Trend Of COVID-19 Research Articles

Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection

In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

Trends in COVID-19 testing, infection, vaccination, and housing assistance among people who inject drugs in Los Angeles and Denver

ABSTRACTIntroductionWe examined COVID-19 trends and the socio-structural correlates of infections and vaccinations among people who inject drugs (PWID).MethodsWe collected retrospective survey data from PWID (n = 472). We examined changes in COVID-19 testing, infection, and housing assistance for the first year of the pandemic, and then used multivariate logistic regression to examine factors with ever having an infection and receiving at least one vaccination from April 2020-March 2021.ResultsThe sample was mostly male, White and Latinx, and unhoused. Testing by quarter ranged from 46% to 33%. Housing assistance declined from 10% to 7% by the last quarter, while infections ranged from 6% to 4%. Ever being COVID-19 positive was associated with being male, having an income source, and being arrested in the last 3 months. Almost half of participants reported vaccination (47%). Receiving a vaccination was associated with prior U.S. armed forces service, older age, receiving substance use disorder treatment, and having ever received COVID-19 testing.DiscussionEfforts to vaccinate PWID fell short and housing assistance declined. Receiving targeted assistance, including better access to housing and medication treatments for opioid use disorder, could be important efforts to mitigate the heightened risks of COVID-19 and the ongoing harms PWID face that predate the pandemic.

Heatmap Visualization Approach for Descriptive Analytics of Covid-19 Cases in Malaysia

The Covid-19 pandemic has presented unparalleled obstacles to the world, including Malaysia and Indonesia. A visual analytics approach known as the Covid-19 heatmap visualization is proposed to provide relevant and timely information about Covid-19 daily cases and deaths in Malaysia. The aim of this approach is to offer a platform for various stakeholders to make informed decision based on descriptive analytics. The Covid-19 information visualization approach allows users to compare different types of visualizations such as heatmaps, line charts, and bar charts to detect Covid-19 trends or patterns. The approach is implemented as a web-based system using D3 JavaScript library and tested using the data collected from Coronavirus Pandemic Data Explorer. Results show that the Covid-19 heatmap visualization approach is an invaluable approach for understanding pandemic trends as it provides users with easy access to visualizations and fast understanding of Covid-19 situations such as daily cases, deaths and recoveries. The target users for the proposed approach are the National Security Council and Ministry of Health staff, company crews, and the general public. Future upgrades to the approach includes adding additional attributes such as the number of tests and recoveries to assist users in making predictive analytics using machine learning models.

Development of Regression Models for COVID-19 Trends in Malaysia

COVID-19 has emerged as the biggest threat to the world’s population, since December 2019. There have been fatalities, financial losses, and widespread fear as a result of this extraordinary occurrence, especially in Malaysia. Using available COVID-19 data from the Ministry of Health (MOH) Malaysia website, from 25/1/2020 to 17/6/2022, this study generated regression models that describe the trends of COVID-19 cases in Malaysia, taking into account the unpredictable nature of COVID-19 cases. Three techniques are used in Weka software: 60:40 / 70:30 split ratio, 10 and 20-fold cross-validation, Support Vector Regression (SVR), Multi Linear Regression (MLR), and Random Forest (RF). Based on new instances among adults, the study’s findings indicate that RF has the strongest coefficient correlation and the lowest Root Mean Square Error of 22.7611 when it comes to predicting new COVID-19 deaths in Malaysia. Further investigation into prospective characteristics like vaccination status and types, as well as other external factors like locations, could be added to this study in the future.

Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic

Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods – dynamic network markers (DNMs) and network analysis-based methods – as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used.

The Mountain Products Market - Post Covid-19 Period

Abstract The future of Romania’s mountain areas is based on living mountains on which sustainable and integrated development and a good quality of life are promoted and which can sustain the national mountain economy over time. Mountain products can be approached as a healthy solution to help a nation’s economic development leading to higher living standards. The main challenges related to mountain products and the effects of their development are presented, taking into account the quality of all products obtained on the mountain. This study analyzes the effects of the Covid-19 crisis on the producer market in the mountain areas in Romania, trying to provide solutions and development proposals. Analyzing the post Covid-19 trends of the mountain products market and taking into account the unique and natural specificities and properties, we will propose some solutions that could be considered for the development of this market. Only strategic planning can lead to a sustainable development of the mountain products market in Romania.

Population-based estimates of COVID-19 period prevalence and cumulative monthly incidence in New York City: A comparison of estimates from three surveys, July–August 2020

Background Diagnosis-based surveillance of COVID-19 underestimates COVID-19 burden. Questions about COVID-19-consistent symptoms were added to three population-based surveys to obtain representative estimates of COVID-19 period prevalence and monthly cumulative incidence. Objective To evaluate if estimates of COVID-19 period prevalence and cumulative monthly incidence differed when collected from surveys with different sampling frames and modes of administration. Methods Data were collected from adult New York City (NYC) residents via the Community Health Survey (CHS) (sampling frame: random digit dial with dual landline and cellphone frame; mode: phone) and the Citywide Mobility Survey (CMS) (sampling frame: probabilistically selected panel; mode: online) in July 2020 and via CHS and Healthy NYC (sampling frame: probabilistically selected panel; mode: online and phone) in August 2020. Persons with COVID-19-like illness (CLI) were identified based on reported symptoms in the past 30 days. To obtain COVID-19 estimates, CLI estimates were adjusted by the proportion of laboratory-confirmed SARS-CoV-2 infections among citywide emergency department CLI visits in which patients received SARS-CoV-2 testing. We used t-tests to compare estimated CLI period prevalence in July 2020 between CHS and CMS and CLI period prevalence and cumulative monthly incidence in August 2020 between CHS and Healthy NYC. Results CLI period prevalence was similar between CHS and CMS during July (12.2% vs. 9.9%, respectively, p=0.511); COVID-19 period prevalence was 1.7% and 1.3%, respectively. In contrast, CLI period prevalence was higher per Healthy NYC during August 2020 than CHS (18.1% vs. 11.3%, p=0.014); COVID-19 period prevalence was 0.7% and 0.4%, respectively. CLI cumulative monthly incidence in August was similar (5.7% and 4.0%, respectively; p=0.246) in both surveys. Conclusions Because estimates of CLI were not consistently different by sampling frame or mode of administration, additional research to understand the cause of differences between CHS and Healthy NYC can support use of symptom-based surveillance to monitor COVID-19 trends.

IMPACTS OF COVID-19 ON CORPORATE UNIVERSITY AND TRENDS

The main purpose of this article is to identify the main actions adopted by Corporate Universities (CUs) during the COVID-19 pandemic period, as well as trends for the post-pandemic period. To this end, a systematic literature review was conducted on Scopus and Web of Science databases. A questionnaire was developed and validated by experts in the field of Corporate Education, and the survey link was made available on the Qualtrics platform to those involved or interested in the topic. Interviews were also conducted with executives from CUs of large Brazilian organizations during the pandemic period. The questionnaire data were analyzed using descriptive statistics and inferential analysis, as well as content analysis of the interviews. As a result, the main actions taken by CUs focused on the qualification of their instructors, leaders, and professionals. There was an increase in investments by CUs, with an emphasis on the adoption of digital technologies, accompanied by actions aimed at engagement, motivation, and emotional well-being. The main trends for the post-COVID-19 period point to the expansion of short-term hybrid courses and changes in business models to adapt to the new challenges generated by the pandemic. One of the main consequences of the pandemic is the considerable recognition, by employees of organizations, users, and leaders of CUs, of the importance of social and humanities approaches adopted by them. Furthermore, it is understood that the relevant role of CUs tends to be reinforced based on the increasing value of the notion of sustainability in their courses and managerial training practices aimed at the sustainable development of organizations and society.

Research on the application and effect of flipped-classroom combined with TBL teaching model in WeChat-platform-based biochemical teaching under the trend of COVID-19

BackgroundBiochemistry is a core subject in clinical medical education. The traditional classroom teaching model led by teachers is often limited to the knowledge transfer of teachers and the passive acceptance of students. It lacks interactive and efficient teaching methods and is not enough to meet the learning needs and educational goals of modern students. The combination of WeChat public platform, flipped classroom and TBL teaching model is closer to the needs of real life and workplace, helping students to cultivate comprehensive literacy and the ability to solve practical problems. At the same time, this teaching model has yet to be used in biochemistry courses.ObjectiveTo explore the influence of the mixed teaching model of flipped classroom and combining TBL based on WeChat public platform upon undergraduates in biochemistry.MethodsUsing the mixed research method of quasi-experimental research design and descriptive qualitative research, 68 students were selected into the traditional and the blended teaching groups. Among them, the blended teaching group adopts the blended teaching model of flipped classroom combined with TBL based on the WeChat platform to learn biochemical courses. In this study, an independent sample t-test was intended to analyze the differences in final scores, a chi-square test was served to analyze the differences in satisfaction questionnaires, and thematic analysis was used to analyze semi-structured interview data.ResultsCompared with the traditional teaching model, the mixed teaching model significantly improved students' final exam scores (P < 0.05). The teaching satisfaction of the mixed teaching group was also higher than that of the traditional teaching group with statistical significance (P < 0.05). The results of the interviews with eight students were summarized into three topics: (1) Stimulating interest in learning; (2) Improving the ability of autonomous learning; (3) Recommendations for improvement.ConclusionsThe combination of the WeChat platform and flipped classroom with TBL has a positive effect on improving medical students' autonomous learning ability and problem-solving ability. The research shows that the teaching mode of flipped classroom combined with TBL based on the WeChat platform is effective and feasible.

Indonesia’s COVID-19 Trend After the End of a Public Health Emergency of International Concern: Preparation for an Endemic

Three years into the pandemic, the World Health Organization revoked the COVID-19 public health emergency of international concern on May 5, 2023. This decision sparked debate, notably around the possibility of a surge in cases due to the SARS-CoV-2 mutations. To manage this transition, the Indonesian government enacted stringent controls on case numbers. This case series study provided an overview of COVID-19 case trends in Indonesia following the revocation of public health emergency of international concern status by the World Health Organization. Data were collected for 5 weeks after the statement (May 6–June 10, 2023) from the COVID-19 Task Force’s official online platform of the Indonesian Ministry of Health. The trends were monitored in daily confirmed, active, recovered, and death cases, and analyzed the data using Microsoft Excel and Stata 16. The findings indicated a positive trend for Indonesia, with decreased daily confirmed cases (89.42%) and active cases (44.16%). Recovered cases accounted for 97.47%, higher than the global average (96%). Unfortunately, the death rate (2.38%) exceeded the global statistic (1%). These results highlighted the need for sustained vigilance, enhancement of the 3T strategy (testing, tracing, and treatment), and wider vaccination coverage. It remains critical to uphold the 3M protocols—mask-wearing, physical distancing, and hand hygiene—to prevent a potential rebound in cases, even without the public health emergency of international concern status,as the situation transitions toward endemic COVID-19.

Impact of geographical, meteorological, demographic, and economic indicators on the trend of COVID-19: A global evidence from 202 affected countries

Research problemPublic health and the economy face immense problems because of pathogens in history globally. The outbreak of novel SARS-CoV-2 emerged in the form of coronavirus (COVID-19), which affected global health and the economy in almost all countries of the world. Study designThe objective of this research is to examine the trend of COVID-19, deaths, and transmission rates in 202 affected countries. The virus-affected countries were grouped according to their continent, meteorological indicators, demography, and income. This is quantitative research in which we have applied the Poisson regression method to assess how temperature, precipitation, population density, and income level impact COVID-19 cases and fatalities. This has been done by using a semi-parametric and additive polynomial model. FindingsThe trend analysis depicts that COVID-19 cases per million were comparatively higher for two groups of countries i.e., (a) average temperature below 7.5 °C and (b) average temperature between 7.5 °C and 15 °C, up to the 729th day of the outbreak. However, COVID-19 cases per million were comparatively low in the countries having an average temperature between 22.5 °C and 30 °C. The day-wise trend was comparatively higher for the countries having average precipitation between (a) 1 mm and 750 mm and (b) 750 mm and 1500 mm up to the 729th day of the outbreak. The day-wise trend was comparatively higher for the countries having more than 1000 people per sq. km. Discussing the COVID-19 cases per million, the day-wise trend was higher for the HICs, followed by UMICs, LMICs, and LIC. ConclusionThe study highlights the need for targeted interventions and responses based on the specific circumstances and factors affecting each country, including their geographical location, temperature, precipitation levels, population density, and per capita income.

Positive association of SARS-CoV-2 RNA concentrations in wastewater and reported COVID-19 cases in Singapore – A study across three populations

Wastewater testing of SARS-CoV-2 has been adopted globally and has shown to be a useful, non-intrusive surveillance method for monitoring COVID-19 trends. In Singapore, wastewater surveillance has been widely implemented across various sites and has facilitated timely COVID-19 management and response.From April 2020 to February 2022, SARS-CoV-2 RNA concentrations in wastewater monitored across three populations, nationally, in the community, and in High Density Living Environments (HDLEs) were aggregated into indices and compared with reported COVID-19 cases and hospitalisations. Temporal trends and associations of these indices were compared descriptively and quantitatively, using Poisson Generalised Linear Models and Generalised Additive Models. National vaccination rates and vaccine breakthrough infection rates were additionally considered as confounders to shedding. Fitted models quantified the temporal associations between the indices and cases and COVID-related hospitalisations.At the national level, the wastewater index was a leading indicator of COVID-19 cases (p-value <0.001) of one week, and a contemporaneous association with hospitalisations (p-value <0.001) was observed. At finer levels of surveillance, the community index was observed to be contemporaneously associated with COVID-19 cases (p-value <0.001) and had a lagging association of 1-week in HDLEs (p-value <0.001). These temporal differences were attributed to differences in testing routines for different sites during the study period and the timeline of COVID-19 progression in infected persons.Overall, this study demonstrates the utility of wastewater surveillance in understanding underlying COVID-19 transmission and shedding levels, particularly for areas with falling or low case ascertainment. In such settings, wastewater surveillance showed to be a lead indicator of COVID-19 cases. The findings also underscore the potential of wastewater surveillance for monitoring other infectious diseases threats.

Evolution of Depression and Anxiety During the COVID-19 Pandemic and Across Demographic Groups in a Large Sample of U.S. Adults

Evolution of Depression and Anxiety During the COVID-19 Pandemic and Across Demographic Groups in a Large Sample of U.S. Adults

Difference in presentation, outcomes, and hospital epidemiologic trend of COVID-19 among first, second, and third waves: a review of hospital records and prospective cohort study.

The authors reviewed the hospital records between April 2020 and September 2021 and followed up on the patients for post-COVID complications. Older adult patients were predominantly affected during the third wave, and middle-aged patients were predominantly affected during the first and second waves. Men were predominantly admitted, considering the three waves, although more women were admitted in the second wave. Cough was more common in the second and third waves than in the first wave 522 (59.7%). Respiratory distress was the most common in the third wave, 251(67.1%), and least common in the first wave, 403 (46.1%). Anosmia was more common in the third wave 116 (31.2%). In the third wave, patients presenting in a critical state 23 (6.2%) and with severe disease 152 (40.8%) were more common. The hospital admission median (IQR) was longer in the first wave, 12 (8-20), than in other waves. More patients were admitted in the first wave (52%) than in the other waves, and patients received more oxygen in the third wave (75%) than in the other waves. Death occurred more commonly in the first wave (51%) than in the other waves. The positivity rate was higher in the third wave (22.8%) than in the other waves. In the third wave, the positivity rate was higher in women (24.3%) than in men. Post-COVID cough increased in the second wave, and fatigue was higher in the third wave than in the other waves. Tiredness and memory loss were greater during the second wave than in other waves. The authors found differences in the presentation, outcomes, and hospital epidemiologic trend of COVID-19 among the three waves.

Indonesia’s COVID-19 Trend After the End of a Public Health Emergency of International Concern: Preparation for an Endemic

Three years into the pandemic, the World Health Organization revoked the COVID-19 public health emergency of international concern on May 5, 2023. This decision sparked debate, notably around the possibility of a surge in cases due to the SARS-CoV-2 mutations. To manage this transition, the Indonesian government enacted stringent controls on case numbers. This case series study provided an overview of COVID-19 case trends in Indonesia following the revocation of public health emergency of international concern status by the World Health Organization. Data were collected for 5 weeks after the statement (May 6–June 10, 2023) from the COVID-19 Task Force’s official online platform of the Indonesian Ministry of Health. The trends were monitored in daily confirmed, active, recovered, and death cases, and analyzed the data using Microsoft Excel and Stata 16. The findings indicated a positive trend for Indonesia, with decreased daily confirmed cases (89.42%) and active cases (44.16%). Recovered cases accounted for 97.47%, higher than the global average (96%). Unfortunately, the death rate (2.38%) exceeded the global statistic (1%). These results highlighted the need for sustained vigilance, enhancement of the 3T strategy (testing, tracing, and treatment), and wider vaccination coverage. It remains critical to uphold the 3M protocols—mask-wearing, physical distancing, and hand hygiene—to prevent a potential rebound in cases, even without the previously designated public health emergency of international concern status,as the situation transitions toward endemic COVID-19.

An innovative ensemble model based on deep learning for predicting COVID-19 infection

Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.

A reaction–diffusion epidemic model with virus mutation and media coverage: Theoretical analysis and numerical simulation

In this paper, a novel COVID-19 reaction–diffusion model with virus mutation and media coverage is investigated. First, the solution’s uniform boundedness for the system is established. Then, the basic reproduction numbers for ordinary and mutant viruses spread in heterogeneous environments are defined. Furthermore, the endemic equilibrium’s asymptotic distribution for the system is explored. In addition, when one diffusion coefficient tends to zero and the other diffusion coefficients are greater than zero and fixed, the solution of the system will asymptotically approach endemic equilibrium. Next, a theoretical analysis of how high-frequency media coverage affects the development of the COVID-19 epidemic is conducted. Theoretical research shows that high-frequency media coverage will lead to the disappearance of the disease. Meantime, global sensitivity analysis on the basic reproduction numbers R01 and R02 are performed. Finally, theoretical simulations and instance predictions are carried out. Because of the complexity of the Shanghai epidemic and changes in management and control, the infection rates β1(t),β2(t) are given in the form of a piecewise function with more practical significance, and they are used to predict the epidemic trend of COVID-19 in Shanghai. Through a series of numerical simulations and analysis, the key indicators of the Shanghai COVID-19 epidemic are as follows: (1) The basic reproduction numbers in the early, middle, and late stages of COVID-19 are R¯0(1:34)=0.9152, R¯0(35:49)=3.1476, and R¯0(50:140)=0.6547, respectively; (2) This epidemic round in Shanghai will peak at 3,270 new daily confirmed cases on the 49th day (April 15); (3) The final size of the epidemic will reach 63,470 confirmed cases; (4) This round of COVID-19 epidemic in Shanghai, China, is expected to be fully cleared in late June to early July. The above conclusions are basically consistent with the facts. Of course, with the rise in temperature and strict control, the epidemic situation in Shanghai, China, is expected to be cleared earlier. Our results provide new ideas for preventing and controlling the COVID-19 epidemic.

Designing a Wastewater-Based Epidemiology Study at the U.S. Air Force Academy: Using Severe Acute Respiratory Syndrome Coronavirus 2 to Test a Sentinel System forEarly Disease Outbreak Detection.

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) in wastewater has been proposed as a sentinel surveillance epidemiological tool for detection of infectious disease at a community level and as a complementary approach to syndromic surveillance of infectious disease outbreaks. We have designed a study to test the presence and quantity of SARS-CoV2, the virus responsible for COVID19, in the wastewater treatment facility (WWTF) of the U.S. Air Force Academy. Wastewater samples were tested in the laboratory to quantify the amount of SARS-CoV2 RNA using reverse transcription-quantitative polymerase chain reaction. Raw SARS-CoV2 viral titer in wastewater was normalized to the viral titer of a fecal marker, pepper mild mottle virus, to correct for dilutions. Temporal and spatial trends of COVID19 were analyzed. Furthermore, we compared wastewater analysis results against clinical data to assist public health decisions. Preliminary data suggest that wastewater analysis can provide temporal and spatial trends of COVID19. The geographically discrete WWTF at the U.S. Air Force suggests that wastewater testing is a useful approach to developing a comprehensive sentinel surveillance system. Together with ongoing syndromic surveillance data, this proof-of-concept study seeks to determine whether early detection of SARS-CoV2 in a closed system WWTF correlates to changes in community and clinically reported COVID19. The well-documented population served by the geographically discrete WWTF at the U.S. Air Force Academy may serve to better elucidate the adjunctive role of wastewater testing in a comprehensive surveillance system. These results may be of particular interest to the DoD and local commanders given the WWTFs under their immediate control and the information that these studies may provide in support of operational readiness through early detection of disease outbreaks.

Analysis of perceptions and emotional repercussions in Twitter users in Colombia during the COVID-19 pandemic

IntroductionThe COVID-19 pandemic has negatively impacted mental health. Up to a quarter of the population has reported mental health disorders. This has been studied mainly from a nosological perspective, according to diagnostic criteria. Nevertheless, we did not find studies that have explored the daily expressions of the population. Our objective was to evaluate the perceptions of the COVID-19 pandemic and its repercussions on the emotional well-being of the Colombian population. MethodsWe performed a Twitter metrics and trend analysis. Initially, in the trend analysis, we calculated the average duration in hours of the 20 most popular trending topics of the day in Colombia and we grouped them into trends related to COVID-19 and unrelated trends. Subsequently, we identified dates of events associated with the pandemic relevant to the country, and they were related to the behaviour of the trends studied. Additionally, we did an exploratory analysis of these, selected the tweets with the greatest reach and categorised them in an inductive way to analyse them qualitatively. ResultsIssues not related to COVID-19 were more far-reaching than those related to coronavirus. However, a rise in these issues was seen on some dates consistent with important events in Colombia. We found expressions of approval and disapproval, solidarity and accusation. Inductively, we identified categories of informative tweets, humour, fear, stigma and discrimination, politics and entities, citizen complaints, and self-care and optimism. ConclusionsThe impact of the COVID-19 pandemic generates different reactions in the population, which increasingly have more tools to express themselves and know the opinions of others. Social networks play a fundamental role in the communication of the population, so this content could serve as a public health surveillance tool and a useful and accessible means of communication in the management of health crises.

COVID-19 transmission dynamics and the impact of vaccination: modelling, analysis and simulations.

Despite the lifting of COVID-19 restrictions, the COVID-19 pandemic and its effects remain a global challenge including the sub-Saharan Africa (SSA) region. Knowledge of the COVID-19 dynamics and its potential trends amidst variations in COVID-19 vaccine coverage is therefore crucial for policy makers in the SSA region where vaccine uptake is generally lower than in high-income countries. Using a compartmental epidemiological model, this study aims to forecast the potential COVID-19 trends and determine how long a wave could be, taking into consideration the current vaccination rates. The model is calibrated using South African reported data for the first four waves of COVID-19, and the data for the fifth wave are used to test the validity of the model forecast. The model is qualitatively analysed by determining equilibria and their stability, calculating the basic reproduction number and investigating the local and global sensitivity analysis with respect to . The impact of vaccination and control interventions are investigated via a series of numerical simulations. Based on the fitted data and simulations, we observed that massive vaccination would only be beneficial (deaths averting) if a highly effective vaccine is used, particularly in combination with non-pharmaceutical interventions. Furthermore, our forecasts demonstrate that increased vaccination coverage in SSA increases population immunity leading to low daily infection numbers in potential future waves. Our findings could be helpful in guiding policy makers and governments in designing vaccination strategies and the implementation of other COVID-19 mitigation strategies.