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In this paper, we propose a numerical algorithm to obtain the optimal epidemic parameters for a time-dependent Susceptible-Unidentified infected-Confirmed (tSUC) model. The tSUC model was developed to investigate the epidemiology of unconfirmed infection cases over an extended period. Among the epidemic parameters, the transmission rate can fluctuate significantly or remain stable due to various factors. For instance, if early intervention in an epidemic fails, the transmission rate may increase, whereas appropriate policies, including strict public health measures, can reduce the transmission rate. Therefore, we adaptively estimate the transmission rate to the given data using the linear change points of the number of new confirmed cases by the given cumulative confirmed data set, and the time-dependent transmission rate is interpolated based on the estimated transmission rates at linear change points. The proposed numerical algorithm preprocesses actual cumulative confirmed cases in India to smooth it and uses the preprocessed data to identify linear change points. Using these linear change points and the tSUC model, it finds the optimal time-dependent parameters that minimize the difference between the actual cumulative confirmed cases and the computed numerical solution in the least-squares sense. Numerical experiments demonstrate the numerical solution of the tSUC model using the optimal time-dependent parameters found by the proposed algorithm, validating the performance of the algorithm. Consequently, the proposed numerical algorithm calculates the time-dependent transmission rate for the actual cumulative confirmed cases in India, which can serve as a basis for analyzing the COVID-19 pandemic in India. [ABSTRACT FROM AUTHOR]

South Korea's remarkable success in controlling the spread of COVID-19 during the pre-Omicron period was based on extensive contact tracing and large-scale testing. Here we suggest a general criterion for tracing and testing based on South Korea's experience, and propose a new framework to assess tracing and testing. We reviewed papers on South Korea's response to COVID-19 to capture its concept of tracing and testing. South Korea expanded its testing capabilities to enable group tracing combined with preemptive testing, and to conduct open testing. According to our proposed model, COVID-19 cases are classified into 4 types: confirmed in quarantine, source known, source unknown, and unidentified. The proportion of the first two case types among confirmed cases is defined as "traced proportion", and used as the indicator of tracing and testing effectiveness. In conclusion, South Korea successfully suppressed COVID-19 transmission by maintaining a high traced proportion (> 60%) using group tracing in conjunction with preemptive testing as a complementary strategy to traditional contact tracing. [ABSTRACT FROM AUTHOR]

Background: Self-medication practices involve the use of medications without healthcare professional requests. The threat of coronavirus disease 2019 (COVID-19) caused the practice of a fittest to survive action, with the assumption that something is better than nothing. Moreover, owing to the lack of effective treatment for COVID-19, the general public has shifted toward self-medication and symptomatic treatment, with approximately 80% of people stockpiling medication for use during the pandemic. Thus, this study aimed to assess the factors associated with self-medication practices during the COVID-19 pandemic crisis in southwestern Ethiopia. Methods: A community-based cross-sectional study design was employed at selected drug retail outlets in southwestern Ethiopia for 415 community pharmacy clients from July 1, 2021, to September 1, 2021. Purposive sampling techniques were employed to select five drug retail outlets on the basis of high patient flows, and we took the study participants until the required quota allotted to each selected drug retail outlet had been filled. Bivariable and multivariable logistic regression analyses were employed to identify factors associated with self-medication. AORs with 95% CIs were used to report associations, and the level of significance was set at P < 0.05. Results: Self-medication was significantly associated with being female (AOR 3.51, 95% CI 1.04–12.41), having a college education or above (AOR 47, 95% CI 4.32–55.21), time wastage at public health facilities (AOR 2.71, 95% CI 3.47–5.21), being afraid of contracting COVID-19 (AOR 0.006, 95% CI 0.004–0.185), and having high fees at public health facility (AOR 0.006, 95% CI 0.004–0.185). The most frequently used medications to treat or prevent the COVID-19 pandemic were analgesics (42.4%) and cold medicines (29.5%). Headache (22.2%), fever (13.2%), respiratory infection (14.3%), and cold (21.4%) were the most frequently reported symptoms of the COVID-19 pandemic. Conclusion: This study revealed high self-medication practices among study area residents during the COVID-19 pandemic. The primary reasons for self-medication in the context of COVID-19 are fear of the pandemic and time wastage at public health facilities. Therefore, special attention should be given to educating public and health care providers on the types of illnesses that can be self-diagnosed and self-treated and the types of drugs to be used for self-medication. [ABSTRACT FROM AUTHOR]

Background: Studying the characteristics of hospitalized Coronavirus Disease 2019 (COVID-19) patients is vital for understanding the disease and preparing for future outbreaks. The aim of this study was to analyze and describe the clinical profiles and factors associated with mortality among COVID-19 patients admitted to Jimma Medical Center COVID-19 Treatment Center (JMC CTC) in Ethiopia. Methods: All confirmed COVID-19 patients admitted to JMC CTC between 17 April 2020 and 05 March 2022 were included in this study. Socio-demographic data, clinical information, and outcome variables were collected retrospectively from medical records and COVID-19 database at the hospital. Bivariable and multivariable analyses were performed to determine factors associated with COVID-19 severity and mortality. A P-value < 0.05 was considered statistically significant. Results: A total of 542 confirmed COVID-19 patients were admitted to JMC CTC, of which 322 (59.4%) were male. Their median age was 48 years (IQR 32–64). About 51% (n = 277) of them had severe COVID-19 upon admission. Patients with hypertension [AOR: 2.8 (95% CI: 1.02–7.7, p = 0.046)], diabetes [AOR: 8.8 (95% CI: 1.2–17.3, p = 0.039)], and underlying respiratory diseases [AOR: 18.8 (95% CI: 2.06–71.51, p = 0.009)] were more likely to present with severe COVID-19 cases. Overall, 129 (23.8%) died in the hospital. Death rate was higher among patients admitted with severe disease [AHR = 5.5 (3.07–9.9) p < 0.001)] and those with comorbidities such as hypertension [AHR = 3.5 (2.28–5.41), p < 0.001], underlying respiratory disease [AHR = 3.4 (1.97–5.94), p < 0.001], cardiovascular disease (CVDs) [AHR = 2.8 (1.73–4.55), p < 0.001], and kidney diseases [AHR = 3.7 (2.3–5.96), p < 0.001]. Conclusion: About half of COVID-19 cases admitted to the hospital had severe disease upon admission. Comorbidities such as hypertension, diabetes, and respiratory diseases were linked to severe illness. COVID-19 admissions were associated with high inpatient mortality, particularly among those with severe disease and comorbidities. [ABSTRACT FROM AUTHOR]

Objective: To assess the impact of an intervention package on the prescription of antibiotic and subsequently the rate of clinical recovery for non-severe acute febrile illnesses at primary health centers. Methods: Patients over 6 months of age presenting to primary health care centres with fever or history of fever within the past 7 days were randomized to receive either the intervention package constituted of point-of-care tests including COVID-19 antigen tests, a diagnostic algorithm and training and communication packages, or the standard practice. The primary outcomes were antibiotic prescriptions at Day 0 (D0) and the clinical recovery at Day 7 (D7). Secondary outcomes were non-adherence of participants and parents/caregivers to prescriptions, health workers' non-adherence to the algorithm, and the safety of the intervention. Results: A total of 1098 patients were enrolled. 551 (50.2%) were randomized to receive the intervention versus 547 (49.8%) received standard care. 1054 (96.0%) completed follow-up and all of them recovered at D7 in both arms. The proportion of patients with antibiotic prescriptions at D0 were 33.2% (183/551) in the intervention arm versus 58.1% (318/547) under standard care, risk difference (RD) -24.9 (95% CI -30.6 to -19.2, p < 0.001), corresponding to one more antibiotic saved every four (95% CI: 3 to 5) consultations. This reduction was also statistically significant in children from 6 to 59 months (RD -34.5; 95% CI -41.7 to -27.3; p < 0.001), patients over 18 years (RD -35.9; 95%CI -58.5 to -13.4; p = 0.002), patients with negative malaria test (RD -46.9; 95% CI -53.9 to -39.8; p < 0.001), those with a respiratory diagnosis (RD -48.9; 95% CI -56.9 to -41.0, p < 0.001) and those not vaccinated against COVID-19 (-24.8% 95%CI -30.7 to -18.9, p-value: <0.001). A significant reduction in non-adherence to prescription by patients was reported (RD -7.1; 95% CI -10.9 to -3.3; p < 0.001). Conclusion: The intervention was associated with significant reductions of antibiotic prescriptions and non-adherence, chiefly among patients with non-malaria fever, those with respiratory symptoms and children below 5 years of age. The addition of COVID-19 testing did not have a major impact on antibiotic use at primary health centers. Trial registration: Clinitrial.gov; NCT04081051 registered on 06/09/2019. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a numerical algorithm to obtain the optimal epidemic parameters for a time-dependent Susceptible-Unidentified infected-Confirmed (tSUC) model. The tSUC model was developed to investigate the epidemiology of unconfirmed infection cases over an extended period. Among the epidemic parameters, the transmission rate can fluctuate significantly or remain stable due to various factors. For instance, if early intervention in an epidemic fails, the transmission rate may increase, whereas appropriate policies, including strict public health measures, can reduce the transmission rate. Therefore, we adaptively estimate the transmission rate to the given data using the linear change points of the number of new confirmed cases by the given cumulative confirmed data set, and the time-dependent transmission rate is interpolated based on the estimated transmission rates at linear change points. The proposed numerical algorithm preprocesses actual cumulative confirmed cases in India to smooth it and uses the preprocessed data to identify linear change points. Using these linear change points and the tSUC model, it finds the optimal time-dependent parameters that minimize the difference between the actual cumulative confirmed cases and the computed numerical solution in the least-squares sense. Numerical experiments demonstrate the numerical solution of the tSUC model using the optimal time-dependent parameters found by the proposed algorithm, validating the performance of the algorithm. Consequently, the proposed numerical algorithm calculates the time-dependent transmission rate for the actual cumulative confirmed cases in India, which can serve as a basis for analyzing the COVID-19 pandemic in India.

Background: The global impact of the coronavirus disease 2019 (COVID-19) pandemic has resulted in significant morbidity and mortality. Immunocompromised patients, particularly those treated for B-cell lymphoma, have shown an increased risk of persistent infection with SARS-CoV-2 and severe outcomes and mortality. Multi-mutational SARS-CoV-2 variants can arise during the course of such persistent cases of COVID-19. No optimal, decisive strategy is currently available for patients with persistent infection that allows clinicians to sustain viral clearance, determine optimal timing to stop treatment, and prevent virus reactivation. We introduced a novel treatment combining antivirals, neutralizing antibodies, and genomic analysis with frequent monitoring of spike-specific antibody and viral load for immunocompromised patients with persistent COVID-19 infection. The aim of this retrospective study was to report and evaluate the efficacy of our novel treatment for immunocompromised B-cell lymphoma patients with persistent COVID-19 infection. Methods: This retrospective descriptive analysis had no controls. Patients with B-cell lymphoma previously receiving immunotherapy including anti-CD20 antibodies, diagnosed as having COVID-19 infection, and treated in our hospital after January 2022 were included. We selected anti-SARS-CoV-2 monoclonal antibodies according to subvariants. Every 5 days, viral load was tested by RT-PCR, with antivirals continued until viral shedding was confirmed. Primary outcome was virus elimination. Independent predictors of prolonged viral shedding time were determined by multivariate Cox regression. Results: Forty-four patients were included in this study. Thirty-five patients received rituximab, 19 obinutuzumab, and 26 bendamustine. Median treatment duration was 10 (IQR, 10–20) days; 22 patients received combination antiviral therapy. COVID-19 was severe in 16 patients, and critical in 2. All patients survived, with viral shedding confirmed at median 28 (IQR, 19–38) days. Bendamustine use or within 1 year of last treatment for B-cell lymphoma, and multiple treatment lines for B-cell lymphoma significantly prolonged time to viral shedding. Conclusions: Among 44 consecutive patients treated, anti-SARS-CoV-2 monoclonal antibodies and long-term administration of antiviral drugs, switching, and combination therapy resulted in virus elimination and 100% survival. Bendamustine use, within 1 year of last treatment for B-cell lymphoma, and multiple treatment lines for B-cell lymphoma were the significant independent predictors of prolonged viral shedding time. [ABSTRACT FROM AUTHOR]

Background: The World Health Organization recommended the use of chemical-based disinfectants as an effective prevention of the COVID-19 pandemic. However, calls for poisoning were reported in several medical centers. The widespread use of chemical-based disinfectants as a preventive measure during the COVID-19 pandemic has underscored potential gaps in community awareness and performance, posing health risks. This study evaluates and compares levels of awareness and performance regarding the safe use of disinfectants in Jordan and UAE. Methods: The study was conducted between October 2022 and June 2023 via an online questionnaire. Data of respondents from Jordan (n = 828) and UAE (n = 619) were analyzed using SPSS. ANOVA, Mann-Whitney, and Kruskal-Wallis tests evaluated significant differences in awareness and performance levels across different demographic groups in Jordan/UAE and between them. Spearman's correlation test examined the correlation between awareness and performance among respondents. Multinomial logistic regression analysis explored associations between various variables and awareness/performance levels within each population. Results: Findings reveal weak awareness (72.4% and 9.03% in UAE and Jordan, respectively) and moderate performance level (98.8% in UAE and Jordan), with a weak correlation (UAE, rho = 0.093; Jordan, rho = 0.164) observed between the two countries (P < 0.05). Multinomial logistic regression analysis indicates gender-related associations with awareness levels and education-related associations with performance levels. Conclusions: The study emphasizes the urgent need for awareness campaigns and workshops to promote safer disinfectant practices to develop effective interventions aligning with sustainable development goals. [ABSTRACT FROM AUTHOR]

Introduction: Carbapenemase-producing Enterobacterales (CPE) are an important public health threat, with costly operational and economic consequences for NHS Integrated Care Systems and NHS Trusts. UK Health Security Agency guidelines recommend that Trusts use locally developed risk assessments to accurately identify high-risk individuals for screening, and implement the most appropriate method of testing, but this presents many challenges. Methods: A convenience sample of cross-specialty experts from across England met to discuss the barriers and practical solutions to implementing UK Health Security Agency framework into operational and clinical workflows. The group derived responses to six key questions that are frequently asked about screening for CPE. Key findings: Four patient groups were identified for CPE screening: high-risk unplanned admissions, high-risk elective admissions, patients in high-risk units, and known positive contacts. Rapid molecular testing is a preferred screening method for some of these settings, offering faster turnaround times and more accurate results than culture-based testing. It is important to stimulate action now, as several lessons can be learnt from screening during the COVID-19 pandemic, as well as from CPE outbreaks. Conclusion: Further decisive and instructive information is needed to establish CPE screening protocols based on local epidemiology and risk factors. Local management should continually evaluate local epidemiology, analysing data and undertaking frequent prevalence studies to understand risks, and prepare resources– such as upscaled screening– to prevent increasing prevalence, clusters or outbreaks. Rapid molecular-based methods will be a crucial part of these considerations, as they can reduce unnecessary isolation and opportunity costs. [ABSTRACT FROM AUTHOR]

Objective: To compare the similarities and differences between patients with Coronavirus Disease 2019 (COVID-19) and those with other community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU), utilizing propensity score matching (PSM), regarding hospitalization expenses, treatment options, and prognostic outcomes, aiming to inform the diagnosis and treatment of COVID-19. Methods: Patients admitted to the ICU of the Third People's Hospital of Datong City, diagnosed with COVID-19 from December 2022 to February 2023, constituted the observation group, while those with other CAP admitted from January to November 2022 formed the control group. Basic information, clinical data at admission, and time from symptom onset to admission were matched using PSM. Results: A total of 70 patients were included in the COVID-19 group and 119 in the CAP group. The patients were matched by the propensity matching method, and 37 patients were included in each of the last two groups. After matching, COVID-19 had a higher failure rate than CAP, but the difference was not statistically significant (73% vs. 51%, p = 0.055). The utilization rate of antiviral drugs (40% vs. 11%, p = 0.003), γ-globulin (19% vs. 0%, p = 0.011) and prone position ventilation (PPV) (27% vs. 0%, p < 0.001) in patients with COVID-19 were higher than those in the CAP, and the differences were statistically significant. The total hospitalization cost of COVID-19 patients was lower than that of CAP patients, and the difference was statistically significant (27889.5 vs. 50175.9, p = 0.007). The hospital stay for COVID-19 patients was shorter than for CAP patients, but the difference was not statistically significant (10.9 vs. 16.6, p = 0.071). Conclusion: Our findings suggest that limited medical resources influenced patient outcomes during the COVID-19 pandemic. Addressing substantial demands for ICU capacity and medications during this period could have potentially reduced the mortality rate among COVID-19 patients. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has caused significant disruptions to everyday life and has had social, political, and financial consequences that will persist for years. Several initiatives with intensive use of technology were quickly developed in this scenario. However, technologies that enhance epidemiological surveillance in contexts with low testing capacity and healthcare resources are scarce. Therefore, this study aims to address this gap by developing a data science model that uses routinely generated healthcare encounter records to detect possible new outbreaks early in real-time. Methods: We defined an epidemiological indicator that is a proxy for suspected cases of COVID-19 using the health records of Emergency Care Unit (ECU) patients and text mining techniques. The open-field dataset comprises 2,760,862 medical records from nine ECUs, where each record has information about the patient's age, reported symptoms, and the time and date of admission. We also used a dataset where 1,026,804 cases of COVID-19 were officially confirmed. The records range from January 2020 to May 2022. Sample cross-correlation between two finite stochastic time series was used to evaluate the models. Results: For patients with age 18 years, we find time-lag () = 72 days and cross-correlation () ~ 0.82, = 25 days and ~ 0.93, and = 17 days and ~ 0.88 for the first, second, and third waves, respectively. Conclusions: In conclusion, the developed model can aid in the early detection of signs of possible new COVID-19 outbreaks, weeks before traditional surveillance systems, thereby anticipating in initiating preventive and control actions in public health with a higher likelihood of success. [ABSTRACT FROM AUTHOR]

Background: Over one-third of people living with HIV (PLH) in Ukraine are not on treatment. Index testing services, which link potentially exposed partners (named partners) of known PLH (index patients) with testing and treatment services, are being scaled in Ukraine and could potentially close this gap. Methods: This retrospective study included patient data from 14,554 adult PLH who initiated antiretroviral treatment (ART) between October 2018 and May 2021 at one of 35 facilities participating in an intervention to strengthen index testing services. Mixed effects modified Poisson models were used to assess differences between named partners and other ART initiators, and an interrupted time series (ITS) analysis was used to assess changes in ART initiation over time. Results: Compared to other ART initiators, named partners were significantly less likely to have a confirmed TB diagnosis (aRR = 0.56, 95% CI = 0.40, 0.77, p < 0.001), a CD4 count less than 200 cells/mm3 (aRR = 0.84, 95% CI = 0.73, 0.97, p = 0.017), or be categorized as WHO HIV stage 4 (aRR = 0.68, 9% CI = 0.55, 0.83, p < 0.001) at the time of ART initiation, and were significantly more likely to initiate ART within seven days of testing for HIV (aRR = 1.36, 95% CI = 1.22, 1.50, p < 0.001). Our ITS analysis showed a modest 2.34% (95% CI = 0.26%, 4.38%; p = 0.028) month-on-month reduction in mean ART initiations comparing the post-intervention period to the pre-intervention period, although these results were likely confounded by the COVID epidemic. Conclusion: Our findings suggest that index testing services may be beneficial in bringing PLH into treatment at an earlier stage of HIV disease and decreasing delays between HIV testing and ART initiation, potentially improving patient outcomes and retention in the HIV care cascade. [ABSTRACT FROM AUTHOR]

Objective: Multisystem Inflammatory Syndrome in Children (MIS-C) associated with COVID-19 is a rare and serious medical condition. This study aims to review the clinical presentation, laboratory parameters, outcomes, and management of MIS-C cases in a pediatric hospital in Syria. Methods: This retrospective observational study aimed to investigate MIS-C between May 2020 and October 2021. Data collection involved extracting information from medical records, and patients were identified based on the case definition established by the World Health Organization (WHO). Various laboratory investigations, diagnostic evaluations, clinical presentations, and treatments were performed to assess patients. Descriptive statistical analysis was conducted using Microsoft Excel. Results: A total of 232 COVID-19 cases were reported with COVID-19 Infection. Among these cases, 25 (10.77%) were identified as MIS-C. The median age of the patients was 5.5 years, with the majority being male patients (72%). Patients experienced fever (100%), bilateral conjunctivitis (88%), rash (84%), gastrointestinal symptoms (76%), and cardiac dysfunction (72%). Other notable findings included oral cavity changes (64%), edema (36%), cervical lymphadenopathy (36%), and neurological manifestations (28%). Respiratory symptoms were uncommon (16%). All patients recovered, with no recorded deaths. Conclusion: The predominant presence of positive SARS-CoV-2 IgG in the majority of patients in this study supports the post-infectious nature of MIS-C. Respiratory symptoms were less prevalent in both pediatric COVID-19 and MIS-C patients. Early supportive care is crucial in management, although additional research is needed to establish definitive guidelines. Larger studies are necessary to overcome the limitations of this study and to enhance our understanding of MIS-C in pediatric COVID-19 patients. [ABSTRACT FROM AUTHOR]

Background: Immune dysregulation in individuals with long COVID has been detected. Differential diagnosis of diffuse infiltration on chest CT in long COVID is challenging. Case presentation: A 62-year-old man presented with a 10-month history of dyspnea after COVID-19 infection. Dyspnea became worse in the one month preceding presentation. The chest CT showed multifocal, subpleural, bilateral opacities due to long-COVID, and infiltration around the bronchovascular bundle in the bilateral lower lung field. The pathology for the transbronchial cryobiopsy (TBCB) first reported chronic inflammation (mainly interstitial pneumonia). The patient had positive results on tests for the antibody, RO-52+, EJ+. The presumptive diagnosis of connective tissue disease-interstitial lung disease was made. Prednisone and cyclophosphamide were given. At follow-up one month later, the chest CT showed new diffuse ground-glass infiltration. The previous TBCB specimen was re-evaluated. Foamy macrophages were found in the alveolar air space. Periodic acid-Schiff (PAS) staining was performed. Numerous intracytoplasmic organisms were detected, with morphologic features consistent with those of Tropheryma whipplei. The patient recovered after intravenous ceftriaxone and oral trimethoprim-sulfamethoxazole. The final diagnosis was lung T. whipplei infection and long COVID-19. Conclusion: This is the first case report of Tropheryma whipplei infection in the lung of a patient with long COVID-19. T. whipplei should be considered as a potential pathogen for diffuse lung infiltration in the post-COVID-19 era. [ABSTRACT FROM AUTHOR]

Background: Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models. Methods: We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios. Results: At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12–50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics. Discussion: Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic swift implementation of research cohorts was key. While many studies focused exclusively on infected individuals, population based cohorts are essential for the follow-up of SARS-CoV-2 impact on public health. Here we present the CON-VINCE cohort, estimate the point and period prevalence of the SARS-CoV-2 infection, reflect on the spread within the Luxembourgish population, examine immune responses to SARS-CoV-2 infection and vaccination, and ascertain the impact of the pandemic on population psychological wellbeing at a nationwide level. Methods: A representative sample of the adult Luxembourgish population was enrolled. The cohort was followed-up for twelve months. SARS-CoV-2 RT-qPCR and serology were conducted at each sampling visit. The surveys included detailed epidemiological, clinical, socio-economic, and psychological data. Results: One thousand eight hundred sixty-five individuals were followed over seven visits (April 2020—June 2021) with the final weighted period prevalence of SARS-CoV-2 infection of 15%. The participants had similar risks of being infected regardless of their gender, age, employment status and education level. Vaccination increased the chances of IgG-S positivity in infected individuals. Depression, anxiety, loneliness and stress levels increased at a point of study when there were strict containment measures, returning to baseline afterwards. Conclusion: The data collected in CON-VINCE study allowed obtaining insights into the infection spread in Luxembourg, immunity build-up and the impact of the pandemic on psychological wellbeing of the population. Moreover, the study holds great translational potential, as samples stored at the biobank, together with self-reported questionnaire information, can be exploited in further research. Trial registration: Trial registration number: NCT04379297, 10 April 2020. [ABSTRACT FROM AUTHOR]

Background: France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 – the second since the start of the pandemic – to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness. Methods: Focusing on September 2020–June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions. Results: The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions. Conclusions: Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness. [ABSTRACT FROM AUTHOR]