Epidemic Wave Research Articles

Bayesian phylodynamics reveals the transmission dynamics of avian influenza A(H7N9) virus at the human–live bird market interface in China

In 2013 to 2017, avian influenza A(H7N9) virus has caused five severe epidemic waves of human infections in China. The role of live bird markets (LBMs) in the transmission dynamics of H7N9 remains unclear. Using a Bayesian phylodynamic approach, we shed light on past H7N9 transmission events at the human-LBM interface that were not directly observed using case surveillance data-based approaches. Our results reveal concurrent circulation of H7N9 lineages in Yangtze and Pearl River Delta regions, with evidence of local transmission during each wave. Our results indicate that H7N9 circulated in humans and LBMs for weeks to months before being first detected. Our findings support the seasonality of H7N9 transmission and suggest a high number of underreported infections, particularly in LBMs. We provide evidence for differences in virus transmissibility between low and highly pathogenic H7N9. We demonstrate a regional spatial structure for the spread of H7N9 among LBMs, highlighting the importance of further investigating the role of local live poultry trade in virus transmission. Our results provide estimates of avian influenza virus (AIV) transmission at the LBM level, providing a unique opportunity to better prepare surveillance plans at LBMs for response to future AIV epidemics.

Lesson from the COVID-19 pandemic lockdown: A major change of hospital-diagnosed bacteremia epidemiology

ObjectivesWhen the COVID-19 pandemic reached France early in 2020, the enforced nationwide lockdown deeply altered lifestyle as well as hospital processes and modalities of care. The aim of the study was to evaluate the impact during the lockdown of the first epidemic wave on the epidemiology of bacteremia in one French University Hospital. Patients and MethodsRetrospective cohort study including adult patients with positive blood culture between 23rd March to 24th May 2020. The clinical-microbiological characteristics were compared with those of the period from 25th March to 26th May 2019. The data were adjusted to the number of hospitalizations (h). ResultsIn 2020, 189 bacteremia were diagnosed from 1939 vials (9658 hospitalizations, 10911 emergency room consultations) compared to 143 from 1976 vials (14797 hospitalizations, 16493 emergency room consultations) recorded in 2019. The incidence of bacteremia increased up to 19.7 per 1000h in 2020 vs 9.7 in 2019 (p < 0.001). The main differences (2020 vs 2019) were: Staphylococcus aureus bacteremia (2.4 vs 1.0/1000h, p = 0.012), polymicrobial bacteremia (2.2 vs 0.9/1000h p = 0.013) and Gram-negative bacteremia (8.9 vs 4.3/1000h, p < 0.01). Conversely, Streptococcus pneumoniae incidence decreased (0 vs 0.47/1000h, p = 0.047). The standardized incidence ratio calculation confirmed these results. ConclusionThe lockdown and the impact of the first wave of the Covid-19 pandemic on the health system resulted in increased hospital-diagnosed bacteremia and decreased pneumococcal bacteremia. Disruption and overload of ICUs, lockdown with preventive control measures, and decrease in human-to-human interaction may have been the main reasons.

Use of Remdesivir in Patients with SARS-CoV-2 Pneumonia in a Real-Life Setting during the Second and Third COVID-19 Epidemic Waves.

In this retrospective comparative study, we evaluated the effectiveness of remdesivir (RDSV) in patients with SARS-CoV-2 pneumonia. Individuals hospitalized between March 2020 and August 2022 at S.M. Goretti Hospital, Latina, with a positive test for SARS-CoV-2 and, concomitantly, pneumonia, were included. The overall survival was the primary endpoint. The composite secondary endpoint included death or progression in severe ARDS at 40 days. The study population was stratified according to treatment into two groups: the RDSV group (patients treated with RDSV-based regimens) and the no-RDSV group (patients treated with any other, not RDSV-based, regimens). Factors associated with death and progression to severe ARDS or death were assessed by multivariable analysis. A total of 1153 patients (632 belonging to the RDSV group and 521 to the no-RDSV group) were studied. The groups were comparable in terms of sex, PaO2/FiO2 at admission, and duration of symptoms before hospitalization. Further, 54 patients (8.5%) in the RDSV group and 113 (21.7%) in the no-RDSV group (p < 0.001) died. RDSV was associated with a significantly reduced hazard ratio (HR) of death (HR, 0.69 [95% CI, 0.49-0.97]; p = 0.03), compared to the no-RDSV group, as well as a significantly reduced OR of progression in severe ARDS or death (OR, 0.70 [95% CI 0.49-0.98]; p = 0.04). An overall significantly higher survival rate was observed in the RDSV group (p < 0.001, by log-rank test). These findings reinforce the survival benefit of RDSV and support its routine clinical use for the treatment of COVID-19 patients.

Unveiling the paths of COVID-19 in a large city based on public transportation data

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased approx 25% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant lambda approx 33 days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

Pandemic response strategies and threshold phenomena

This paper critically evaluates the Suppression Threshold Strategy (STS) for controlling Covid-19 (C-19). STS asserts a “fundamental distinction” between suppression and mitigation strategies, reflected in very different outcomes in eventual mortality depending on whether reproductive number R is caused to fall below 1. We show that there is no real distinction based on any value of R which falls in any case from early on in an epidemic wave. We show that actual mortality outcomes lay on a continuum, correlating with suppression levels, but not exhibiting any step changes or threshold effects. We argue that an excessive focus on achieving suppression at all costs, driven by the erroneous notion that suppression is a threshold, led to a lack of information on how to trade off the effects of different specific interventions. This led many countries to continue with inappropriate intervention-packages even after it became clear that their initial goal was not going to be attained. Future pandemic planning must support the design of “Plan B", which may be quite different from “Plan A".

College Teacher Identity Crisis and Its Improving Path in Online Courses During the Epidemic

在疫情防控常态化形势下，传统课堂向线上教学模式的转变增加了老师授课的难度。根据文献检索，我们发现尽管国内外对于教师身份认同的研究并不少，但与疫情浪潮下的线上教学相关的文献数目屈指可数。鉴于此，本研究依据教师身份认同和社会文化等理论，通过对103位高校教师进行问卷调查与个别教师的半结构式访谈，结合多样化多视角参考资料分析疫情冲击下网课中的教师身份认同现状及其遭受危机的原因。结果表明，由于线上授课效果有时不尽如人意，在线课堂授课方式以及互动方式的改变加大了教师授课的难度，阻碍了教师对课堂教学质量的评估，使得教师身份认同面临危机。同时，本研究通过综合分析问卷结果以及访谈所得，参考对照大量前沿的国内外的相关核心文献资料，从多维度探寻提升教师身份认同的路径，为其提出可行性建议。 The transition from the traditional classroom to online learning has made it more difficult for teachers to teach in the context of ongoing epidemic prevention and control. According to our literature search, while there are many studies on teacher identity both at home and abroad, the number of studies on online teaching under the epidemic wave is small. Based on teacher identity theory and sociocultural theory, this study conducted a questionnaire survey among 103 college teachers, which were combined with diverse and multi-perspective reference materials to analyze the status quo of teacher identity in online courses affected by the epidemic and the causes of the impact. The findings indicate that online teaching is not always satisfactory and that changes in online teaching methods and interactive methods increase the difficulty of teachers' teaching, making them constantly locate themselves and be located in social participation and interaction, putting teachers' identities in jeopardy. Simultaneously, using a comprehensive analysis of the questionnaire and a large number of cutting-edge domestic and foreign core literature, this study investigates ways to improve teachers' identities from multiple perspectives and proposes feasible solutions

Spatiotemporal dynamics and recurrence of chikungunya virus in Brazil: an epidemiological study

Chikungunya virus (CHIKV) is an Aedes mosquito-borne virus that has caused large epidemics linked to acute, chronic, and severe clinical outcomes. Currently, Brazil has the highest number of chikungunya cases in the Americas. We aimed to investigate the spatiotemporal dynamics and recurrence pattern of chikungunya in Brazil since its introduction in 2013. In this epidemiological study, we used CHIKV genomic sequencing data, CHIKV vector information, and aggregate clinical data on chikungunya cases from Brazil. The genomic data comprised 241 Brazilian CHIKV genome sequences from GenBank (n=180) and the 2022 CHIKV outbreak in Ceará state (n=61). The vector data (Breteau index and House index) were obtained from the Brazilian Ministry of Health for all 184 municipalities in Ceará state and 116 municipalities in Tocantins state in 2022. Epidemiological data on laboratory-confirmed cases of chikungunya between 2013 and 2022 were obtained from the Brazilian Ministry of Health and Laboratory of Public Health of Ceará. We assessed the spatiotemporal dynamics of chikungunya in Brazil via time series, mapping, age-sex distribution, cumulative case-fatality, linear correlation, logistic regression, and phylogenetic analyses. Between March 3, 2013, and June 4, 2022, 253 545 laboratory-confirmed chikungunya cases were reported in 3316 (59·5%) of 5570 municipalities, mainly distributed in seven epidemic waves from 2016 to 2022. To date, Ceará in the northeast has been the most affected state, with 77 418 cases during the two largest epidemic waves in 2016 and 2017 and the third wave in 2022. From 2016 to 2022 in Ceará, the odds of being CHIKV-positive were higher in females than in males (odds ratio 0·87, 95% CI 0·85-0·89, p<0·0001), and the cumulative case-fatality ratio was 1·3 deaths per 1000 cases. Chikungunya recurrences in the states of Ceará, Tocantins (recurrence in 2022), and Pernambuco (recurrence in 2021) were limited to municipalities with few or no previously reported cases in the previous epidemic waves. The recurrence of chikungunya in Ceará in 2022 was associated with a new East-Central-South-African lineage. Population density metrics of the main CHIKV vector in Brazil, Aedes aegypti, were not correlated spatially with locations of chikungunya recurrence in Ceará and Tocantins. Spatial heterogeneity of CHIKV spread and population immunity might explain the recurrence pattern of chikungunya in Brazil. These results can be used to inform public health interventions to prevent future chikungunya epidemic waves in urban settings. Global Virus Network, Burroughs Wellcome Fund, Wellcome Trust, US National Institutes of Health, São Paulo Research Foundation, Brazil Ministry of Education, UK Medical Research Council, Brazilian National Council for Scientific and Technological Development, and UK Royal Society. For the Portuguese translation of the abstract see Supplementary Materials section.

Characteristic profiles of patients with diabetes mellitus and COVID-19 during the second epidemic wave in East Java, Indonesia: a retrospective study

Background: The transmissibility and severity of coronavirus disease 2019 (COVID-19) are higher in the delta variant (B.1.617.2) infection responsible for Indonesia's second epidemic wave. This study aimed to establish an epidemiological profile of COVID-19 in patients with diabetes mellitus, a comorbid that has been widely suggested for its association with increased severity and mortality. Methods: This study employed a retrospective observation on medical records from May to December 2021. Patients (n=115) aged &gt;18 presented with COVID-19 and diabetes mellitus were enrolled. The SARS-CoV-2 infection was confirmed by a pharyngeal swab sample's reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The diabetic condition was determined by previous medical records and persistent level of random blood sugar (RBS) of &gt;200 mg/dL. Demographic, clinical characteristics, COVID-19 symptoms, radiological findings, and hematological and coagulopathy markers were collected from the medical records. The data were presented as frequency (%) assigned based on severity levels: mild, moderate, severe, and critical COVID-19. Results: A group of patients aged 50—59 years had higher prevalence of severe COVID-19 (n=14; 28.6%) than the other groups. Severe cases were relatively more common in a group with body mass index (BMI) ranged from 30—&lt;35 kg/m2 (n=13; 27.1%). Notably higher number of severe cases was recorded in patients having &lt;3.5 mg/dL albumin (n=11 out of 50), &gt;200 mg/dL RBS (n=22 out of 93), and ≥1 μg/mL D-dimer (n=18 out of 73). Critical COVID-19 cases were found in those with C-reactive protein levels of &lt;5 mg/L (n=6; 6.8%) and procalcitonin levels of ≥0.05 ng/mL (n=6; 7.1%). Patients who had stage 1 and 3 chronic kidney disease (CKD) were found to develop critical COVID-19 with a frequency of 5.9% (n=2) and 10.5% (n=4), respectively. Conclusion: The severity of COVID-19 in diabetic patients is indicatively associated with the age, BMI, hematological and coagulopathy markers, and CKD. Active monitoring of these parameters in diabetic COVID-19 patients is encouraged.

Estimating infection fatality risk and ascertainment bias of COVID-19 in Osaka, Japan from February 2020 to January 2022

The present study aimed to estimate the infection fatality risk (IFR) and ascertainment bias of SARS-CoV-2 for six epidemic waves in Japan from February 2020 to January 2022. We used two types of datasets: (i) surveillance-based datasets containing the cumulative numbers of confirmed cases and deaths in each epidemic wave and (ii) seroepidemiological datasets conducted in a serial cross-sectional manner. Smoothing spline function was employed to reconstruct the age-specific cumulative incidence of infection. We found that IFR was highest during the first wave, and the second highest during the fourth wave, caused by the Alpha variant. Once vaccination became widespread, IFR decreased considerably among adults aged 40 years plus during the fifth wave caused by the Delta variant, although the epidemic size of fifth wave was the largest before the Omicron variant emerged. We also found that ascertainment bias was relatively high during the first and second waves and, notably, RT-PCR testing capacity during these early periods was limited. Improvements in the ascertainment were seen during the third and fourth waves. Once the Omicron variant began spreading, IFR diminished while ascertainment bias was considerably elevated.

SARS-CoV-2 seroprevalence and living conditions in Bamako (Mali): a cross-sectional multistage household survey after the first epidemic wave, 2020

ObjectivesIn low-income settings with limited access to diagnosis, COVID-19 information is scarce. In September 2020, after the first COVID-19 wave, Mali reported 3086 confirmed cases and 130 deaths. Most reports...

Acceptance and willingness to pay for COVID-19 vaccines available in Vietnam: an online study during the fourth epidemic wave

COVID-19 vaccines available in Vietnam have different prices, efficacies, and side effects. We studied acceptance and willingness to pay (WTP) for COVID-19 vaccines in Vietnam, using a self-designed online questionnaire. Respondents were 2093 unvaccinated adults. Multiple regression analyses identified factors associated with vaccine acceptance and WTP. Acceptance of free vaccines was around 90% for the three available in Vietnam (Astra Zeneca, SPUTNIK V, and Pfizer-BioNTech). WTP for the same vaccines was about 70%. Vaccine acceptance was associated with being female and/or chronically ill or undergoing COVID-19-related job changes. WTP was associated variously with family economic status, occupational changes due to COVID-19, chronic disease, and perceived risk of infection. Most respondents were willing to be vaccinated and many were willing to pay for it, depending on personal and family circumstances. Vietnam should budget for free vaccines to support those unable to pay.

Clinical characteristics and factors associated with COVID-19-related mortality and hospital admission during the first two epidemic waves in 5 rural provinces in Indonesia: A retrospective cohort study.

Data on coronavirus disease 2019 (COVID-19) clinical characteristics and severity from resource-limited settings are limited. This study examined clinical characteristics and factors associated with COVID-19 mortality and hospitalisation in rural settings of Indonesia, from 1 January to 31 July, 2021. This retrospective cohort included individuals diagnosed with COVID-19 based on polymerase chain reaction or rapid antigen diagnostic test, from five rural provinces in Indonesia. We extracted demographic and clinical data, including hospitalisation and mortality from a new piloted COVID-19 information system named Sistem Informasi Surveilans Epidemiologi (SISUGI). We used mixed-effect logistic regression to examine factors associated with COVID-19-related mortality and hospitalisation. Of 6,583 confirmed cases, 205 (3.1%) died and 1,727 (26.2%) were hospitalised. The median age was 37 years (Interquartile range 26-51), with 825 (12.6%) under 20 years, and 3,371 (51.2%) females. Most cases were symptomatic (4,533; 68.9%); 319 (4.9%) had a clinical diagnosis of pneumonia and 945 (14.3%) presented with at least one pre-existing comorbidity. Age-specific mortality rates were 0.9% (2/215) for 0-4 years; 0% (0/112) for 5-9 years; 0% (1/498) for 10-19 years; 0.8% (11/1,385) for 20-29 years; 0.9% (12/1,382) for 30-39 years; 2.1% (23/1,095) for 40-49 years; 5.4% (57/1,064) for 50-59 years; 10.8% (62/576) for 60-69 years; 15.9% (37/232) for ≥70 years. Older age, pre-existing diabetes, chronic kidney disease, liver diseases, malignancy, and pneumonia were associated with higher risk of mortality and hospitalisation. Pre-existing hypertension, cardiac diseases, COPD, and immunocompromised condition were associated with risk of hospitalisation but not with mortality. There was no association between province-level density of healthcare workers with mortality and hospitalisation. The risk of COVID-19-related mortality and hospitalisation was associated with higher age, pre-existing chronic comorbidities, and clinical pneumonia. The findings highlight the need for prioritising enhanced context-specific public health action to reduce mortality and hospitalisation risk among older and comorbid rural populations.

A simple algorithm based on initial Ct values predicts the duration to SARS-CoV-2 negativity and allows more efficient test-to-release and return-to-work schedules

Especially during global pandemics but also in the context of epidemic waves, the capacity for diagnostic quantitative reverse transcription-polymerase chain reactions (qRT-PCRs) rapidly becomes a limiting factor. The aim of the study was to optimize retesting regimens for test-to-release from isolation and return-to-work applications. For this purpose, we investigated the association between Ct values at the first diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the period until test negativity was reached, or at least until the Ct value exceeded 30, which is considered to indicate the transition to a non-infectious state. We included results from the testing of respiratory material samples for the detection of SARS-CoV-2 RNA, tested from March 1, 2020 to January 31, 2022. Lower initial Ct values were associated with longer periods of SARS-CoV-2 RNA positivity. Starting with Ct values of <20, 20 to 24.99, 25 to 29.99, 30 to 34.99, and ≥35, it took median intervals of 20 (interval: 14–25), 16 (interval: 10–21), 12 (interval: 7–16), 7 (interval: 5–14), and 5 (interval: 2–7) days, respectively, until the person tested negative. Accordingly, a Ct threshold of 30 was surpassed after 13 (interval: 8–19), 9 (interval: 6–14), 7 (interval: 6–11), 6 (interval: 4–10), and 3 (interval: 1–6) days, respectively, in individuals with aforementioned start Ct values. Furthermore, the time to negativity was longer for adults versus children, wild-type SARS-CoV-2 variant versus other variants of concern, and in patients who were treated in the intensive care units. Based on these data, we propose an adjusted retesting strategy according to the initial Ct value in order to optimize available PCR resources.

Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19

It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic to provide optimal clinical care and resource management. Many methods have been proposed to provide a roadmap against different pandemics, including the recent pandemic disease COVID-19. Due to recurrent epidemic waves of COVID-19, which have been observed in many countries, mathematical modeling and forecasting of COVID-19 are still necessary as long as the world continues to battle against the pandemic. Modeling may aid in determining which interventions to try or predict future growth patterns. In this article, we design a combined approach for analyzing any pandemic in two separate parts. In the first part of the paper, we develop a recurrent SEIRS compartmental model to predict recurrent outbreak patterns of diseases. Due to its time-varying parameters, our model is able to reflect the dynamics of infectious diseases, and to measure the effectiveness of the restrictive measures. We discuss the stable solutions of the corresponding autonomous system with frozen parameters. We focus on the regime shifts and tipping points; then we investigate tipping phenomena due to parameter drifts in our time-varying parameters model that exhibits a bifurcation in the frozen-in case. Furthermore, we propose an optimal numerical design for estimating the system’s parameters. In the second part, we introduce machine learning models to strengthen the methodology of our paper in data analysis, particularly for prediction scenarios. We use MLP, RBF, LSTM, ANFIS, and GRNN for training and evaluation of COVID-19. Then, we compare the results with the recurrent dynamical system in the fitting process and prediction scenario. We also confirm results by implementing our methods on the released data on COVID-19 by WHO for Italy, Germany, Iran, and South Africa between 1/22/2020 and 7/24/2021, when people were engaged with different variants including Alpha, Beta, Gamma, and Delta. The results of this article show that the dynamic model is adequate for long-term analysis and data fitting, as well as obtaining parameters affecting the epidemic. However, it is ineffective in providing a long-term forecast. In contrast machine learning methods effectively provide disease prediction, although they do not provide analysis such as dynamic models. Finally, some metrics, including RMSE, R-Squared, and accuracy, are used to evaluate the machine learning models. These metrics confirm that ANFIS and RBF perform better than other methods in training and testing zones.

Analysing the COVID-19 epidemic in Italy through the Reed-Frost model: A methodology to delimit epidemic waves over time.

to set out a method based on the Reed Frost model to delimit over time COVID-19 epidemic waves in Italy. the available national epidemic reports published by the Protezione Civile (Italian civil defence) from 24.02.2020 to 16.022022 were used to collect data on COVID-19 epidemic in Italy. Then, the Reed-Frost model was applied to develop a methodology based on the calculation of the effective contact probability, i.e., the probability of contact. in Italy, a daily report related to the epidemic was immediately available, including main epidemiological data (point and periodic infection prevalence, mortality, etc), which made it possible for researchers from different institutions to perform analyses about the epidemic. an iterative methodology was developed resulting in the identification of the start-of-wave, end-of-wave, and inter-wave periods and of the starting and ending days of the COVID-19 epidemic waves in Italy (first wave: from 26±2 February 2020 to 28±2 June 2020). this study led to the development of an accessible and reproducible method to determine the start-of-wave and end-of-wave dates of an epidemic, starting only from the number of cases and susceptible people. The main implications of the method mainly consist in allowing benchmarking and forecasting analyses of the epidemic trend to be carried out to support policy and decision-making processes.

Deaths from undetected COVID-19 infections as a fraction of COVID-19 deaths can be used for early detection of an upcoming epidemic wave.

With countries across the world facing repeated epidemic waves, it becomes critical to monitor, mitigate and prevent subsequent waves. Common indicators like active case numbers may not be sensitive enough in the presence of systemic inefficiencies like insufficient testing or contact tracing. Test positivity rates are sensitive to testing strategies and cannot estimate the extent of undetected cases. Reproductive numbers estimated from logarithms of new incidences are inaccurate in dynamic scenarios and not sensitive enough to capture changes in efficiencies. Systemic fatigue results in lower testing, inefficient tracing and quarantining thereby precipitating the onset of the epidemic wave. We propose a novel indicator for detecting the slippage of test-trace efficiency based on the number of deaths/hospitalizations resulting from known and hitherto unknown infections. This can also be used to forecast an epidemic wave that is advanced or exacerbated due to a drop in efficiency in situations where the testing has come down drastically and contact tracing is virtually nil as is prevalent currently. Using a modified SEIRD epidemic simulator we show that (i) Ratio of deaths/hospitalizations from an undetected infection to total deaths converges to a measure of systemic test-trace inefficiency. (ii) This index forecasts the slippage in efficiency earlier than other known metrics. (iii) Mitigation triggered by this index helps reduce peak active caseload and eventual deaths. Deaths/hospitalizations accurately track the systemic inefficiencies and detect latent cases. Based on these results we make a strong case that administrations use this metric in the ensemble of indicators. Further, hospitals may need to be mandated to distinctly register deaths/hospitalizations due to previously undetected infections. Thus the proposed metric is an ideal indicator of an epidemic wave that poses the least socio-economic cost while keeping the surveillance robust during periods of pandemic fatigue.

Impact of national-scale targeted point-of-care symptomatic lateral flow testing on trends in COVID-19 infections, hospitalisations and deaths during the second epidemic wave in Austria (REAP3)

BackgroundIn October 2020, amidst the second COVID-19 epidemic wave and before the second-national lockdown, Austria introduced a policy of population-wide point-of-care lateral flow antigen testing (POC-LFT). This study explores the impact of this policy by quantifying the association between trends in POC-LFT-activity with trends in PCR-positivity (as a proxy for symptomatic infection), hospitalisations and deaths related to COVID-19 between October 22 and December 06, 2020.MethodsWe stratified 94 Austrian districts according to POC-LFT-activity (number of POC-LFTs performed per 100,000 inhabitants over the study period), into three population cohorts: (i) high(N = 24), (ii) medium(N = 45) and (iii) low(N = 25). Across the cohorts we a) compared trends in POC-LFT-activity with PCR-positivity, hospital admissions and deaths related to COVD-19; b) compared the epidemic growth rate before and after the epidemic peak; and c) calculated the Pearson correlation coefficients between PCR-positivity with COVID-19 hospitalisations and with COVID -19 related deaths.ResultsThe trend in POC-LFT activity was similar to PCR-positivity and hospitalisations trends across high, medium and low POC-LFT activity cohorts, with association with deaths only present in cohorts with high POC-LFT activity. Compared to the low POC-LFT-activity cohort, the high-activity cohort had steeper pre-peak daily increase in PCR-positivity (2.24 more cases per day, per district and per 100,000 inhabitants; 95% CI: 2.0–2.7; p < 0.001) and hospitalisations (0.10; 95% CI: 0.02, 0.18; p = 0.014), and 6 days earlier peak of PCR-positivity. The high-activity cohort also had steeper daily reduction in the post-peak trend in PCR-positivity (-3.6; 95% CI: -4.8, -2.3; p < 0.001) and hospitalisations (-0.2; 95% CI: -0.32, -0.08; p = 0.001). PCR-positivity was positively correlated to both hospitalisations and deaths, but with lags of 6 and 14 days respectively.ConclusionsHigh POC-LFT-use was associated with increased and earlier case finding during the second Austrian COVID-19 epidemic wave, and early and significant reduction in cases and hospitalisations during the second national lockdown. A national policy promoting symptomatic POC-LFT in primary care, can capture trends in PCR-positivity and hospitalisations. Symptomatic POC-LFT delivered at scale and combined with immediate self-quarantining and contact tracing can thus be a proxy for epidemic status, and hence a useful tool that can replace large-scale PCR testing.

Heterogeneity in COVID-19 infection among older persons in South Africa: Evidence from national surveillance data.

The 2021 World Health Organization study on the impact of COVID-19 on older people (≥60 years) in the African region highlighted the difficulties they faced as the virus spread across borders and dominated the way of life. These difficulties included disruptions to both essential health care services and social support, as well as disconnections from family and friends. Among those who contracted COVID-19, the risks of severe illness, complications, and mortality were highest among near-old and older persons. Recognizing that older persons are a diverse group including younger- and older-aged individuals, a study was conducted to track the epidemic among near-old (50-59 years) and older persons (≥60 years) in South Africa covering the 2 years since the epidemic emerged. Using a quantitative secondary research approach, data for near-old and older persons were extracted for comparative purposes. COVID-19 surveillance outcomes (confirmed cases, hospitalizations, and deaths) and vaccination data were compiled up to March 5th, 2022. COVID-19 surveillance outcomes were plotted by epidemiological week and epidemic waves to visualize the overall growth and trajectory of the epidemic. Means for each age-group and by COVID-19 waves, together with age-specific rates, were calculated. Average numbers of new COVID-19 confirmed cases and hospitalizations were highest among people aged 50-59- and 60-69-years. However, average age-specific infection rates showed that people aged 50-59 years and ≥80 years were most vulnerable to contracting COVID-19. Age-specific hospitalization and death rates increased, with people aged ≥ 70 years most affected. The number of people vaccinated was slightly higher among people aged 50-59 years before Wave Three and during Wave Four, but higher among people aged ≥ 60 years during Wave Three. The findings suggest that uptake of vaccinations stagnated prior to and during Wave Four for both age groups. Health promotion messages and COVID-19 epidemiological surveillance and monitoring are still needed, particularly for older persons living in congregate residential and care facilities. Prompt health-seeking should be encouraged, including testing and diagnosis as well as taking up vaccines and boosters, particularly for high-risk older persons.

The impact of occupational exposures on infection rates during the COVID-19 pandemic: a test-negative design study with register data of 207 034 Dutch workers.

This study aimed to investigate the effects of occupational exposures on the risk of a positive COVID-19 test, and whether this differed across pandemic waves. Data from 207 034 workers from The Netherlands with test data on COVID-19 from June 2020 until August 2021 were available. Occupational exposure was estimated by using the eight dimensions of a COVID-19 job exposure matrix (JEM). Personal characteristics, household composition and residence area were derived from Statistics Netherlands. A test-negative design was applied in which the risk of a positive test was analyzed in a conditional logit model. All eight dimensions of occupational exposure included in the JEM increased the odds of a positive test for the entire study period and three pandemic waves [OR ranging from 1.09, (95% confidence interval (CI) 1.02-1.17) to 1.77 (95% CI 1.61-1.96)]. Adjusting for a previous positive test and other covariates strongly reduced the odds to be infected, but most dimensions remained at elevated risk. Fully adjusted models showed that contaminated work spaces and face covering were mostly relevant in the first two pandemic waves, whereas income insecurity showed higher odds in the third wave. Several occupations have a higher predicted value for a positive COVID-19 test, with variation over time. Discussion Occupational exposures are associated with a higher risk of a positive test, but variations over time exist in occupations with the highest risks. These findings provide insights for interventions among workers for future pandemic waves of COVID-19 or other respiratory epidemics.

Poor adherence to antiretroviral therapy among adult people living with HIV initiated during the COVID-19 epidemic waves - observations at the University Teaching Hospital in Lusaka, Zambia.

Coronavirus disease 2019 (COVID-19)-related disruptions in healthcare services and clinical outcomes have been predicted and documented. However, little is known about how antiretroviral therapy (ART) adherence disruptions caused by the COVID-19 pandemic have manifested amidst the 'Undetectable = Untransmittable' campaign initiative. Using a patient's viral load as a proxy for medication adherence, our study aimed to determine the adherence to ART on first-line medications among adult people living with human immunodeficiency virus (PLWHIV) at the University Teaching Hospital in Lusaka, Zambia during the pandemic. This was a hospital-based cross-sectional study. Secondary data of PLWHIV registered to receive ART from the Adult Infectious Disease Centre was extracted from the SmartCare® electronic health record system to constitute a resultant data set that this study used. The data extraction form was used to extract values of dependent (ART adherence measured by viral load detectability) and independent variables and imported them into the statistical analysis tool, STATA version 16.1 MP. Descriptive statistics of individual characteristics, testing for associations using Pearson's chi-square test, and stratified and combined multivariable logistic regression were performed. Of the 7,281 adult PLWHIV included in this study, 9.0% (95% CI 8.3-9.6%) were virally detectable. Estimates of the odds ratios of detectable viral load remained significantly higher among adult PLWHIV who were initiated on ART after the U=U campaign was launched in Zambia and were on a monthly 2.51 (1.31-9.03) or 6-monthly 4.75 (3.52-6.41) dispensing of a dolutegravir-based regimen and those on 6-monthly dispensing of an efavirenz-based regimen 4.67 (2.16-10.08) compared to their counterparts. Overall estimates showed us the same picture 4.14 (3.22-5.31), having adjusted for all other predictor variables. We found that a high proportion of people with detectable viral load in the study population, irrespective of medication refill interval and type of regimen, was concentrated among adult PLWHIV who started treatment during the COVID-19 epidemic waves, as compared to those who started treatment before the pandemic. This observed disparity suggests the inherent impact of the pandemic on the adherence to ART among adult PLWHIV in Lusaka, Zambia. This further illustrates how exposed program responses are to external shocks, especially in already weakened health systems, and the need to create program response buffers and resilient program-specific strategies to minimize the effect of external disruptions.