Trends and Dynamics of COVID-19 in Zimbabwe: Implications for Selected Sustainable Development Goals

This study analysed the spatial and temporal trends and dynamics of COVID-19 to understand their implications on Sustainable Development Goals (SDGs) in Zimbabwe. Data on daily cases and mortality rates of COVID-19 were collected from the Worldometer website, whilst data on lockdown measures and travel restrictions were collected from Zimbabwe's Ministry of Health and Child Care. Exploratory and confirmatory analyses were employed on statistical data. COVID-19 statistical data were first tested for normality using the Kolmogorov-Smirnov test. Subsequently, the non-parametric Mann-Kendal (M-K) test was performed to determine the monthly average number of new cases and deaths trend from March 2020 to February 2022 using XLSTAT (2020). The study shows a significant increase (p = 0.00, α= 0.05) in COVID-19 cases between March 2020 and February 2022. The trend is characterised by sharp increases associated with wave periods. Although the results show no correlation between stringency index and COVID-19 cases, periods of high stringency are associated with a slightly lower number of cases. The spatial trends show that highly populated areas have high numbers of patient cases. Indeed, the lockdown measures put in place, among other factors, contributed to controlling the spread of the virus. The trends and dynamics of COVID-19 in Zimbabwe have implications for achieving SDG 1, SDG 2, SDG 3 and SDG 6. Thus, there is a need to factor in the temporal and spatial realities of COVID-19 in making a policy framework for effective control of the pandemic and promotion of sustainable development. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation

Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022. © 2023 The Authors. Published by Universitas Airlangga.

Predictive Information Workflow of Forecasting Number of COVID-19 Confirmed Cases

There are questions about how to accurately prepare with the correct number of resources for distribution in order to properly manage the healthcare resources (e.g., healthcare workers, Masks, ART-19 TestKit) required to tighten the grip on the COVID-19 pandemic. Mathematical and computational forecasting models have well served the means to address these questions, as well as the resulting advisories to governments. A workflow is proposed in this research, aiming to develop a forecasting simulation that makes accurate predictions on COVID-19 confirmed cases in Singapore. According to the analysis of the prior works, six candidate forecasting models are evaluated and compared in the workflow: polynomial regression, linear regression, SVM, Prophet, Holt's linear, and LSTM models. The study's goal is to determine the most suitable forecasting model for COVID-19 cases in Singapore. Two algorithms are also proposed to better compute the performance of two models: the order algorithm to determine optimal degree order for the polynomial regression model, and the optimizing algorithm for the Holt's linear model to calculate the optimal smoothing parameters. Observed from the experiment results with the COVID-19 dataset, the Prophet method model achieves the best performance with the lowest Root Mean Square Error (RMSE) score of 1557.744836 and Mean Absolute Percentage Error (MAPE) score of 0.468827, compared to the other five models. The Prophet method model achieving average accuracy range of 90% when forecasting the number of confirmed COVID-19 cases in Singapore for the next 87 days ahead. is chosen and recommended to be used as a system model for forecast the COVID-19 confirm cases in Singapore. The developed workflow will greatly assist the authorities in taking timely actions and making decisions to contain the COVID-19 pandemic. © 2022 IEEE.

EVALUATION OF THE EFFECT OF RAINFALL ON COVID-19 TRANSMISSION IN TERENGGANU, MALAYSIA

One of the variables leading to the global spread of COVID-19 cases is the weather, which includes temperature and air quality. In this study, an investigation of the association between precipitation and COVID-19 cases was conducted to provide useful information on the possibility of this climate factor (precipitation) on the progression of COVID-19 cases for an appropriate management strategy. Secondary COVID-19 and rainfall data obtained from the Ministry of Health and the Meteorological Department in Malaysia were used for the study. The collected data were subjected to Pearson correlation analysis. The results of this study showed that both rainy days and rainfall amount were insignificant to COVID-19 cases, indicating that rainfall amount was not associated with COVID-19 transmission in Terengganu, Malaysia. Thus, this discovery could be used to inform individual and COVID-19 supervisors and the government as it prepares for the new weather season. © Penerbit UMT

Multiple measures of structural racism as predictors of U.S. county-level COVID-19 cases and deaths

Minoritized racial groups in the U.S. have experienced disproportionately higher rates of COVID-19 cases and deaths. Studies have linked structural racism as a critical factor causing these disproportionate health burdens. We analyse the relationships between county-level COVID-19 cases and deaths and five measures of structural racism on Black Americans: Black–White residential segregation, differences in educational attainment, unemployment, incarceration rates, and health insurance coverage between Black and White Americans. When controlling for socioeconomic, demographic, health and behavioural factors significant relationships were found between all measures of structural racism with cases and/or deaths except Black–White differences in health insurance coverage. Black–White disparities in educational attainment and incarceration were the strongest predictors. The results varied greatly across regions of the U.S. We also found strong relationships between COVID-19 and mobility and the proportion of foreign-born non-citizens. This work supports the important need to confront structural racism on multiple fronts to address health disparities.

Analyzing the impact of vaccinations and weather factors on the COVID-19 pandemic.

OBJECTIVES: A world-wide immunization project was launched at the peak of COVID-19 pandemic to contain and minimize the adverse effects of SARS-CoV-2 virus. We carried out a series of statistical analyses in this paper to determine, confirm and quantify the impact of the vaccinations on COVID-19 cases and mortalities, amidst critical confounding factors-temperature and solar irradiance. METHODS: The experiments in this paper were carried out on the world data, data from 21 countries, and the five major continents. The significance of the 2020-2022 vaccinations on the COVID-19 cases and mortalities response data were evaluated via Hypotheses' tests. Correlation coefficient analyses were carried out to determine the extent of the relationship between vaccination coverage and corresponding COVID-19 mortalities data. The impact of vaccination was quantified. The effects of the weather factors-temperature and solar irradiance, on COVID-19 cases and mortalities data were analyzed. RESULTS: The series of hypotheses tests carried out reveal that vaccinations did not affect cases; however, vaccinations significantly impacted the mean daily mortalities in all five major continents and globally. The correlation coefficient analysis results show vaccination coverage to be highly and negatively correlated with daily mortalities in the world-the five major continents and most of the countries studied in this work. The percentage reduction in mortalities as a result of wider vaccination coverage was indeed significant. Temperature and solar irradiance impacted daily COVID-19 cases and mortalities data during the vaccination and post-vaccination periods. CONCLUSION: Results show that the world-wide vaccination against COVID-19 project had a significant impact in reducing mortalities and minimizing the adverse effects due to COVID-19 globally, in all five (5) major continents of the world and the countries studied in this work, however, temperature and solar irradiance still had effects on COVID-19 response in the vaccination eras.

The epidemiological follow-up process for suspected and confirmed cases of COVID-19 in migrant shelters on the northern border of Mexico from July to December 2020: Between contagion underestimation and containment.

Background: Elements associated with an increased risk factor for the contagion of COVID-19 in shelters include the turnover and overcrowding of people, time spent in communal areas, daily supply needs, water availability, and sanitation levels. The "Report on the Effects of the COVID-19 Pandemic on Migrants and Refugees," shows that factors such as the shortage of food, supplies, water, sanitizing materials, spaces for healthy distancing, financial resources for rent and essential services, and the lack of medical or psychological care complicated providing care for migrants and applicants seeking international protection. Objective: We describe shelter operations regarding the detection and follow-up of suspected and confirmed COVID-19 cases showing mild symptoms among the migrant population housed in the border cities under study. Methods: We conducted semi-structured, in-depth interviews with study subjects (people in charge, managers, coordinators, shelter directors) from 22 migrant shelters, and 30 with key informants. We studied the cities of Tijuana (Baja California), Nogales (Sonora), Ciudad Juárez (Chihuahua), Piedras Negras (Coahuila), and Heroica Matamoros (Tamaulipas). The research was based on a qualitative methodological design with an ethnographic approach. The information collected was transcribed and systematized into two tables or analytical templates, one for interviews with study subjects, and another for interviews with key actors. Findings: Overall, seventy-eight registered shelters provided accommodation services for migrants in the five cities the study focused on: thirty-seven in Tijuana, five in Nogales, twenty-two in Ciudad Juárez, eight in Piedras Negras, and five plus a camp (six in total) in Matamoros. The major concentration of shelters was in Tijuana (47.4%) and Ciudad Juárez (28.2%). At the beginning of the pandemic, only a few shelter facilities met quarantine and isolation guidelines, such as having separate bathrooms and sufficient space to isolate the "asymptomatic" and "confirmed" from close "contacts". The lack of isolation space and the inability to support the monitoring of patients with COVID-19 posed a challenge for those housed in shelters, forcing many shelters to close or continue operating behind closed doors to avoid becoming a source of infection during the pandemic. Discussion and outlook: Contrary to speculation, during the onset of the pandemic northern border migrant shelters did not become sources of COVID-19 infection. According to the data analyzed from 78 shelters only seven had confirmed cases, and the classification of "outbreak" was applied only in two facilities. Contagion control or containment was successful as the result of following a preventive containment logic, including the isolation of all suspected but unconfirmed cases, without a clear understanding of the human and financial resources required to maintain isolation areas. However, shelters in the study implemented protocols for epidemiological surveillance, control, and prevention with elements that interfered with monitoring spaces, and processes that caused oversights that resulted in underestimating the number of cases. Limitations: Due to travel restrictions imposed to prevent and contain coronavirus infections it was impossible to stay on-site in the cities studied, except for Tijuana, or carry-out recordings of migrants' views in shelters.

The mitigating effect of masks on the spread of Covid-19.

We use US state-level data from early in the pandemic -March 15, 2020 to November 15, 2020- to estimate the effects of mask mandates and compliance with mandates on Covid-19 cases and deaths, conditional on mobility. A one-standard-deviation increase in mobility is associated with a 6 to 20 percent increase in the cases growth rate; a mask mandate can offset about one third of this increase with our most conservative estimates. Also, mask mandates are more effective in states with higher compliance. Given realized mobility, our estimates imply that total infections in the US on November 15, 2020 would have been 23.7 to 30.4 percent lower if a national mask mandate had been enacted on May 15, 2020. This reduction in cases translates to a 25 to 35 percent smaller decline in aggregate hours worked over the same period relative to a 2019 baseline.

School-age vaccination, school openings and Covid-19 diffusion.

This article investigates the relationship between school openings and Covid-19 diffusion when school-age vaccination becomes available. The analysis relies on a unique geo-referenced high frequency database on age of vaccination, Covid-19 cases and hospitalization indicators from the Italian region of Sicily. The study focuses on the change of Covid-19 diffusion after school opening in a homogeneous geographical territory (i.e., with the same control measures and surveillance systems, centrally coordinated by the Regional Government). The identification of causal effects derives from a comparison of the change in cases before and after school opening in the school year 2020/21, when vaccination was not available, and in 2021/22, when the vaccination campaign targeted individuals of age 12-19 and above 19. Results indicate that, while school opening determined an increase in the growth rate of Covid-19 cases in 2020/2021, this effect has been substantially reduced by school-age vaccination in 2021/2022. In particular, we find that an increase of approximately 10% in the vaccination rate of school-age population reduces the growth rate of Covid-19 cases after school opening by approximately 1%.

Household Readiness to Care for Mild and Asymptomatic COVID-19 Cases at Home, Southwest Ethiopia: A Community-based Cross-Sectional Study.

Background: Corona virus disease (COVID-19) continued with its notorious effects overwhelming health institutions. Thus, home-based identification and care for asymptomatic and mild cases of COVID-19 has been recommended. Therefore, the objective of this study was to assess the level of household readiness for caring asymptomatic and mild cases of COVID-19 at home. Methods: A community-based cross-sectional study was conducted from March-June 2021 on randomly selected 778 households. Data entry and analysis were carried out using EpiData and SPSS version 25, respectively. Multivariable logistic regression was modeled to identify independent predictors of community readiness. Results: Overall readiness of the community was very low (43.8%). Factors positively affecting household readiness were male household heads (AOR = 1.6; 95%CI: 1.05, 2.45), primary (AOR=2.0; CI:.62, 1.59) and higher (AOR = 1.90; 95%CI: 1.04, 3.45) educational level of the respondents, number of rooms within household (AOR = 1.22; CI: 1.03, 1.46), having additionally house (AOR = 2.61; CI: 1.35, 5.03), availability of single use eating utensils (AOR = 2.76; 95%CI: 1.66, 4.56), availability of community water supply (AOR = 8.21; 95% CI: 5.02, 13.43), and community participation and engagement (AOR = 2.81; 95% CI: 1.93, 4.08) in accessing transport, water and sanitation. Conclusions: The community was less prepared in terms of housing, infection prevention, water and sanitation. Considering alternative options including universal coverage of vaccine is important; designed behavioral change communications can enhance community participation and engagement in improving access to transport, water and sanitation to reduce risk of infections.

Impact of Upsurge in Covid-19 Cases on Indian Stock Market: A Machine Learning Approach

In the present study, the influence of the surge in pandemic cases and fatalities due to pandemics on the stock performance of NIFTY 50 have been analyzed by employing a regression analysis algorithm using Python Software. The data have been collected for 27 months starting from 1st Jan 2020 to 31st March 2022 and for the application of machine learning tools the data connected to the stock market and COVID 19 have been integrated into the first phase and thereafter preprocessing has been carried out on the data to bring uniformity to data in the second phase. After preprocessing in the third phase data has been evaluated using five leading regression algorithms. The findings of the study reflected that COVID 19 figures and fatalities have severally impacted the stock market returns of the leading index i.e., NIFTY 50. Further, it was gathered from the study that REPTree regression would be a better fit to the model and Gaussian Process would be least fitted to the model as REPTree the lower values of MAE, RMSE, RAE, and R2 error in case of performance evaluation of upsurge in COVID 19 fatalities and surge in stock market returns. © 2022 IEEE.

The Pattern of Non-Covid-19 Paediatric Admissions in Malaysia Teaching Hospitals: Before and during the Covid-19 Pandemic with Movement Control Order

To study the characteristics of paediatric patients admitted to two non-COVID-19 teaching hospitals before and during the implementation of the Movement Control Order (MCO) in Malaysia. The retrospective study was performed in two phases (before MCO between February to March 2020, and during MCO lockdown between March to April 2020) in two teaching hospitals on the East Coast of Peninsular Malaysia. Hospitalized children <18 years were included and those coming for elective procedures or oncology treatment were excluded. The clinical data were retrieved from both hospitals' admission records. There was a total of 496 and 191 admissions to two teaching hospitals on the East Coast of Peninsular Malaysia, respectively. A significant reduction in the number of non-COVID-19 hospital admissions was seen in both hospitals. For Hospital Universiti Sains Malaysia, the daily hospital admissions were reduced with a mean of 10 (before MCO) to 7 (during MCO) admissions/day (95% CI 1.54,7.54, p=0.001). In Sultan Ahmad Shah Medical Centre, a reduction in hospital admission was seen from 5 (before MCO) to 3 (during MCO) admissions/day (95% CI 0.61, 3.15, p=0.005). Our study observed a general drop in non-COVID-related respiratory illnesses and infectious disease cases during the MCO period. There were significant differences in neurological (p=0.029) and accident (p = 0.001) cases admissions observed between the two periods. Copyright © 2022, Faculty of Medicine, University of Malaya. All rights reserved.

Dysbiosis: An Indicator of COVID-19 Severity in Critically Ill Patients.

Here, we examined the dynamics of the gut and respiratory microbiomes in severe COVID-19 patients in need of mechanical ventilation in the intensive care unit (ICU). We recruited 85 critically ill patients (53 with COVID-19 and 32 without COVID-19) and 17 healthy controls (HCs) and monitored them for up to 4 weeks. We analyzed the bacterial and fungal taxonomic profiles and loads of 232 gut and respiratory samples and we measured the blood levels of Interleukin 6, IgG, and IgM in COVID-19 patients. Upon ICU admission, the bacterial composition and load in the gut and respiratory samples were altered in critically ill patients compared with HCs. During their ICU stay, the patients experienced increased bacterial and fungal loads, drastic decreased bacterial richness, and progressive changes in bacterial and fungal taxonomic profiles. In the gut samples, six bacterial taxa could discriminate ICU-COV(+) from ICU-COV(-) cases upon ICU admission and the bacterial taxa were associated according to age, PaO2/FiO2, and CRP levels. In the respiratory samples of the ICU-COV(+) patients, bacterial signatures including Pseudomonas and Streptococcus were found to be correlated with the length of ICU stay. Our findings demonstrated that the gut and respiratory microbiome dysbiosis and bacterial signatures associated with critical illness emerged as biomarkers of COVID-19 severity and could be a potential predictor of ICU length of stay. We propose using a high-throughput sequencing approach as an alternative to traditional isolation techniques to monitor ICU patient infection.

Using Machine Learning classification methods to detect COVID-19 Status in Selangor District

Recently, Covid 19 pandemic has been recognized as a public health emergency of international concern. According to global COVID-19 infection data, the total number of cases is over 147 million, with over 3 million fatalities. Common model that use to predict binary outcome is Logistic regression model. However, the majority of the models have not been implemented widely by using ML approaches. Thus, the interest of this study has been coined to the Covid 19 cases prediction model that influence the extend of risk to urge Covid 19 infection. Therefore, this study addressed how to use four ML algorithms offered by Rapid Miner software tools to identify the optimum classification model. The results show that Decision Tree has been very promising to produce a high percentage of accuracy rate of 75.47% compared to other models. Further research on the data structure is necessary to be conducted in order to address problems like bias and an unbalanced dataset. In addition, new factors like vaccination status should be incorporated into the model to determine whether the respondent is at risk of contracting COVID 19 or not. © 2022 IEEE.

Machine Learning Model for Predicting Number of COVID-19 Cases in Countries with Low Number of Tests

Background: The COVID-19 pandemic has presented a series of new challenges to governments and healthcare systems. Testing is one important method for monitoring and controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries, not every country is able to employ widespread testing. Methods and Objective: Here, we have developed machine learning models for predicting the prevalence of COVID-19 cases in a country based on multilinear regression and neural network models. The models are trained on data from US states and tested against the reported infections in European countries. The model is based on four features: Number of tests, Population Percentage, Urban Population, and Gini index. Result(s): The population and the number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R2 was found to be 0.88 in the multi-linear regression and 0.91 for the neural network model Conclusion(s): The model predicts that the actual prevalence of COVID-19 infection in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers. Copyright © 2022 Hashim et al.

The Impact of Digitalization on Unemployment During Covid-19 Pandemic

The Covid19 pandemic that hit the world turned to be far more than a health crisis;the lock downs and the untraditional measures that were taken by most of the countries of the world to decrease the spread of the virus changed the form of our daily activities, and resulted in major socio-economic challenges. Although many businesses and institutions shifted to online mode in several sectors and industries, the lack of solid digital technologies and communications infrastructure made it hard for other countries to cope with the new global reality [1]. This paper claims that digitalized countries;countries with higher Digital Quality of Life (DQL) Index that is developed by the cyber security Company Surfshark in 2019, reported lower increase in their unemployment rate. The paper also claims that higher Covid19 annual cases lead to higher increase in unemployment rates. A Generalized Linear Model, for a sample of 59 countries including 118 panel data observations, was adopted to test the paper’s claims. The regression results revealed that there is a significant inverse relationship between DQL index and the percentage change in the unemployment rate, yet the positive coefficient between the percentage change in Covid19 cases and the percentage change in the unemployment rate is insignificant. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dynamics and Co-movements Between the COVID-19 Outbreak and the Stock Market in Latin American Countries: An Evaluation Based on the Wavelet-Partial Wavelet Coherence Model.

The COVID-19 outbreak and the global uncertainty it causes produce an apparent panic in stock markets. Efforts to explain the economic spillover effects of COVID-19 can guide authorities to design a control policy against the financial impacts of pandemics. The paper examines the effects of the COVID-19 cases on the stock markets in the emerging Latin American countries of Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The paper employs a continuous partial wavelet methodology to observe lead-lag relations between the daily variables of new COVID-19 cases and the stock market index for each Latin American country. Brazilian new COVID-19 cases led the Bovespa (BVSP) index to decline during the whole period, except February and June 2020, at one month-two month-frequency band. The wavelet and phase difference analyses indicate that, except for Brazil, COVID-19 cases did not affect the stock market indexes adversely during the whole sample period but did affect the stock exchange markets negatively during some sub-sample periods of the entire sample of each country. Dynamics of Latin American stock exchange markets in the short and long run can be explained by some other parameters of real and financial sectors and COVID-19 cases.

Five waves of the COVID-19 pandemic and green-blue spaces in urban and rural areas in Poland.

Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green-blue space indicator in m2/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas.

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.

Measuring the impact of COVID-19

The COVID-19 pandemic has had major direct impacts on health, including hundreds of millions of cases and nearly five million deaths as of October 2021. Advances in data availability, informatics, visualization, and modeling have made it relatively simple to track the number of cases, hospitalizations, and deaths across the course of the pandemic. More difficult to measure are the many indirect impacts of the COVID-19 pandemic and the response to it. Canceled medical appointments, missed routine screenings, disruptions to routine immunization schedules, and interruptions to critical preventative health services like childhood lead screening have all occurred as a result of the pandemic and the global public health and healthcare response to it. In addition to the impacts on access to healthcare and public health essential services, the COVID-19 pandemic has had major impacts on employment, childcare, food security, and mental health that have direct and indirect effects on health now and for many years into the future.