"It depends:" a qualitative study on digital health academic-industry collaboration.

BACKGROUND: Academic-industry collaborations (AICs) are endorsed to alleviate challenges in digital health, but partnership experiences remain understudied. The qualitative study's objective investigated collaboration experiences between academic institutions and digital health companies. METHODS: A phenomenology methodology captured experiences of AICs, eliciting perspectives from academic researchers and industry affiliates (e.g., leadership, company investigators). Semi-structured interviews probed eligible collaborators about their experiences in digital health. Analysts coded and organized data into significant statements reaching thematic saturation. RESULTS: Participants (N=20) were interviewed from 6 academic institutions and 14 unique industry partners. Seven themes emerged: (I) Collaboration evolves with time, relationships, funding, and evidence; (II) Collaboration demands strong relationships and interpersonal dynamics; (III) Operational processes vary across collaborations; (IV) Collaboration climate and context matters; (V) Shared expectations lead to a better understanding of success; (VI) Overcoming challenges with recommendations; (VII) Collaboration may help navigate the global pandemic. CONCLUSIONS: Digital health academic industry collaboration demands strong relationships, requiring flexible mechanisms of collaboration and cultural fit. Diverse models of collaboration exist and remain dependent on contextual factors. While no collaboration conquers all challenges in digital health, AICs may serve as a facilitator for improved digital health products, thus advancing science, promoting public health, and benefiting the economy.

Cancer referrals at African Inland Church Kijabe Hospital, Kiambu County (2014-2020) and the impact of COVID-19.

In Kenya, cancer is the third leading cause of death. The African Inland Church Kijabe Hospital (AICKH) is a level 4 missionary hospital. The hospital serves the Kenyan population in many areas, including cancer care, and some of these services were affected during the coronavirus disease 2019 (COVID-19) pandemic. We aimed to leverage a recently established hospital-based cancer registry of patients treated at AICKH between 2014 and 2020 to describe the cancer cases and patient referral patterns seen at AICKH during the COVID-19 pandemic in 2020. A cross-sectional retrospective survey was conducted through medical records abstraction in the surgery, breast clinic, palliative care and pathology departments. A total of 3279 cases were included in the study, with females accounting for 58.1% of the cases. The top-three cancers overall were breast (23.0%), oesophagus (20.5%) and prostate (8.6%). There was a minimal increase in the number of cancer cases in 2020 (1.7%) compared with 2019, with an increase of 19.3% in 2019 compared with 2018. In conclusion, AICKH is one of the few hospitals in Kenya where a large number of cancer patients seek healthcare, and referral of cancer cases changed in 2020, which may be due to the COVID-19 pandemic. Future efforts can leverage this registry to determine the impacts of cancer diagnosis and treatment on survival outcomes.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA

Forecasting COVID-19 incidents is a trending research study in today's world. Since Machine learning models have been occupied in forecasting recently, this study focus on comparing statical and machine learning models such as ARIMA, RNN, LSTM, Seq2Seq, and Stacked LSTM. The performances were evaluated using two loss functions, namely, AIC and RMSE. The results showed that RNN performs with the lowest RMSE with-49.5% compared with the ARIMA. Seq2Seq scored the highest correlation of determination (R2) with 0.92. © 2022 IEEE.

Statistical Modelling for The Covid -19 Mortality Rate In the Kingdom of Saudi Arabia

We introduce a new model called the length-biased exponential distribution which become a fascinating new model in a number of research domains in recent years. By adding an extra shape parameter, a new generalised form that is coupled to the length-biased exponential distribution addressed in this work may be constructed, enhancing its utility. The new distribution is known as the new extension length-biased exponential distribution (NELBE). Its density function, as well as its survival and hazard rate curves, are all shown graphically. The study presented the quantile function, linear representations, and some other properties. We displayed and graphed the shapes of the distribution functions. When it came time to calculate the distribution parameters, we employed a total of six distinct estimating strategies. In order to compare and draw conclusions about the performance of the different estimators, a thorough numerical analysis was done. Here, two real data set on COVID-19 mortality rate was examined to show how adaptable and practical the suggested distribution is.

African-initiated churches and environmental care in Limpopo, South Africa: A missional enquiry

Climate change in South Africa is increasingly threatening the most vulnerable populations in rural areas of the country, such as the Limpopo province. Religious communities could be important actors in South Africa, and their role in sustainable development could be critical. Research on the capacities of religious communities for climate change adaptation is vital for reaching the United Nations Sustainable Development Goals 13, 14 and 15. This article drew on empirical research focusing on adaptive practices to climate change. It asked the following question: how do African Independent and Pentecostal churches located in the province of Limpopo relate to climate change in their communal and individual activities? To answer this question, qualitative semistructured individual interviews, group interviews and results from focus groups were used for data collection. The research learned that eco-theology is not the most prominent topic in the majority of the participants' congregations and their communal activities. However, all the participants had noticed the effects of climate change in their immediate surroundings. As a consequence, these individuals took care of their surrounding environments. Focus groups were formed with the hope of consolidating individual efforts into a collective toolkit. This article concluded that the majority of the research participants are not knowledgeable about climate change as a concept. However, they are cognisant of the impact climate change has on them. Intradisciplinary and/or interdisciplinary implications: This article was practical theology research. It was strengthened by research findings from agricultural sciences, ecology, development sciences, missiology and intercultural theology to propose an eco-theology from below based on individual adaptive measures to climate change.

COVID-19 Prediction based on Infected Cases and Deaths of Bangladesh using Deep Transfer Learning

The severely infectious virus known as "COVID-19" has wreaked havoc on the planet, trapping to keep the disease from spreading, while billions of people are staying inside. Every experts and professionals in many disciplines are working tirelessly to create a vaccine and preventative techniques to help the globe overcome this difficult crisis. In Bangladesh, the number of persons infected with Coronavirus is particularly alarming. A accurate prognosis of the epidemic, on the other hand, may aid in the management of this contagious illness until a remedy is discovered. This study aims to forecast impending COVID-19 exposed instances and fatalities using a time series dataset utilizing proposed deep transfer learning model where encoder-decoder CNN-LSTM along with deep CNN pretrained models such as: ResNet-50, DenseNet-201, MobileNet-V2, and Inception-ResNet-V2 performed. We also predict the regular exposed instances and fatalities throughout the following 180 days in data visualization segment using AIC and BIC selection criteria. The suggested paradigms are also used to anticipate Bangladesh's daily confirmed cases and daily which is evaluated by error based on three performance criteria. We discovered that ResNet-50 performs better among others for predicting infected case and deaths owing to COVID-19 in Bangladesh in terms of MAPE, MAE and RMSE evaluations.

Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series.

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.

Association between short-term exposure to air pollution and COVID-19 mortality in all German districts: the importance of confounders.

Background: The focus of many studies is to estimate the effect of risk factors on outcomes, yet results may be dependent on the choice of other risk factors or potential confounders to include in a statistical model. For complex and unexplored systems, such as the COVID-19 spreading process, where a priori knowledge of potential confounders is lacking, data-driven empirical variable selection methods may be primarily utilized. Published studies often lack a sensitivity analysis as to how results depend on the choice of confounders in the model. This study showed variability in associations of short-term air pollution with COVID-19 mortality in Germany under multiple approaches accounting for confounders in statistical models. Methods: Associations between air pollution variables PM2.5, PM10, CO, NO, NO2, and O3 and cumulative COVID-19 deaths in 400 German districts were assessed via negative binomial models for two time periods, March 2020-February 2021 and March 2021-February 2022. Prevalent methods for adjustment of confounders were identified after a literature search, including change-in-estimate and information criteria approaches. The methods were compared to assess the impact on the association estimates of air pollution and COVID-19 mortality considering 37 potential confounders. Results: Univariate analyses showed significant negative associations with COVID-19 mortality for CO, NO, and NO2, and positive associations, at least for the first time period, for O3 and PM2.5. However, these associations became non-significant when other risk factors were accounted for in the model, in particular after adjustment for mobility, political orientation, and age. Model estimates from most selection methods were similar to models including all risk factors. Conclusion: Results highlight the importance of adequately accounting for high-impact confounders when analyzing associations of air pollution with COVID-19 and show that it can be of help to compare multiple selection approaches. This study showed how model selection processes can be performed using different methods in the context of high-dimensional and correlated covariates, when important confounders are not known a priori. Apparent associations between air pollution and COVID-19 mortality failed to reach significance when leading selection methods were used. Supplementary Information: The online version contains supplementary material available at 10.1186/s12302-022-00657-5.

Optimal Choice for Online Shopping in COVID-19 Pandemic

In the context of the outbreak of the COVID-19 epidemic in Vietnam, it has severely affected the economy and people's lives lead to changes in consumer habits with daily activities and shopping, which creates growth opportunities for online businesses. This study aims to determine the factors affecting the online shopping intention of consumers during the COVID-19 pandemic in Vietnam market. The results of the AIC Algorithm for the Online shopping intention (OSI) showed that 5 independent variables Reliability (RE), Perceived usefulness (PU), Perceived ease of use (PEU), Perceived behavioral control (PBC), Subjective norm (SN) have a positive impact on the Online shopping intention (OSI) and Perceived risk (PR) has a negative impact on the Online shopping intention (OSI) during the COVID-19 pandemic. Previous studies revealed that using linear regression. This study uses A/C Optimal Choice of Consumers for Online Shopping in COVID-19 Pandemic.

AIC Algorithm for Entrepreneurial Intention in Covid19 Pandemic

This paper applied the AIC algorithm pointed out that the factors impacted the entrepreneurial intention in the Covid19 Pandemic. The COVID-19 epidemic had caused great harm to the start-up community when up to 50% of startups confirmed that they were operating in moderation and generating negligible income;while 23% of start-ups think that they are losing capital raising opportunities and expanding their market, 20% of start-ups choose to freeze their activities. We selected 178 students living in Ho Chi Minh City, Vietnam, to survey. The results suggest that the research results show that the factors affecting the Entrepreneurship intention of students from strong impact to weak impact are as follows: personality characteristics, subjective norm, feasibility perception, attitude towards entrepreneurship, financial approach impact entrepreneurship intention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

The Process State Tracking Method in Poisson Process

Understanding the state transition of a process from the time series data obtained from the process is important from the viewpoint of both analyzing and controlling the process. In particular, it is important to clarify a turning point of the state transition, that is, the point of change in the process and to find the cause of the state transition by observing and analyzing the data from the process. This paper considers the method of detecting several change points in a process based on the likelihood theory and information criterion when a series of data from the process follows the Poisson process. Then, the method of finding any process state fluctuation and points of change is called “the process state tracking method”. The validity and applicability of the process state tracking method introduced in this paper is confirmed through some numerical applications. © 2021 Japan Industrial Management Association. All rights reserved.

A Machine Learning Univariate Time series Model for Forecasting COVID-19 Confirmed Cases: A Pilot Study in Botswana

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

Length of Stay Analysis of COVID-19 Hospitalizations Using a Count Regression Model and Quantile Regression: A Study in Bologna, Italy.

This study aimed to identify and explore the hospital admission risk factors associated with the length of stay (LoS) by applying a relatively novel statistical method for count data using predictors among COVID-19 patients in Bologna, Italy. The second goal of this study was to model the LoS of COVID patients to understand which covariates significantly influenced it and identify the potential risk factors associated with LoS in Bolognese hospitals from 1 February 2020 to 10 May 2021. The clinical settings we focused on were the Intensive Care Unit (ICU) and ordinary hospitalization, including low-intensity stays. We used Poisson, negative binomial (NB), Hurdle-Poisson, and Hurdle-NB regression models to model the LoS. The fitted models were compared using the Akaike information criterion (AIC), Vuong's test criteria, and Rootograms. We also used quantile regression to model the effects of covariates on the quantile values of the response variable (LoS) using a Poisson distribution, and to explore a range of conditional quantile functions, thereby exposing various forms of conditional heterogeneity and controlling for unobserved individual characteristics. Based on the chosen performance criteria, Hurdle-NB provided the best fit. As an output from the model, we found significant changes in average LoS for each predictor. Compared with ordinary hospitalization and low-intensity stays, the ICU setting increased the average LoS by 1.84-fold. Being hospitalized in long-term hospitals was another contributing factor for LoS, increasing the average LoS by 1.58 compared with regular hospitals. When compared with the age group [50, 60) chosen as the reference, the average LoS decreased in the age groups [0, 10), [30, 40), and [40, 50), and increased in the oldest age group [80, 102). Compared with the second wave, which was chosen as the reference, the third wave did not significantly affect the average LoS, whereas it increased by 1.11-fold during the first wave and decreased by 0.77-fold during out-wave periods. The results of the quantile regression showed that covariates related to the ICU setting, hospitals with longer hospitalization, the first wave, and the out-waves were statistically significant for all the modeled quantiles. The results obtained from our study can help us to focus on the risk factors that lead to an increased LoS among COVID-19 patients and benchmark different models that can be adopted for these analyses.

Consumers' behavioral intentions in online food delivery application usage using AIC

With 60% are young people in about 100 million people, Vietnam is considered a potential market in online payments. Significantly, during the Covid-19 epidemic period, the habit of cashless payment was increasingly developed. Along with online payment services, online food delivery apps are also a sector benefiting from the changing consumption habits of Vietnamese. The paper aims to analyze the optimal model of clients' behavioral intentions in online food delivery application usage in Vietnam. Data was gathered from clients of Vietnam who are existing in Ho Chi Minh City. We applied RStudio software for statistical analysis. The findings of this study disclose that determinants (performance expectance, effort expectance, price value, and experience and habit) play an essential role and positively influence on clients' behavioral intentions in using online food delivery applications. This research evaluates the optimal model by the Akaike information criterion (AIC) approach. ©Kalahari Journals.

Learning it the hard way - how enjoying life and positive appraisal buffer the negative effects of stressors on mental health in the COVID-19 pandemic.

BACKGROUND: Higher levels of stress and negative emotions such as anxiety and depression have been reported since the beginning of the COVID-19 pandemic, but it remains less clear how positive emotions, such as hedonic capacity, may be affected. Further, during lockdowns, the ability to learn new pleasurable activities (hedonic learning) may be particularly relevant. Here, we investigated if state hedonia and/or hedonic learning mediated the relationship between COVID-19 stress and mental health. Moreover, we explored whether positive appraisal style (PAS), a major resilience factor, influenced these relationships. METHODS: Using a cross-sectional design, 5000 German-speaking participants filled out online questionnaires targeting stressors, mental health, state hedonia, hedonic learning, and PAS between April 9 and May 15, 2020. After confirming the factor structure of our constructs, we applied latent structural equation modeling to test mediation as well as moderated mediation models. RESULTS: Stress showed a positive association with mental health symptoms, which was buffered by both state hedonia and hedonic learning. While higher stress was related to lower state hedonia, participants reported more hedonic learning with greater stressor load. The latter effect was greater for individuals with high PAS. LIMITATIONS: The present results should be replicated in longitudinal designs with representative samples to confirm the directionality and generalizability of effects. CONCLUSIONS: Both state hedonia and hedonic learning buffered the effect of stress on mental health in an early phase of the COVID-19 pandemic. Learning new rewarding activities in combination with a PAS may be especially relevant for maintaining mental health during lockdowns.

Bertalanffy-Pütter models for the first wave of the COVID-19 outbreak.

The COVID-19 pandemics challenges governments across the world. To develop adequate responses, they need accurate models for the spread of the disease. Using least squares, we fitted Bertalanffy-Pütter (BP) trend curves to data about the first wave of the COVID-19 pandemic of 2020 from 49 countries and provinces where the peak of the first wave had been passed. BP-models achieved excellent fits (R-squared above 99%) to all data. Using them to smoothen the data, in the median one could forecast that the final count (asymptotic limit) of infections and fatalities would be 2.48 times (95% confidence limits 2.42-2.6) and 2.67 times (2.39-2.765) the total count at the respective peak (inflection point). By comparison, using logistic growth would evaluate this ratio as 2.00 for all data. The case fatality rate, defined as the quotient of the asymptotic limits of fatalities and confirmed infections, was in the median 4.85% (confidence limits 4.4%-6.5%). Our result supports the strategies of governments that kept the epidemic peak low, as then in the median fewer infections and fewer fatalities could be expected.

Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA).

Most countries are reopening or considering lifting the stringent prevention policies such as lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed, recovered and deaths) are increasing significantly. As of July 25th, there are 16.5 million global cumulative confirmed cases, 9.4 million cumulative recovered cases and 0.65 million deaths. There is a tremendous necessity of supervising and estimating future COVID-19 cases to control the spread and help countries prepare their healthcare systems. In this study, time-series models - Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) are used to forecast the epidemiological trends of the COVID-19 pandemic for top-16 countries where 70%-80% of global cumulative cases are located. Initial combinations of the model parameters were selected using the auto-ARIMA model followed by finding the optimized model parameters based on the best fit between the predictions and test data. Analytical tools Auto-Correlation function (ACF), Partial Auto-Correlation Function (PACF), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. Evaluation metrics Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) were used as criteria for selecting the best model. A case study was presented where the statistical methodology was discussed in detail for model selection and the procedure for forecasting the COVID-19 cases of the USA. Best model parameters of ARIMA and SARIMA for each country are selected manually and the optimized parameters are then used to forecast the COVID-19 cases. Forecasted trends for confirmed and recovered cases showed an exponential rise for countries such as the United States, Brazil, South Africa, Colombia, Bangladesh, India, Mexico and Pakistan. Similarly, trends for cumulative deaths showed an exponential rise for countries Brazil, South Africa, Chile, Colombia, Bangladesh, India, Mexico, Iran, Peru, and Russia. SARIMA model predictions are more realistic than that of the ARIMA model predictions confirming the existence of seasonality in COVID-19 data. The results of this study not only shed light on the future trends of the COVID-19 outbreak in top-16 countries but also guide these countries to prepare their health care policies for the ongoing pandemic. The data used in this work is obtained from publicly available John Hopkins University's COVID-19 database.

ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound.

Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public's concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and  Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios.

Prognosticating the Spread of Covid-19 Pandemic Based on Optimal Arima Estimators.

COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19 from the explicit data based on optimal ARIMA model estimators. Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and the Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to the number of autoregressive terms, d refers to the number of times the series has to be differenced before it becomes stationary, and q refers to the number of moving average terms. Results obtained from the ARIMA model showed a significant decrease in cases in Australia; a stable case for China and rising cases have been observed in other countries. This study predicted the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.