencies and over time space by taking in account the number of infected cases in Saudi Arabia and over the World, and the number of death cases in Saudi Arabia as well as over the World. Our study reaches two main findings. First, the preliminary results reported by the ARDL bound test as a benchmark model showed significant long-run and short-run effects of the market volatility on illiquidity in contemporaneous and lagged manner. Second, the wavelet coherence analysis tools exhibited important results: (i) the wavelet coherency between illiquidity ratio and realized volatility in Saudi Arabia appear highly pronounced over all time horizons. (ii) PWC plots showed a significant mutual effect between liquidity risk and realized volatility when eliminating the effect of local COVID-19 cases. (iii) MWC plots highlighted that the response of the market illiquidity index to both the amplification in confirmed local cases (resp. international confirmed cases) and the stock market volatility appear significant in the short and middle horizons.

Resilience of Milk Supply Chains during and after the COVID-19 Crisis in Latvia

Abstract The impacts of the COVID-19 pandemic on national economies are characterized as the worst economic turmoil in recent years, as economic growth slowed to the level of the 2008 global financial crisis. At the end of 2019 and the beginning of 2020, economic growth surveys did not project that the novel virus could have a negative impact on economic growth. As a result of the rapid spread of the virus and the increase in the number of infection cases, national governments took strict measures to limit the spread of the virus, which led to a significant deterioration in the economic situation in all industries of the economy. Agriculture plays an important role in providing food to the country’s population, yet the pandemic made a negative impact on sales volumes and prices in the domestic and international markets. The research aims to assess the impacts of the pandemic on milk supply chains in Latvia and come up with solutions to stabilizing the situation in the dairy sector. The national restrictions to combat the pandemic also hampered the dairy sector concerning the sale of raw milk and finished products in foreign markets, thereby leading to overproduction and stockpiling of the finished products, price dumping and a significant decrease in milk purchase prices to or below the production cost level for many dairy farms. Although there is no indication that the pandemic would lead to a decrease in the numbers of livestock and farms or output in early 2021, it is too early to assess the overall impact of the pandemic on dairy farming, as uncertainty over the duration of pandemic restrictions is a major risk to farm survival. The operation of the dairy sector was stabilized by means of the support provided by the government, as well as the farms’ accumulated financial and feed resources. In order to identify future challenges for milk supply chains, the research performed an analysis of operational risks for milk producers and processors to identify the most important risks and design recommendations for their elimination. The longer there is uncertainty about the return of the dairy sector to pre-pandemic output levels, the more likely it is that the pandemic is going to lead to a decrease in the numbers of dairy livestock and farms.

Social Stigma of COVID-19: A Semiotic Analysis of WHO Campaign Posters

COVID-19 has been reported to be risen in numbers of infected cases and deaths. The massive report by media and social network which focus on the spreading and infection may affect not only physical health but also individual’s and general population’s mental health, isolation and stigma. To eradicate COVID-19-related stigma and discrimination perpetuated by both individual and group of people, WHO exhibits some anti-stigma campaign posters. This study employs qualitative method to acquire deep investigation of meaning and to involve the social context. Thus, by using Roland Barthes’s semiotic approach, analyzing signifiers and signifieds, this study was aimed to unmask both denotative and connotative meanings of the stigma embed within the six health campaign posters of COVID-19 by Southeast Asia WHO. The analysis was focused not only on the verbal sign of posters (linguistic text), but also its relation to their visual sign (imagery messages). From the analysis of the two sign systems of posters, the result shows that the six posters connote acts of discriminatory behaviours, stigmatization, stereotype and blaming. Through the posters, WHO propagates people to work together to fight COVID-19 and to bring out the best humanity, to have better awareness and positive attitudes and appeals governments, citizens, media, key influencers of communities to have a role in preventing and to stop stigma surrounding in South-Asia and specifically in Indonesia which becomes the target of the poster viewers during the pandemic. Those messages are connoted through different font colors and sizes and the illustration on each poster.

COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach

COVID-19 has sparked a worldwide pandemic, with the number of infected cases and deaths rising on a regular basis. Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. Thus, if we can accurately forecast the trend of cases globally, the spread of the pandemic can be controlled. In this study, a hybrid CNN-LSTM model was developed on a time-series dataset to forecast the number of confirmed cases of COVID-19. The proposed model was evaluated and compared with 17 baseline models on test and forecast data. The primary finding of this research is that the proposed CNN-LSTM model outperformed them all, with the lowest average MAPE, RMSE, and RRMSE values on both test and forecast data. Conclusively, our experimental results show that, while standalone CNN and LSTM models provide acceptable and efficient forecasting performance for the confirmed COVID-19 cases time series, combining both models in the proposed CNN-LSTM encoder-decoder structure provides a significant boost in forecasting performance. Furthermore, we demonstrated that the suggested model produced satisfactory predicting results even with a small amount of data.

TIME SERIES ANALYSIS AND FORECASTING OF CoViD-19 DATA: CASE OF THE WEST AFRICAN ECONOMIC AND MONETARY UNION AREA

In this paper, we first analyze different time series CoViD-19 data by exploring confirmed, death and recovered daily reports from January 22, 2020 to May 30, 2021 in the West African Economic and Monetary Union (WAEMU) Area. We then use the Prophet model to forecast the total number of infected cases from May 31, 2021 to July 29, 2021 by providing graphics and by giving tables containing the total number of infected cases from July 25 to 29, 2021 for each country in the zone.

Real-Time Prediction of the COVID-19 Epidemic in Thailand using Simple Model-Free Method and Time Series Regression Model

The novel coronavirus 2019 (COVID-19) pandemic was declared a global health crisis. The real-time accurate and predictive model of the number of infected cases could help inform the government of providing medical assistance and public health decision-making. This work is to model the ongoing COVID-19 spread in Thailand during the 1st and 2nd phases of the pandemic using the simple but powerful method based on the model-free and time series regression models. By employing the curve fitting, the model-free method using the logistic function, hyperbolic tangent function, and Gaussian function was applied to predict the number of newly infected patients and accumulate the total number of cases, including peak and viral cessation (ending) date. Alternatively, with a significant time-lag of historical data input, the regression model predicts those parameters from 1-day-ahead to 1-month-ahead. To obtain optimal prediction models, the parameters of the model-free method are fine-tuned through the genetic algorithm, whereas the generalized least squares update the parameters of the regression model. Assuming the future trend continues to follow the past pattern, the expected total number of patients is approximately 2,689 - 3,000 cases. The estimated viral cessation dates are May 2, 2020 (using Gaussian function), May 4, 2020 (using a hyperbolic function), and June 5, 2020 (using a logistic function), whereas the peak time occurred on April 5, 2020. Moreover, the model-free method performs well for long-term prediction, whereas the regression model is suitable for short-term prediction. Furthermore, the performances of the regression models yield a highly accurate forecast with lower RMSE and higher R2 up to 1-week-ahead. HIGHLIGHTS COVID-19 model for Thailand during the first and second phases of the epidemic The model-free method using the logistic function, hyperbolic tangent function, and Gaussian function applied to predict the basic measures of the outbreak Regression model predicts those measures from one-day-ahead to one-month-ahead The parameters of the model-free method are fine-tuned through the genetic algorithm GRAPHICAL ABSTRACT

Combating the COVID-19 Pandemic: Experiences of the First Wave From Nepal.

Unprecedented and unforeseen highly infectious Coronavirus Disease 2019 (COVID-19) has become a significant public health concern for most of the countries worldwide, including Nepal, and it is spreading rapidly. Undoubtedly, every nation has taken maximum initiative measures to break the transmission chain of the virus. This review presents a retrospective analysis of the COVID-19 pandemic in Nepal, analyzing the actions taken by the Government of Nepal (GoN) to inform future decisions. Data used in this article were extracted from relevant reports and websites of the Ministry of Health and Population (MoHP) of Nepal and the WHO. As of January 22, 2021, the highest numbers of cases were reported in the megacity of the hilly region, Kathmandu district (population = 1,744,240), and Bagmati province. The cured and death rates of the disease among the tested population are ~98.00 and ~0.74%, respectively. Higher numbers of infected cases were observed in the age group 21–30, with an overall male to female death ratio of 2.33. With suggestions and recommendations from high-level coordination committees and experts, GoN has enacted several measures: promoting universal personal protection, physical distancing, localized lockdowns, travel restrictions, isolation, and selective quarantine. In addition, GoN formulated and distributed several guidelines/protocols for managing COVID-19 patients and vaccination programs. Despite robust preventive efforts by GoN, pandemic scenario in Nepal is, yet, to be controlled completely. This review could be helpful for the current and future effective outbreak preparedness, responses, and management of the pandemic situations and prepare necessary strategies, especially in countries with similar socio-cultural and economic status.

Monitoring and Analysis of COVID-19 Pandemic: The Need for an Empirical Approach

The current worldwide pandemic produced by coronavirus disease 2019 (COVID-19) has changed the paradigm of mathematical epidemiology due to the high number of unknowns of this new disease. Thus, the empirical approach has emerged as a robust tool to analyze the actual situation carried by the countries and also allows us to predict the incoming scenarios. In this paper, we propose three empirical indexes to estimate the state of the pandemic. These indexes quantify both the propagation and the number of estimated cases, allowing us to accurately determine the real risk of a country. We have calculated these indexes' evolution for several European countries. Risk diagrams are introduced as a tool to visualize the evolution of a country and evaluate its current risk as a function of the number of contagious individuals and the empiric reproduction number. Risk diagrams at the regional level are useful to observe heterogeneity on COVID-19 penetration and spreading in some countries, which is essential during deconfinement processes. During the pandemic, there have been significant differences seen in countries reporting case criterion and detection capacity. Therefore, we have introduced estimations about the real number of infectious cases that allows us to have a broader view and to better estimate the risk. These diagrams and indexes have been successfully used for the monitoring of European countries and regions during the COVID-19 pandemic.

Epidemiological study on severe acute respiratory syndrome coronavirus 2 virus transmission network in northeast China.

In this study, corona virus disease 2019 (COVID-19) transmission networks were built to analyze the epidemic situation of COVID-19 in Liaoning and Jilin provinces in early 2020. We explore the characteristics of the spread of COVID-19, and put forward effective recommendations for epidemic prevention and control. We collected demographic characteristics, exposure history, and course of action of COVID-19 cases. We described the demographic and case characteristics of these cases to show the basic characteristics of COVID-19 cases in both provinces. Combined with the spatial analysis of confirmed cases, the distribution law of the number of confirmed cases in different regions was analyzed. We exhibit the relationship among COVID-19 cases with a transmission network. The transmission characteristics of COVID-19 were analyzed through the transmission network. Mainly cases in Liaoning and Jilin provinces were imported cases from other provinces and the vast majority of these cases were related to Hubei province. The number of confirmed cases in different regions was positively correlated with their GDP and population. The main clinical symptoms of the cases were fever. Judge from the transmission network relationship between the 2 provinces, the transmission chain in Liaoning province contains fewer cases than that in Jilin province. The main transmission routes of the local cases in the 2 provinces were the family members, and the infection of the imported cases were mainly occurred in public places. It was estimated that the unidentified asymptomatic infected cases in the 2 provinces account for approximately 7.3% of the total number of infected cases. The length of the transmission chain suggests that the spread of COVID-19 can be effectively controlled with effective prevention measures.

COVID-19 in tuberculosis in patients

Coronavirus Disease 2019 (COVID‐19) has become a pandemic and the number of infected cases continues to rise. Millions have been laboratory‐confirmed cases and reported worldwide, with a mortality rate of 4.6%. To be the best of our knowledge, study not has yet evaluated the impact of tuberculosis on COVID‐19 severity and mortality in India. Therefore, we performed a study to assess whether biochemical values in tuberculosis is more deranged when compared to the person who is not having the tuberculosis and suffering from the infection of COVID‐19.

Spatial network based model forecasting transmission and control of COVID-19

The SARS-CoV-2 driven infectious novel coronavirus disease (COVID-19) has been declared a pandemic by its brutal impact on the world in terms of loss on human life, health, economy, and other crucial resources. To explore more about its aspects, we adopted the SEIRD (Susceptible–Exposed–Infected–Recovered–Death) pandemic spread with a time delay on the heterogeneous population and geography in this work. Focusing on the spatial heterogeneity, epidemic spread on the framework of modeling that incorporates population movement within and across the boundaries is studied. The entire population of interest in a region is divided into small distinct geographical sub regions, which interact using migration networks across boundaries. Utilizing the time delay differential equations based model estimations, we analyzed the spread dynamics of disease in India. The numerical outcomes from the model are validated using real time available data for COVID-19 cases. Based on the developed model in the framework of the recent data, we verified total infection cases in India considering the effect of nationwide lockdown at the onset of the pandemic and its unlocking by what seemed to be the end of the first wave. We have forecasted the total number of infection cases in two extreme situations of nationwide no lockdown and strict lockdown scenario. We expect that in future for any change in the key parameters, due to the regional differences, predictions will lie within the bounds of the above mentioned extreme plots. We computed the approximate peak infection in forwarding time and relative timespan when disease outspread halts. The most crucial parameter, the time-dependent generalization of the basic reproduction number, has been estimated. The impact of the social distancing and restricted movement measures that are crucial to contain the pandemic spread has been extensively studied by considering no lockdown scenario. Our model suggests that attaining a reduction in the contact rate between susceptible and infected individuals by practicing strict social distancing is one of the most effective control measures to manage COVID-19 spread in India. The cases can further decrease if social distancing is followed in conjunction with restricted movement.

PENERAPAN DATA MINING TERHADAP DATA COVID-19 MENGGUNAKAN ALGORITMA KLASIFIKASI

Coronavirus 2019 or more commonly referred to as COVID-19 is a type of virus that attacks the respiratory system. Until now the number of spread and the number of deaths caused by this virus continues to increase. As of April 21, 2020, based on data from the WHO, the total number of cases infected with this virus reached 2,397,217 with 162 deaths from all over the world. For South Korea itself, as of March 21, 2020, the total number of infected cases was 10,683 with a total of 237 deaths. In this study, researchers conducted data processing on the spread of COVID-19 in South Korea with Rapidminer using a classification algorithm, namely Naïve Bayes, C4.5, and K-Nearest Neighbor by performing the stages of selection, preprocessing, transfotmating, data mining and interpretation or evaluating the quality of the best accuracy of 80.79% with AUC of 0.881 achieved by the Naïve Bayes algorithm. The distribution of the data found that the influential attribute of the isolated class factor from the patient contained in the sex attribute where more women experienced isolation. Keywords— COVID-19, data mining, classification, C4.5, Naïve Bayes, K-NN

COVID-19 and the economy: job loss and economic shutdown

The COVID-19 pandemic devastated several countries across the globe, having economic, social, and health impacts on all nations, some more than others with over 31 million infection cases and almost 1 million deaths as a result of the pandemic. This has caused a ripple effect on the agricultural sector as seen by the food scarcity and the increase in food spoilage ravaging the sector as many local farmers are unable to transport their produce to consumers. The pandemic has also confronted the hospitality industry with an unprecedented challenge as a result of the temporary closure they had to experience due to the lockdown imposed in affected countries. The transport sector is not excluded though to the positive side as there has been a decrease in air pollution within some countries due to the various travel restrictions and bans placed on the movement of people. Another sector seriously hit by the pandemic is the tourism sector as there has been a projected loss of about $1.2 trillion equivalent to 1.5% of the global Gross Domestic Product (GDP). The same goes for the sports sector as major tournaments have either been cancelled or postponed. This paper discusses the impact of COVID-19 on the global economy and its corresponding implication on job loss in various sectors. With the daily increase in the number of infected cases globally, systems and structures which will be able to neutralize the effects of future pandemics need to be put in place and reviewed from time to time.

Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments.

During the COVID-19 pandemic, governments globally had to impose severe contact restriction measures and social mobility limitations in order to limit the exposure of the population to COVID-19. These public health policy decisions were informed by statistical models for infection rates in national populations. In this work, we are interested in modelling the temporal evolution of national-level infection counts for the United Kingdom (UK-Wales, England, Scotland), Germany (GM), Italy (IT), Spain (SP), Japan (JP), Australia (AU) and the United States (US). We model the national-level infection counts for the period January 2020 to January 2021, thus covering both the pre- and post-vaccine roll-out periods, in order to better understand the most reliable model structure for the COVID-19 epidemic growth curve. We achieve this by exploring a variety of stochastic population growth models and comparing their calibration, with respect to in-sample fitting and out-of-sample forecasting, both with and without exposure adjustment, to the most widely used and reported growth model, the Gompertz population model, often referred to in the public health policy discourse during the COVID-19 pandemic. Model risk as we explore it in this work manifests in the inability to adequately capture the behaviour of the disease progression growth rate curve. Therefore, our concept of model risk is formed relative to the standard reference Gompertz model used by decision-makers, and then we can characterise model risk mathematically as having two components: the dispersion of the observation distribution, and the structure of the intensity function over time for cumulative counts of new infections daily (i.e. the force of infection) attributed directly to the COVID-19 pandemic. We also explore how to incorporate in these population models the effect that governmental interventions have had on the number of infected cases. This is achieved through the development of an exposure adjustment to the force of infection comprised of a purpose-built sentiment index, which we construct from various authoritative public health news reporting. The news reporting media we employed were the New York Times, the Guardian, the Telegraph, Reuters global blog, as well as national and international health authorities: the European Centre for Disease Prevention and Control, the United Nations Economic Commission for Europe, the United States Centres for Disease Control and Prevention, and the World Health Organisation. We find that exposure adjustments that incorporate sentiment are better able to calibrate to early stages of infection spread in all countries under study.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Effect evaluation of non-pharmaceutical interventions taken in China to contain the COVID-19 epidemic based on the susceptible-exposed-infected-recovered model

This paper takes confirmed cases of COVID-19 from January 20 to March 18, 2020 as the sample set to establish the susceptible-exposed-infected-recovered (SEIR) model. By evaluating effects of different non-pharmaceutical interventions (NPIs), the research expects to provide references to other countries for formulating corresponding policies. This article divides all non-pharmaceutical interventions into three types according to their different roles. The results show that type-A and type-B non-pharmaceutical interventions both can delay the timing of large-scale infections of the susceptible population, timing of the number of exposed individuals to peak, and timing of peaking of the number of infected cases, as well as decrease the peak number of exposed cases. Moreover, type-B non-pharmaceutical interventions have more significant effects on susceptible and exposed populations. Type-C non-pharmaceutical interventions for improving the recovery rate of patients are able to effectively reduce the peak number of patients, greatly decrease the slope of the curve for the number of infected cases, substantially improve the recovery rate, and lower the mortality rate; however, these non-pharmaceutical interventions do not greatly delay the timing of the number of infected cases to peak. And based on the above analysis, we proposed some suggestions.

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers.

BackgroundThe outbreak of COVID-19 has been defined by the World Health Organization as a pandemic, and containment depends on traditional public health measures. However, the explosive growth of the number of infected cases in a short period of time has caused tremendous pressure on medical systems. Adequate isolation facilities are essential to control outbreaks, so this study aims to quickly estimate the demand and number of isolation beds.MethodsWe established a discrete simulation model for epidemiology. By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease healing time, and (4) population mobility. Finally, a method for predicting the number of isolation beds was summarized through multiple linear regression. This is a city level model that simulates the epidemic situation from the perspective of population mobility.ResultsThrough simulation, we show that the incubation period, response speed and detection capacity of the hospital, disease healing time, degree of population mobility, and infectivity of cured patients have different effects on the infectivity, scale, and duration of the epidemic. Among them, (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease healing time, and (4) population mobility have a significant impact on the demand and number of isolation beds (P <0.05), which agrees with the following regression equation: N = P × (−0.273 + 0.009I + 0.234M + 0.012T1 + 0.015T2) × (1 + V).