Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.

Access to special COVID-19 relief from distress grant and livelihood outcome of livestock farming households in Eastern Cape Province, South Africa

Unexpected events and shocks constitute greater threats to the attainment of zero hunger targets in Africa and the world over, and in the extreme case, lead to total collapse of the global food system and food supply chain. Consequently, this causes significant loss of critical income sources, renders individuals vulnerable, and further deteriorates households' livelihood outcome and welfare state. Therefore, the need for social protection programs to mitigate the impact of distress and unexpected events, as well as extreme occurrences cannot be over emphasized. This research used dataset from the 1499 households captured in the 2021 South African General Household Survey to investigate whether access to a special relief from distress grant has effect on the livestock farming households' food security status in Eastern Cape Province of South Africa. Descriptive statistics, cross-tabulation, a two-sample t-test, a food insecurity experience-based scale technique, and a fractional outcome model were used to analyze the datasets. Based on access to the grant, households in the non-beneficiary group are significantly distinguishable from the beneficiary counterparts, such that the beneficiary households out-performed the non-beneficiary households in the food break-even and food surplus categories. The findings further indicated the possibility of transition of the beneficiary households' population under the transitory food insecurity category to either the chronic food insecurity status or food break-even status, subject to the effectiveness of the food security policy to which they are exposed. The fractional outcome model also indicated that non -metropolitan resident households (p < 0.05), access to the special grant (p < 0.01), access to health facilities (p < 0.01), age of households' heads (p < 0.01), colored, indian and white population groups (both at p < 0.01), as well as access to remittance (p < 0.01) made significant contributions to the households' food security status. The Wald test indicated that access to the special relief grant had a significant effect on the households' food security status in the study area. The study therefore recommends accelerated investments in various social investment programs as sustained responses to expected and unexpected shocks and occurrences to be able to induce progress and realize more resilient food systems.

Predicting the COVID-19 Outbreak

This paper focuses on the use of mathematical modelling of propagation dynamics of infectious diseases. We use the discrete logistic model to propose a simple method to determine the start of coronavirus outbreak. Further, we apply the proposed method on real data of confirmed coronavirus cases from the Kingdom of Saudi Arabia. Our results suggested that the proposed method can be used for raising an alarm of coronavirus outbreak. © 2022 IEEE.

A Statistical Analysis of the Covid-19 Outbreak Data

The exponential model is a commonly used epidemic model for the analysis of initial outbreak data due to an infectious disease. But there have been questions about its validity in practice. This article examines this issue through statistical analysis on 22 countries' initial COVID-19 outbreak data provided by the World Health Organization. For each of 22 countries, a general regression analysis is conducted for the cumulative confirmed cases. Our regression function is a 3-5 piecewise fitted functions which are obtained via regression analysis

Unleashing the potential of online learning in Spain: An econometric analysis

Online learning and training continue gaining momentum worldwide resulting in the reduction of the traditional form of face-to-face education with its temporal and spatial limitations. Online education improves access to education and training, as witnessed during the Covid-19 pandemic. This article focuses on online education adoption in Spain. A representative survey on ICT use in households conducted annually by the Spanish National Institute of Statistics is used to construct a panel database for the years 2008–2020. The first objective is to provide an econometric model for adopting online education using this panel data. Next is to measure the effects of relevant observable individual socioeconomic variables on adoption. A Heckman selection model allows for estimating the impact of gender, age, education, digital skills, habitat, and income. The article also measures the effects of Covid-19 in 2020 on different population groups. The drivers and impediments have the expected signs and plausible sizes. The paper concludes with policy recommendations and suggestions for further research. © 2023 Elsevier Ltd

Dynamic mechanism of eliminating COVID-19 vaccine hesitancy through web search.

This research focuses on the research problem of eliminating COVID-19 vaccine hesitancy through web search. A dynamic model of eliminating COVID-19 vaccine hesitancy through web search is constructed based on the Logistic model, the elimination degree is quantified, the elimination function is defined to analyze the dynamic elimination effect, and the model parameter estimation method is proposed. The numerical solution, process parameters, initial value parameters and stationary point parameters of the model are simulated, respectively, and the mechanism of elimination is deeply analyzed to determine the key time period. Based on the real data of web search and COVID-19 vaccination, data modeling is carried out from two aspects: full sample and segmented sample, and the rationality of the model is verified. On this basis, the model is used to carry out dynamic prediction and verified to have certain medium-term prediction ability. Through this research, the methods of eliminating vaccine hesitancy are enriched, and a new practical idea is provided for eliminating vaccine hesitancy. It also provides a method to predict the quantity of COVID-19 vaccination, provides theoretical guidance for dynamically adjusting the public health policy of the COVID-19, and can provide reference for the vaccination of other vaccines.

A study on the factors that influenced the choice of transport mode before, during, and after the first lockdown in Milan, Italy.

COVID-19 measures implied many changes to travel behaviour and transport mode choice during the pandemic. This study seeks to understand what individual characteristics and travel attributes are related to transport mode choice before, during, and after the first lockdown in Italy. Based on an online survey (carried out in May 2020 in Milan), three multinomial regression models are presented. The results show that and in which measure parameters regarding distance and duration of daily travel are markedly related to transport mode before the lockdown. However, these factors are less significantly associated with the transport mode during and after the lockdown. Meanwhile, factors such as Preferences and Worry about using public transport have more significant relationship with the modal choice during the pandemic. Regarding individual characteristics, women are more likely to use active mode during and after the lockdown. Additionally, two personality traits of Agreeableness, and Openness to experiences are related to transport mode during and after the lockdown, respectively. Overall, this study reveals that in addition to socio-demographic factors, other variables such as worry about using public transport, preference, and personality are associated with the choice of transport mode during the lockdown.

Predicting and explaining absenteeism risk in hospital patients before and during COVID-19.

In order to address one of the most challenging problems in hospital management - patients' absenteeism without prior notice - this study analyses the risk factors associated with this event. To this end, through real data from a hospital located in the North of Portugal, a prediction model previously validated in the literature is used to infer absenteeism risk factors, and an explainable model is proposed, based on a modified CART algorithm. The latter intends to generate a human-interpretable explanation for patient absenteeism, and its implementation is described in detail. Furthermore, given the significant impact, the COVID-19 pandemic had on hospital management, a comparison between patients' profiles upon absenteeism before and during the COVID-19 pandemic situation is performed. Results obtained differ between hospital specialities and time periods meaning that patient profiles on absenteeism change during pandemic periods and within specialities.

FORECASTING the COVID-19 USING the DISCRETE GENERALIZED LOGISTIC MODEL

Using mathematical models to describe the dynamics of infectious-diseases transmission in large communities can help epidemiological scientists to understand different factors affecting epidemics as well as health authorities to decide measures effective for infection prevention. In this study, we use a discrete version of the Generalized Logistic Model (GLM) to describe the spread of the coronavirus disease 2019 (COVID-19) pandemic in Saudi Arabia. We assume that we are operating in discrete time so that the model is represented by a first-order difference equation, unlike time-continuous models, which employ differential equations. Using this model, we forecast COVID-19 spread in Saudi Arabia and we show that the short-term predicted number of cumulative cases is in agreement with the confirmed reports. © 2022

AN EMPIRICAL FORECASTING METHOD FOR EPIDEMIC OUTBREAKS WITH APPLICATION TO COVID-19

In this paper we describe an empirical forecasting method for epidemic outbreaks. It is an iterative process to find possible parameter values for epidemic models to best fit real data. As a demonstration of principle, we used the logistic model, the simplest model in epidemiology, for an experiment of live forecasting. Short-term forecasts can last for five or more days with relative errors consistently kept below 5%. The method should improve with more realistic models. © 2022 Mathematics in Applied Sciences and Engineering. All rights reserved.

A descriptive and validation study of a predictive model of severity of SARS-COV-2 infection.

Objectives: The strain the SARS-COV-2 pandemic is putting on hospitals requires that predictive values are identified for a rapid triage and management of patients at a higher risk of developing severe COVID-19. We developed and validated a prognostic model of COVID-19 severity. Methods: A descriptive, comparative study of patients with positive vs. negative PCR-RT for SARS-COV-2 and of patients who developed moderate vs. severe COVID-19 was conducted. The model was built based on analytical and demographic data and comorbidities of patients seen in an Emergency Department with symptoms consistent with COVID-19. A logistic regression model was designed from data of the COVID-19-positive cohort. Results: The sample was composed of 410 COVID-positive patients (303 with moderate disease and 107 with severe disease) and 81 COVID-negative patients. The predictive variables identified included lactate dehydrogenase, C-reactive protein, total proteins, urea, and platelets. Internal calibration showed an area under the ROC curve (AUC) of 0.88 (CI 95%: 0.85-0.92), with a rate of correct classifications of 85.2% for a cut-off value of 0.5. External validation (100 patients) yielded an AUC of 0.79 (95% CI: 0.71-0.89), with a rate of correct classifications of 73%. Conclusions: The predictive model identifies patients at a higher risk of developing severe COVID-19 at Emergency Department, with a first blood test and common parameters used in a clinical laboratory. This model may be a valuable tool for clinical planning and decision-making.

Psychological distress of cancer patients caused by treatment delay during the COVID-19 pandemic in China: A cross-sectional study.

OBJECTIVE: The currents study sought to explore the impact of treatment delay on the mental health for patients with cancer during the 2019 coronavirus disease (COVID-19) pandemic. METHODS: Travel restrictions were imposed in most areas of the country between 23 January 2020 and 25 February 2020 owing to the COVID-19 epidemic. Travel restrictions were lifted from 26 February 2020 to 12 March 2020. The number of new confirmed cases significantly reduced after 12 March 2020. Study participants, comprised of individuals from three distinct groups: (1) 835 cancer patients who attended Zhejiang Cancer Hospital between 26 February 2020 and 12 March 2020; (2) 185 healthy volunteers recruited between 26 February 2020 and 12 March 2020; (3) 168 cancer patients who attended the hospital during the non-epidemic period (after 12 March 2020). Two outcome measures including patients' posttraumatic stress responses and general psychological distress (GPD) were assessed using the Chinese versions of the Impact of Events Scale-Revised and the Kessler Psychological Distress Scale (K10). Treatment delay was assessed via counting the time interval from diagnosis to treatment initiation, or from planned treatment date to actual date of therapy. Communication satisfaction was evaluated via a self-report questionnaire. An independent sample t-test or Wilcoxon rank sum test was used for comparison. Statistical analysis included Chi-square test, Mann-Whitney test and multivariate logistic regression. RESULTS: All 1188 participants (835 patients with cancer and 185 controls during the outbreak, and 168 patients with cancer during the non-epidemic period) completed and submitted the questionnaires. A positive association was observed between treatment delays and increased GPD levels (OR 1.716; 95% confidence interval ,CI 1.254-2.348; p = 0.001) as well as posttraumatic stress disorder (PTSD) symptoms (OR: 1.545, 95% CI: (1.166-2.047), p = 0.002). Patients who reported good communication with their doctors showed a significantly lower risk of GPD (OR: 0.526, 95% CI (0.348-0.794), p = 0.002) and PTSD (OR: 0.683, 95% CI (0.490-0.951), p = 0.024) compared with patients who reported unsatisfactory communication or had no contact with their doctors. Multivariate logistic regression analysis showed that treatment at a local hospital, treatment delays and unsatisfactory or no communication with cancer-care professionals were significantly correlated with severe GPD and PTSD symptoms of patients (all p ≤ 0.05). CONCLUSION: The findings indicate that cancer patients who underwent treatment delays during the COVID-19 pandemic may become vulnerable to psychological distress. The results showed that effective communication with doctors and cancer-care professionals during outbreak significantly reduces GPD levels and PTSD symptoms.

COVID-19 and Attendance Demand for Professional Sport in Japan: A Multilevel Analysis of Repeated Cross-Sectional National Data during the Pandemic.

In the current investigation, we assess the effect of COVID-19 on intention-based spectator demand for professional sports in Japan captured by eight, monthly repeated cross-sectional national surveys from May to December 2020 (n = 20,121). We regress spectator demand on individual (e.g., gender), prefecture-wave (e.g., COVID-19 infection status), and prefecture-level factors (i.e., with or without quality professional teams). The results of multilevel logistic regression demonstrate that individual (i.e., male, younger, full-time employment, and with children status) and prefecture-level team factors (i.e., with teams) were associated with intention-based spectator demand. Nevertheless, COVID-19-related factors were found to be unrelated to spectator demand. The findings imply that sports fans are likely to return to the stadium once behavioral restrictions are lifted. The current research provided further evidence that individual factors and team quality serve as influential antecedents of spectator demand in the context of the COVID-19 epidemic.

Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings.

Symptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVID-19 pandemic using logistic regression and a machine-learning approach, in Bogotá, Colombia. Participants from the CoVIDA project were included. A logistic regression using the model was chosen based on biological plausibility and the Akaike Information criterion. Also, we performed an analysis using machine learning with random forest, support vector machine, and extreme gradient boosting. The study included 58,577 participants with a positivity rate of 5.7%. The logistic regression showed that anosmia (aOR = 7.76, 95% CI [6.19, 9.73]), fever (aOR = 4.29, 95% CI [3.07, 6.02]), headache (aOR = 3.29, 95% CI [1.78, 6.07]), dry cough (aOR = 2.96, 95% CI [2.44, 3.58]), and fatigue (aOR = 1.93, 95% CI [1.57, 2.93]) were independently associated with SARS-CoV-2 infection. Our final model had an area under the curve of 0.73. The symptoms-based model correctly identified over 85% of participants. This model can be used to prioritize resource allocation related to COVID-19 diagnosis, to decide on early isolation, and contact-tracing strategies in individuals with a high probability of infection before receiving a confirmatory test result. This strategy has public health and clinical decision-making significance in low- and middle-income settings like Latin America.

Prediction of COVID-19 Cases in India Using Parametric Curve

Due to novel coronavirus (COVID-19), the world is facing a pandemic situation. Human lifestyle changed drastically during this pandemic period, and everyone is badly affected and do not know when the situation is going to be normal. Though the virus is under control, still there is a uncertainty and unpredictable situation exist not only in India but also all over the world. So it is very important to predict the COVID cases as early as possible so that the best precautionary measure can be taken. In this study, we have designed a parametric estimation curve using linear, exponential, and logistic model for forecasting new cases on 30 days ahead. From experimentation, it is found that the logistic model performs better than the linear and exponential model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine Learning Based Prediction of COVID-19 Infection in India

The novel coronavirus (COVID-19) infection had spread throughout the globe since the beginning of 2020 giving rise to a pandemic situation. In this paper, attempts have been made to model the COVID-19 infection in India using exponential, logistic and Gompertz-based mathematical machine learning regression models. These predictive methods show an excellent fit with the daily count of confirmed cases for the period between January 30, 2020, and February 3, 2021. The mean squared logarithmic error (MSLE) of the Gompertz model being lowest among the three machine learning regression methods considered in this paper making it ideal at least as a case study for future predictions in Indian scenario. Nevertheless, the epidemiologists, healthcare personnel, or other Government authorities may use this study as a reference for future planning in prevention of such pandemic situation in similar developing nations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Situation of COVID-19 in Brazil in August 2020: An Analysis via Growth Models as Implemented in the ModInterv System for Monitoring the Pandemic

In this work we introduce a novel methodology to classify the dynamical stages of an epidemic, based on the different acceleration regimes of the corresponding growth curve. Our classification scheme is implemented by fitting the empirical data with a general class of mathematical growth models, from which we compute not only the growth acceleration but also its jerk and jounce (i.e., the first and second derivatives of the acceleration, respectively), thus allowing for a finer distinction of the epidemic stages. Using this methodology, we analyze the cumulative curves of deaths attributed to COVID-19 in the 26 Brazilian States and the Federal District, up until August 21, 2020. The online application ModIntervCOVID-19, which automatically implements the classification scheme and which can be accessed via an internet browser or a mobile app, was used to investigate the epidemic stages in each of the Brazilian federal units. The analysis revealed that almost all states in the Northern and Northeastern regions were already in the saturation phase, meaning that the epidemic was relatively under control, whereas in all Southern states and in most states in the Midwest the epidemic was still accelerating or showed only a slight deceleration. The Southeastern region presented a great diversity of epidemic stages, with each state being found at a different stage, ranging from acceleration to saturation. It is argued that understanding this heterogeneous geographical distribution of the epidemic is relevant for public health authorities, as it may help in devising more effective strategies against the COVID-19 pandemic in a continental country like Brazil.

Analysis of Growth Characteristics of Kimchi Cabbage Using Drone-Based Cabbage Surface Model Image

Cultivation soil is the basis for cabbage growth, and it is important to assess not only to provide information on how it affects the growth of vegetable crops but also for cultivation management. Until now, field cabbage surveys have measured size and growth variations in the field, and this method requires a lot of time and effort. Drones and sensors provide opportunities to accurately capture and utilize cabbage growth and variation data. This study aims to determine the growth stages based on drone remote estimation of the cabbage height and evaluate the impact of the soil texture on cabbage height. Time series variation according to the growth of Kimchi cabbage exhibits an S-shaped sigmoid curve. The logistic model of the growth curve indicates the height and growth variation of Kimchi cabbage, and the growth rate and growth acceleration formula of Kimchi cabbage can thus be derived. The curvature of the growth parameter can be used to identify variations in Kimchi cabbage height and its stages of growth. The main research results are as follows. (1) According to the growth curve, Kimchi cabbage growth can be divided into four stages: initial slow growth stage (seedling), growth acceleration stage (transplant and cupping), heading through slow growth, and final maturity. The three boundary points of the Kimchi cabbage growth curve are 0.2113 Gmax, 0.5 Gmax, and 0.7887 Gmax, where Gmax is the maximum height of Kimchi cabbage. The growth rate of cabbage reaches its peak at 0.5 Gmax. The growth acceleration of cabbage forms inflection points at 0.2113 Gmax and 0.7887 Gmax, and shows a variation characteristic. (2) The produced logistic growth model expresses the variation in the cabbage surface model value for each date of cabbage observation under each soil texture condition, with a high degree of accuracy. The accuracy evaluation showed that R2 was at least 0.89, and the normalized root-mean-square error (nRMSE) was 0.09 for clay loam, 0.06 for loam, and 0.07 for sandy loam, indicating a very strong regression relationship. It can be concluded that the logistic model is an important model for the phase division of cabbage growth and height variation based on cabbage growth parameters. The results obtained in this study provide a new method for understanding the characteristics and mechanisms of the growth phase transition of cabbage, and this study will be useful in the future to extract various types of information using drones and sensors from field vegetable crops.

Modeling the Number of COVID-19 Total Cases in Iran Using Gompertz and Logistic Growth Curves

Introduction: The growth curve are time dependece regression models which commonly are useful in describing the rapid growth of total cases or deaths in a pandemic situation. Methods: The Gompertz and logistic functions are useful to describe the growth curve of a population or any time dependence variable such as metabolic rate, growth of tumors and total number of cases or deaths in a pervasive disease. The logistics family of growth curve including logistic, SSlogistic, generalized logistic and power logistic and Gompertz models were considered to describe the growth curve of total_cases_per_million (t_c_p_m) of COVID-19 in Iran during the 19-Feb-2020 to 28-May-2021. The models were fitted to data using nls function in R and the fitting accuracy was evaluated using the numerical and graphical approaches. Results: The logistic family and Gompertz growth curve were applied to fit the total_cases_per_million of COVID-19 in Iran as the response versus the time in days as predictor variable. The training and testing RMSE criterions were considered as the numerical criterions to assess the model accuracy. The growth curve of fitted models was compared with the growth curve of observed data. Results indicated that the logistic and Gompertz models provided a better description of target variable than the alternatives. Conclusion: As results shown, the logistic and Gompertz models provided a better description of response variable than the alternatives. Therefore, the logistic and Gompertz models are able to describe and forecast the COVID-19 variables (including total cases, death, recovered and so on) very well. © 2021 Tehran University of Medical Sciences. Published by Tehran University of Medical Sciences.

Risk, uncertainty and ambiguity amid Covid-19: A multi-national analysis of international travel intentions.

This study analyses how Covid-19 shapes individuals' international tourism intentions in context of bounded rationality. It provides a novel analysis of risk which is disaggregated into tolerance/aversion of and competence to manage risks across three different aspects: general, domain (tourism) and situational (Covid-19). The impacts of risk are also differentiated from uncertainty and ambiguity. The empirical study is based on large samples (total = 8962) collected from the world's top five tourism source markets: China, USA, Germany, UK and France. Various risk factors show significant predictive powers of individual's intentions to defer international tourism plans amid Covid-19. Uncertainty and ambiguity intolerance is shown to lead to intentions to take holidays relatively sooner rather than delaying the holiday plans.