COVID-19 Cases Research Articles

Increased Activation Markers of Adaptive Immunity in Patients with Severe COVID-19.

Introduction: COVID-19 is a pandemic disease and is widespread over the world. This disease shows a 5.1% mortality. The understanding of the disease has expanded rapidly in many areas, including virological, epidemiological, clinical, and management dimensions. To better understand the inflammatory and immune profiles that impact the pathogenesis and development of severe COVID-19 symptoms, further studies are essential. This research aims to explore the inflammatory and adaptive immune responses associated with COVID-19, considering factors such as genetic diversity and environmental exposure among Saudi patients. The goal is to determine if patients with severe COVID-19 exhibit different disease phenotypes. Materials and Methods: This case-control study includes 115 participants (healthy and with COVID-19 infection), 55 of which had confirmed cases of COVID-19 in intensive care units (ICUs) at different hospitals in Makkah City, Saudi Arabia. Whole blood samples were collected from June to September 2021 for cellular analyses, and inflammation marker data were collected from hospital records. The expression of activation markers on B (CD27 and CD38) and T cells (CD27 and HLA-DR) was obtained using the flow cytometry technique. Also, serum was collected for cytokine measurements, including IL-6, INF-γ, and TNF- α. Results: The results indicated that lymphopenia and excessive T cell activation were more prevalent in severe cases than in healthy individuals. Furthermore, the results revealed that severe COVID-19 patients had an increased frequency of CD19+ B cells, with changes in B cell subsets. The current study implies impairment and changes in the phenotype of adaptive cells (including T and B cells), with an increase in HLA-DR molecules and inflammation markers with pro-inflammatory cytokines in severe COVID-19 cases. Conclusions: The current study implies impairment and changes in the phenotype of adaptive cells (including T and B cells), with an increase in HLA-DR molecules and inflammation markers in severe COVID-19 cases, which could be targeted for therapeutic interventions. This might be a valuable approach for the diagnosis and treatment of severe COVID-19 cases.

DNA Damage in Moderate and Severe COVID-19 Cases: Relation to Demographic, Clinical, and Laboratory Parameters.

The ability of the SARS-CoV-2 virus to cause DNA damage in infected humans requires its study as a potential indicator of COVID-19 progression. DNA damage was studied in leukocytes of 65 COVID-19 patients stratified by sex, age, and disease severity in relation to demographic, clinical, and laboratory parameters. In a combined group of COVID-19 patients, DNA damage was shown to be elevated compared to controls (12.44% vs. 5.09%, p < 0.05). Severe cases showed higher DNA damage than moderate cases (14.66% vs. 10.65%, p < 0.05), and males displayed more damage than females (13.45% vs. 8.15%, p < 0.05). DNA damage is also correlated with international normalized ratio (INR) (r = 0.471, p < 0.001) and creatinine (r = 0.326, p < 0.05). In addition to DNA damage, severe COVID-19 is associated with age, C-reactive protein (CRP), and creatinine. Receiver operating characteristic analysis identified age, INR, creatinine, DNA damage, and CRP as significant predictors of disease severity, with cut-off values of 72.50 years, 1.46 s, 78.0 µmol/L, 9.72%, and 50.0 mg/L, respectively. The results show that DNA damage correlates with commonly accepted COVID-19 risk factors. These findings underscore the potential of DNA damage as a biomarker for COVID-19 severity, suggesting its inclusion in prognostic assessments to facilitate early intervention and improve patient outcomes.

Accuracy of COVID-19 Prediction Modeling Techniques

The unprecedented impact of the COVID-19 pandemic has revealed that forecasting capability is critically needed in making strategic decisions and formulating reasonable countermeasures. This study aimed to assess the predictive accuracy in forecasting the numbers of COVID-19 cases using Thailand’s national COVID-19 surveillance database from January 2020– June 2021 based on three analytical models: a susceptible-exposed-infectious-recovery compartmental model, an auto-regressive integrated moving average model, and a long short-term memory (LSTM) network model. All forecasting methods had model parameters adjusted weekly according to the most recent situation and predictive accuracy measures, including the mean absolute percent error (MAPE). We found that the MAPE values ranged from 19.65%–22.54%, 28.95%–32.35%, 47.78%–53.55%, and 75.03–84.91% for forecasting one, two, four, and eight weeks ahead, respectively. Among the three models, the LSTM model had slightly higher accuracy than the other two models within the same forecasting range. These prediction models can be used for short-range forecasts in other similar settings while long-range forecasting requires monitoring and updating periodically.

Assessment of Long-Term Changes in Knowledge and Attitudes of Household Contacts of COVID-19 Cases in Northern Spain.

This study aims to describe the long-term changes in the knowledge of, and attitudes towards, COVID-19 and its preventive measures in northern Spain. A telephonic survey was performed among household contacts of COVID-19 cases in Catalonia and Navarre between May 2022 and December 2023. Knowledge and attitudes were assessed through 12 questions using a Likert scale, and responses were grouped as correct or incorrect. The change from baseline to the 6-month follow-up was evaluated with the absolute difference (AD) using the proportion of correct answers. At baseline, 299 subjects were contacted, of whom 63.2% (189) completed the 6-month follow-up. Correct knowledge of transmission (>85%) and the use of preventive measures (>92%) were observed at baseline and maintained over time. The attitudes towards face mask use remained adequate over the course of six months (>79%). However, attitudes regarding the use of face masks indoors (AD = -16.4%; p < 0.001) and those who thought that COVID-19 had a negative impact on their lives (AD = -16.5%; p < 0.001) decreased after 6 months. In the post-acute phase of the pandemic, household contacts maintained the correct level of knowledge towards COVID-19, while some attitudes decreased. These results should serve as a guide for health policy makers in decision-making in case of a new increase in the incidence of SARS-CoV-2.

Wastewater-Based Surveillance Reveals the Effectiveness of the First COVID-19 Vaccination Campaigns in Assisted Living Facilities.

The COVID-19 pandemic has disproportionately affected vulnerable populations, including residents of assisted living facilities (ALFs). This study investigates the impact of non-pharmaceutical interventions (NPIs) and mass vaccination campaigns on SARS-CoV-2 transmission dynamics within four ALFs in Maricopa County, Arizona, United States from January to April 2021. Initial observations reveal a significant SARS-CoV-2 prevalence in Maricopa County, with 7452 new COVID-19 cases reported on 4 January 2021. Wastewater surveillance indicates elevated viral loads within ALFs with peak concentrations reaching 1.35 × 107 genome copies/L at Facility 1 and 4.68 × 105 copies/L at Facility 2. The implementation of NPIs, including isolation protocols, resulted in a rapid decline in viral loads in wastewater. Following mass vaccination campaigns, viral loads reduced across all facilities, except Facility 4. Facility 1 demonstrated a mean viral load decrease from 1.65 × 106 copies/L to 1.04 × 103 copies/L post-vaccination, with a statistically significant U-statistic of 28.0 (p-value = 0.0027). Similar trends are observed in Facilities 2 and 3, albeit with varying degrees of statistical significance. In conclusion, this study provides evidence supporting the role of NPIs and vaccination campaigns in controlling SARS-CoV-2 transmission within ALFs.

A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo

Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number (R0) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.

ScPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

Epidemiology of COVID-19 in Berlin-Neukölln nursing homes

BackgroundThe COVID-19 pandemic has affected various urban population groups in different ways. Earlier studies have shown that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disproportionally impacts nursing home residents by increasing morbidity and mortality following viral exposure. However, little is known about the epidemiology of this disease in detail. Therefore, the objective of this study is to analyze the development of the COVID-19 pandemic in 14 nursing homes across Berlin-Neukölln, Germany, during pandemic waves 1 to 5 (Feb 2020 - May 2022). MethodsReporting data to the Neukölln Department of Public Health on COVID-19 cases in connection with nursing homes were extracted from the SORMAS database. The case fatality rates (CFRs) and odds ratios (ORs) of demographic parameters, prevalent variants of concern (VOCs) and vaccine availability were calculated. In addition, the temporal course in waves 1–5 in Neukölln and the relevant government measures were examined. ResultsData collected from nursing homes providing age-dependent physical care revealed that 1.9 % of the total 108,600 cases registered in Berlin-Neukölln during the study period were related one of the 14 facilities. Compared to the general population in Neukölln, nursing homes exhibited a 20-fold increase in the CFR. Notably, nursing homes with higher bed capacities displayed a greater CFR than did smaller nursing homes. Similarly, elderly residents living in nursing homes faced a much greater mortality rate than did their counterparts living outside of medical settings (OR = 3.5). The original wild-type SARS-CoV-2 strain had the most severe direct impact, with a CFR of 16.7 %, compared to the alpha (CFR = 6.9 %), delta (CFR = 10.2 %) and omicron (CFR = 2.8 %) variants in nursing homes. Interestingly, the number of infections increased following vaccination campaigns, but this trend was accompanied by a decrease in the number of deaths from 2.6 to 1.1 per week. As a result, the CFR significantly decreased from 18.4 to 5.5, while still exceeding the mean CFR compared to that of the general population of Neukölln. ConclusionsOur findings reveal the changing patterns of outbreak frequency and severity across the five pandemic waves. They highlight the crucial role of rapid vaccination programs for residents, staff, visitors, and third-party services in safeguarding nursing homes. Additionally, improvements in containment and cluster strategies are essential in prevaccination scenarios to prevent future infection traps for elderly individuals in long-term care facilities. The presented data highlight the importance of tailored protection measures for one of the most vulnerable populations in our society.

SARS-CoV-2 burden on the floor was associated with COVID-19 cases and outbreaks in two acute care hospitals: a prospective cohort study

SARS-CoV-2 burden on the floor was associated with COVID-19 cases and outbreaks in two acute care hospitals: a prospective cohort study

The impact of the COVID-19 pandemic on presentations with suicidal behaviour in a tertiary hospital Emergency Centre in the Eastern Cape, South Africa–A cross-sectional analysis

IntroductionSuicidal behaviour is a public health emergency, causing an estimated one million deaths globally each year. The impact of the COVID-19 pandemic on suicidal behaviour is not fully understood. This study aimed to describe the impact of the COVID-19 pandemic on presentations with suicidal behaviour at a tertiary hospital in the Eastern Cape, South Africa. MethodThis study was a retrospective cross-sectional analysis of patients with fatal and nonfatal suicidal behaviour who presented to Frere Hospital Emergency Centre in the Eastern Cape, South Africa. The analysis included three study periods: August to October 2019 (pre-COVID-19), August to October 2020 (wave 1), and August to October 2021(wave 3). ResultsA total of 16 419 patients presented to the Frere Hospital Emergency Centre during the study period, with 6 204 in 2019, 4 909 in 2020, and 5 306 in 2021. These correspond to the research periods from August to October for each successive year. The overall prevalence of suicidal behaviour was 1.6 % but decreased marginally during the pandemic (1.8 % in 2019 vs. 1.4 % in 2021) – contrary to what occurred in high-income countries. The prevalence demonstrated a clinically insignificant stepwise decline as the pandemic progressed and was inversely associated with the number of daily new COVID-19 cases nationally. A significant increase in the emergency centre's total length of stay was however observed during the COVID-19 period (143 vs. 80 min, p < 0.001). The high proportion (66 %) of patients with suicidal behaviour requiring admissions increased further during the COVID-19 study period, placing an additional burden on inpatient disciplines. The proportion of social worker consultations increased significantly during the COVID-19 period (65% vs. 44 %, p<.05). ConclusionThe COVID-19 pandemic marginally reduced the prevalence of presentations with suicidal behaviour to the Emergency Centre, contrary to what was observed in high-income countries.

Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model

BackgroundIt is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu’s search volume, in the early warning and predicting the epidemic trend of COVID-19.MethodsThe daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable.ResultsThe Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of “Influenza” and “Pneumonia” in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of “SARS”, “Pneumonia”, “Coronavirus” in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while “Influenza” changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of “COVID-19”, “Pneumonia”, “Coronavirus”, “SARS” and “Mask” could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February.ConclusionThe Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system.

Using the moral intensity framework to examine whether colleges should have reopened in late 2020 for in-person learning: an in-depth analysis of data from the United States with global implications for higher education

ABSTRACT The decision whether to reopen universities for in-person learning in late 2020 relied on ethical decision-making where the consequences were dire to the mission of the institution, health of the community, financial well-being of the institution and employees, and had political ramifications. The risks that universities faced worldwide included health risks to students, faculty, staff, and the community; financial risks to the institution and employees; and mission risks of the institution. Each of these risks had ethical implications. University reopening decisions were made in the United States as the number of COVID-19 cases and deaths were surging globally. Applying a moral intensity framework that assesses the in-person learning decision against six dimensions and using data collected from 62 universities, we show that social consensus and risk mitigation measures designed to reduce the magnitude of consequences and temporal immediacy were important factors in deciding to offer in-person classes. An ex-post facto analysis of COVID-19 infection and death data suggests that university administrators made an ethical decision. When facing a similar crisis management scenario in the future, a moral intensity framework could help university administrators worldwide understand the issues and allow them to make an ethical decision.

Impact of the 2023/24 autumn-winter COVID-19 seasonal booster campaign in preventing severe COVID-19 cases in Italy (October 2023–March 2024)

We assessed the impact of the 2023/2024 COVID-19 vaccination campaign in Italy by estimating the number of averted COVID-19 severe cases (i.e. COVID-19 associated hospitalisations or deaths) between October 2023 and March 2024, in those aged ≥60 years. We estimated that 565 (95 % CI: 497–625) cases, corresponding to 2.1 % (95 % CI: 1.8–2.3) of the expected cases without a vaccination campaign, were averted. We simulated three vaccination coverage scenarios: 50 %, 75 %, 90 % (versus the observed 10.7 %), finding that 9.7 % (95 % CI: 8.5–10.7); 14.5 % (95 % CI: 12.8–16.1); and 17.4 % (95 % CI: 15.3–19.3) of the expected cases would have been averted, respectively.

The seroprevalence of SARS-CoV-2-specific antibodies in Australian children: A cross-sectional study.

Following reduction of public health and social measures concurrent with SARS-CoV-2 Omicron emergence in late 2021 in Australia, COVID-19 case notification rates rose rapidly. As rates of direct viral testing and reporting dropped, true infection rates were most likely to be underestimated. To better understand infection rates and immunity in this population, we aimed to estimate SARS-CoV-2 seroprevalence in Australians aged 0-19 years. We conducted a national cross sectional serosurvey from June 1, 2022, to August 31, 2022, in children aged 0-19 years undergoing an anesthetic procedure at eight tertiary pediatric hospitals. Participant questionnaires were administered, and blood samples tested using the Roche Elecsys Anti-SARS-CoV-2 total spike and nucleocapsid antibody assays. Spike and nucleocapsid seroprevalence adjusted for geographic and socioeconomic imbalances in the participant sample compared to the Australian population was estimated using multilevel regression and poststratification within a Bayesian framework. Blood was collected from 2,046 participants (median age: 6.6 years). The overall adjusted seroprevalence of spike-antibody was 92.1% (95% credible interval (CrI) 91.0-93.3%) and nucleocapsid-antibody was 67.0% (95% CrI 64.6-69.3). In unvaccinated children spike and nucleocapsid antibody seroprevalences were 84.2% (95% CrI 81.9-86.5) and 67.1% (95%CrI 64.0-69.8), respectively. Seroprevalence was similar across geographic remoteness index and socioeconomic quintiles. Nucleocapsid antibody seroprevalence increased with age while the point seroprevalence of the spike antibody seroprevalence decreased in the first year of life and then increased to 97.8 (95% Crl 96.1-99.2) by 12-15 years of age. Most Australian children and adolescents aged 0-19 years, across all jurisdictions were infected with SARS-CoV-2 by August 2022, suggesting rapid and uniform spread across the population in a very short time period. High seropositivity in unvaccinated children informed COVID-19 vaccine recommendations in Australia.

The effects of modal value and imperative mood on self-predicted compliance to health guidance: the case of COVID-19

The effects of modal value and imperative mood on self-predicted compliance to health guidance: the case of COVID-19

Integrating the triple bottom line of sustainability, resilience strategies, and product perishability consideration to design a pharmaceutical supply chain network: A COVID-19 case study

This paper aims to design a resilient and sustainable pharmaceutical supply chain network under the perishability of medicine in which a multi-objective nonlinear mathematical model is formulated. To this end, four objective functions seek to minimize total cost, maximize the social indicators, minimize CO2 emission and minimize de-resilience measures. Moreover, the three main categories of resilience strategies are integrated to mitigate the severe impacts of disruption. In order to solve the model, lexicographic goal programming is applied for small-scale problems, and NSGA-II is utilized for large-scale problems. The applicability of the proposed model is demonstrated by implementing it in a real case study during the COVID-19 situation. Also, a set of sensitivity analyses is conducted to validate the model and show the behavior of the objective functions. The results reveal the superiority of the resilient model with integrated strategies. Eventually, the Pareto front solutions are provided to quantify the trade-offs in satisfying the conflicting objective functions.

Correlation Between Initial Lung Involvement on CT Scan, Assessed by Total Severity Score, and Pulmonary Outcomes After Three Months in Patients with Severe COVID-19

Background: Patients with severe COVID-19 experience various respiratory complications, which can have potential long-term effects. Objectives: This study investigates the long-term pulmonary consequences of severe COVID-19 and their relationship with the severity of lung involvement. Methods: All patients who survived severe COVID-19 in the ICU were selected for the study. A radiologist reviewed the chest CT scans, and patients were categorized based on their total severity score (TSS) into two groups: Group A (TSS ≤ 7) and Group B (TSS &gt; 7). Patients were followed up after three months with a chest CT scan, spirometry, SpO2 measurement, and a dyspnea score assessment. Results: The mean age was 54.69 ± 12.51 for Group A and 55.31 ± 12.73 for Group B. Groups A and B had 23 (46.9%) and 39 (39.4%) female patients, respectively. Patients in Group B had significantly lower SpO2 and a prolonged length of hospitalization. Group B also experienced more severe dyspnea and reduced lung function, as observed in spirometry during the three-month follow-up. The total severity score decreased significantly after three months in all patients (from 14.42 ± 5.90 to 6.68 ± 4.79; P &lt; 0.001). After logistic regression analysis, non-invasive ventilation was independently associated with a higher TSS (OR = 0.45 when comparing Group A to Group B; P = 0.028). Conclusions: The lungs are the most affected organ by COVID-19, making it crucial to investigate the effects of the virus on pulmonary function. Our study showed that patients with higher TSS experienced greater reductions in lung function during the three-month follow-up. Additionally, the use of non-invasive ventilation was independently associated with a higher TSS in severe COVID-19 cases.

Covid-19 Vaccination Impact on Four Asean Countries’ Stock With Spatial Dependency: A Comparison of Panel and Geographically Weighted Regression

Research about various policies and responses toward COVID-19 cases and its impact on stocks has grown recently. It shows that spatial influence is one of the keys in this research. The pandemic is not free from spatial dependence regarding how it indirectly impacts a country’s economy. Each country has different policies to handle COVID-19, such as lockdowns and vaccination. WHO stated that all countries require vaccination to build human immunity against COVID-19 in the future. Naturally, ASEAN implemented this policy; thus, it is crucial to see the extent of the impact of vaccination on the ASEAN economy. However, the residuals have heterogeneity problems when using the panel regression model. One of the reasons is that there is spatial dependence, especially when modeling the COVID-19 pandemic. Therefore, comparing panel regression with a geographically weighted regression panel (GWR-Panel) is substantial when exploring the reaction of stock returns to vaccination and positive cases of COVID-19 in Indonesia, Malaysia, Singapore, and Thailand

Condições de trabalho e saúde de catadores de materiais recicláveis no período de pandemia da COVID-19

Recyclable material collectors belong to a professional category frequently exposed to various risks associated with working conditions in waste management. This study aimed to identify the working conditions and health of collectors in the municipality of Ponta Grossa-PR during the COVID-19 pandemic. The research utilized a sample composed of 83 associated collectors. Among the findings, it was identified that the majority of collectors were female (72.3%). The professional profile demonstrated that collectors engaged in this work out of necessity (34.0%) and because it was the only opportunity available (26.0%). Regarding health and occupational risks during the pandemic, it was found that although the collectors had developed protective strategies to avoid COVID-19 infection (94.0%), nearly half of the workers (47.0%) had contact with potentially infectious waste. During this period, activities were suspended only for those of advanced age (16.9%). Concerning COVID-19 cases, 98.8% of participants reported not having contracted the disease, but this result does not dismiss the fact that the collectors were exposed to contagion risks. From this diagnosis, it was possible to conclude that the COVID-19 pandemic caused various changes in the lives of the collectors, revealing the need for the development of emergency preventive actions for the health of these workers. For this reason, it is suggested that studies continue to guide the development of strategies that can be adopted in other widespread epidemic situations, similar to those experienced during the COVID-19 period.

Factors Affecting The Number Of Domestic Flights In Indonesia During Covid-19 Pandemic Using SARIMAX Method

Indonesia, which consists of thousands of large and small islands, relies heavily on-air transportation to support mobility between regions. As many as 80% of Indonesia's total air transportation passengers are domestic flight passengers. This shows how vital domestic flights are in Indonesia's air transportation system. However, in 2020, the COVID-19 pandemic had an impact that resulted in a decrease in the number of domestic flights in Indonesia. Therefore, an analysis is needed to determine the factors that affect the number of domestic flights in Indonesia. This study uses the SARIMAX method, a time series regression with the addition of seasonal factors and other variables or exogenous factors that significantly affect the model to improve the model's accuracy. Several exogenous variables are considered, including the number of operating civil aviation airports, positive daily cases of COVID-19, calendar effects during Eid al-Fitr and New Year's Day, and social restriction policies. The results showed that the number of operating airports one week before Eid al-Fitr, one week during Eid al-Fitr, one week before New Year, and Emergency PPKM significantly influenced the number of domestic flights. These variables offer pivotal insights into the influence of external factors on domestic flight patterns, exerting significant impacts on passenger travel behavior and subsequently influencing domestic flight volume. The integration of these variables in the SARIMAX model allows for a comprehensive analysis of the complex dynamics influencing domestic air travel in Indonesia. The best SARIMAX model obtained is SARIMAX (1,1,1)(4,1,1)7 with a MAPE value of 5.35% and a coefficient of determination is 97%.