Excess of Mortality in Older People during the Covid-19 Pandemic in Mozambique

Background: The impact of COVID-19 pandemic was apparently less severe in African continents, probably underestimated due to the limited testing capacities and access to health facilities, particularly in rural areas. Hospital and community surveillance of COVID-19 was established in Manhica District, rural Mozambique to understand the epidemic curve and natural history of SARSCoV- 2 including age-specific incidence of severe COVID-19 and reproduction number and effects of interventions through mathematical modelling Methods: Suspected cases visiting the Manhica District Hospital were screened for SARS-CoV-2 by qRT-PCR. Four age-stratified (0-19, 20-39, 40-59 and >=60 years, n=300 each) community-based serosurveys were conducted (Apr 2021-Feb 2022) to estimate the prevalence of antibodies (Abs) against SARS-CoV-2. We fitted a statistical model within a Bayesian framework, to estimate the extent to which older people were over-represented in mortality data throughout the pandemic. This involved training the model on data from the pre-pandemic period and then using this model to generate estimates of the expected levels of mortality in the absence of COVID-19 in adults aged 40+ using data from our reference category (15-39 year olds). Result(s): Between Dec 2020 and Aug 2022, 31.2% of 1332 swabs tested positive for SARS-CoV-2, with high proportion among people aged 50-59 years (62.1%, 36/58). Abs against SARS-CoV-2 were detected in 28% (180/666) of subjects enrolled in survey one, which increased two and tri-fold, in surveys 2 (64%, 595/936) and 3 (91%, 700/768);remaining stable (91.3%, 1023/1121) in 4. Age-specific analysis showed consistency on Abs detection over the surveys, including people non-eligible for vaccination (0-17 years) where >80% (165/188) had Abs detected. 93% (359/384) of subjected with Abs in survey 3, remained positive 3 months later. Shifting age-patterns throughout the pandemic are consistent with a high impact of the disease particularly in older ages. Depending on assumptions made in our modelling, we estimate a cumulative excess mortality rates in adults aged 80+ of between 8 and 17% with the largest peak coinciding with the peak in the delta variant wave. Conclusion(s): Our data reveal that people in rural areas were widely exposed across including unvaccinated ones;and there was a signature COVID-19-like shift in mortality patterns towards older ages, suggesting substantial impact, of the pandemic that is largely not reflected in patterns of confirmed COVID-19 deaths. Quantitative estimates of shift in age-patterns throughout the pandemic. (A) Shows the fit of the model to age-patterns of mortality in the pre-pandemic period 2018-2020. This model is then used to generate the expected numbers of deaths in individuals aged 40+ throughout the pandemic (2020-2022). (B) excess deaths in the pandemic relative to the model, shown in (A), black lines and grey shaded regions show estimates assuming that declines in reported mortality in under 40s are due to declines in mortality (assumption 1), coloured show equivalent estimates assuming that declines in mortality in under 40s are due to declines in ascertainment (assumption 2). (C) Shows estimates from (A) as mortality per 1000 individuals within the age strata, (D) shows each excess mortality estimate as a proportion of the population within the age strata, with seroprevalence estimated from the first two cross-sectional surveys highlighted for reference.

Epidemiology reveals mask wearing by the public is crucial for COVID-19 control

Objective: The pandemic 2019 Coronavirus disease (COVID-19) is the greatest concern globally. Here we analyzed the epidemiological features of China, South Korea, Italy and Spain to find out the relationship of major public health events and epidemiological curves. Study design: In this study we described and analyzed the epidemiological characteristics of COVID-19 in and outside China. We used GAM to generate the epidemiological curves and simulated infection curves with reported incubation period. Result(s): The epidemiological curves derived from the GAM suggested that the infection curve can reflect the public health measurements sensitively. Under the massive actions token in China, the infection curve flattened at 23rd of January. While surprisingly, even before Wuhan lockdown and first level response of public emergency in Guangdong and Shanghai, those infection curve came to the reflection point both at 21st of January, which indicated the mask wearing by the public before 21st Jan were the key measure to cut off the transmission. In the countries outside China, infection curves also changed in response to measures, but its rate of decline was much smaller than the curve of China's. Conclusion(s): The present analysis comparing the epidemiological curves in China, South Korea, Italy and Spain supports the importance of mask wearing by the public. Analysis of the infection curve helped to clarify the impact of important public health events, evaluate the efficiencies of prevention measures, and showed wearing masks in public resulted in significantly reduced daily infected cases.Copyright © 2020 The Author(s)

[The wave train of COVID-19 infections]. / COVID-19: il treno di onde dei contagi.

The present work studies the epidemic curve of COVID-19 in Italy between September 2020 and mid-June 2021 in terms of poussées, that is successive waves. There is obviously only one pandemic, although the virus has spread in the form of several variants, but the daily incidence trend can also be read in terms of overlapping of events that are different from each other or, in any case, induced by various phenomena. It can be hypothesized that in this way a succession of various waves was generated, which are modelled here using appropriate adaptation curves used in the study of epidemic data. Each curve corresponds approximately to the situation that would have occurred if no element had intervened to prevent the decrease of infections after the relative peak, while their overlap is considered to describe the subsequent increases. This interpolation has no predictive purpose, being purely descriptive over the time window under consideration. The discrepancies between the superposition of the modelling curves and the real epidemic curve are therefore also highlighted, especially in the transition periods between the various poussées. Finally, the analysis carried out allows to match the trend of the epidemic in the period considered with, on one hand, the series of events and, on the other, with the containment measures adopted which may have determined the succession of increases and decreases in the incidence of infections.

Analysis and Forecasting of the COVID-19 Epidemic Curve

Corona Virus Disease 2019 (COVID-19) has emerged as a supreme challenge for the whole world as well as India. As of now approximately 6.5 million people died in the world. However, the major setback to the world was in 2021 as a result of the second and third waves of COVID-19, which were caused by a different variation of COVID-19 than the first variant. The governments and health sectors were not aware of the subsequent possible waves due to the lack of data analysis competency and improper forecasting models. Hence finding an inflection point of this epidemic curve for COVID-19 infection and death is very imperative to understand different waves and variants instigating these waves. Similarly predicting the epidemic curve for the future is vital to make the government and the systems aware of the impending situation and make them prepare accordingly. Hence this work attempts to demonstrate conditions for finding inflection points and intervals which helps in finding the number of waves and the variants of COVID-19. Simultaneously the forecasting of the number of infections in forthcoming wave is also done using the auto-regressive integrated moving average model to identify the number of waves in India. The prediction of the two months data was compared with actual data for proper analysis. © 2022 IEEE.

The Decline of Respiratory Viruses During the SARS-CoV-2 Pandemic: Public Health Interventions vs. Viral Competition?

Background. With the spread of the SARS-CoV-2 pandemic in 2020 and the attendant global precautions such as masking, travel restrictions and social distancing, the WHO FluNet data indicated a decline in flu rates. The CDC data for the 2020-2021 season showed the same decline in US flu as well as other respiratory viruses. Two hypotheses to explain the observed phenomenon are the impact of non pharmaceutical interventions (NPI) to prevent SARS-CoV-2 infection and suppression of other respiratory viruses by SARS-CoV-2 through a form of resource competition. Methods. We conducted a study using the EPIC Slicer Dicer analytics tool and the Yale Internal Medicine COVID-19 Database to retrieve data from the Yale New Haven Health System (YNHHS). We tabulated the total number of positive and negative tests for SARS-CoV-2 and a panel of respiratory viruses from September 2, 2018 to April 30, 2022 to cover pre- and peri-pandemic periods. These results were divided into three age groups: <=12, 13-59, and >=60. Epidemic curves of each virus with respect to each other, the season, and the introduction of NPIs were constructed to help differentiate between the two hypotheses. Results. Pre-pandemic data from 09/2018 to 02/2020 revealed seasonal spikes in influenza A and B with 254 positive weekly influenza A/B tests from 11/2018 to 02/2019 for a positivity rate of 7.97% and 481 positive weekly tests (10.53% positivity rate [PR]) from November 2019 to February 2020. There were only 0.35 positive weekly influenza A/B tests (0.05% PR) from 11/2020 to 02/2021 with 2018 positive weekly tests (6.45% PR) for SARS-CoV-2 over the same period. From 11/2020 to 02/2021, there were 56 positive weekly influenza A/B tests (1.44% PR) and 4347 positive weekly SARS-CoV-2 tests (10.35% PR). From 07/ 2021 to 11/2021, there was an increased rate of positive RSV tests (82 per week, 15.76% PR) and rhinovirus tests (58 per week, 18.73% PR). There were 803 positive weekly tests (2.53% positivity rate) for SARS-CoV-2 over this same period. Conclusion. Since the start of the SARS-CoV-2 pandemic, the number of positive tests for influenza A/B and seasonal respiratory viruses have not reached prepandemic levels across the YNHHS. However, rates of influenza and other respiratory viruses have increased since the relaxation of NPIs.

Epidemiological investigation of the COVID-19 outbreak in Vellore district in South India using Geographic Information Surveillance (GIS)

Background. Geographical Information Surveillance (GIS) is an advanced digital technology tool that maps location-based data and helps in epidemiological modeling. During pandemics a rapid analysis of patterns of spread can help put in place strategies for containment and infection control. We applied GIS to analyze patterns of spread and hotspots of COVID-19 infected cases in Vellore district in Tamil Nadu, South India. Methods. Laboratory-confirmed COVID-19 patients from Vellore district and neighboring taluks from March 2020 to June 2021 were geo coded (based on addresses) and spatial maps generated. These were then layered as points on the base map to illustrate the distribution of all COVID-19 cases. Time trends exploring urban-rural burden with age-sex distribution of COVID-19 cases and other variables were correlated with outcomes of death, symptoms and complications. Map of undivided Vellore district showing rural and urban settlements. Results. A total of 45,401 cases of COVID-19 were detected between 28 March 2020 to 31 June 2021 with 20730 cases during the first wave (28 March 2020 to 31 March 2021) and 24671 cases during the second wave (1 April 2021 to 30 June 2021). The overall incidence rates of COVID-19 across the study region was 462.8 per 100,000 and 588.6 per 100,000 population during the first and second waves respectively. Pattern of spread revealed epicentres in densely populated urban areas with radial spread, sparing rural areas, Heat maps also confirmed higher densities at these epicentres, however, the second wave had more peri-urban and rural area involvement. Case fatality rate was 1.89% and 1.6% during the first and second waves and increased with advancing age, i.e., 7.38% were aged more than 60 years in the first wave and 5.02% in the second wave. Incidence was higher in men, 2.40%, and 1.76% as compared to women who had 1.16% and 1.38% in the first and second waves respectively. Overall, case fatality rates were the highest among those who had >2 comorbidities (9.52%). Subdistrict level incidence of COVID-19 during the first and second waves. Epidemic curve of the COVID-19 pandemic during the first and the second waves. Conclusion. Modern surveillance systems like GIS can accurately predict the trends of the outbreak and pattern of spread during future respiratory pandemics. Employing this in real time can help design risk mitigation strategies improving health care access and monitoring with prevention of spread to rural areas.

Regional Trends in Favipiravir and Steroid Use in the Treatment of COVID-19

Background. Two years have passed since the global outbreak of COVID-19 began. Vaccines and many therapeutic agents have now been developed, and treatment is being conducted in accordance with guidelines. In general, it takes a long time for guidelines to be established, as a large amount of clinical data is required. Therefore, in the early and middle stages of an epidemic, treatment is often based on experience at individual centers. This study focuses on two drugs, Favipiravir and Steroid, and investigates how trends in drug use changed in different regions. Methods. We compared the proportion of drug administered patients in the COVID-19 Registry Japan (COVIREGI-JP). Data from four COVID-19 epidemic waves, from January 2020 to June 2021, were included for the analysis. To compare regional trends, 10 categories were used based on existing classifications. In addition, Tokyo and Osaka were accounted for separately, for a total of 12 regions. Severity of each case was divided into mild, moderate 1, moderate 2 and severe based on the condition on admission, and the proportion of Favipiravir or Steroid administered cases was calculated for moderate 2 and severe cases. Results. Favipiravir was administered to more than 50-100% of patients in the first wave. Thereafter, it declined nationwide, with sharp falls in Tokyo (34.9%, 16.5% and 4.3%) and Osaka (48.8%, 41.8% and 8.0%). In Hokkaido, on the other hand, 82.4%, 53.7% and 59.8% of the cases still continued to receive Favipiravir. In the first wave, Steroid was administered to 20-40% of cases. The proportion gradually increased, with 50-80% in the second wave, and 85.5% in Tokyo, 93.4% in Osaka and 90.2% in Hokkaido in the fourth wave, the majority of cases. Changes over time in the proportions of cases treated with Favipiravir and Steroid in each region. The top four and middle four panels show the proportion of cases treated with Favipiravir and Steroid, respectively. The lower epi-curve shows the number of COVID-19 cases in Japan. Conclusion. We confirmed that the more effective treatment was rapidly spreading throughout the country. More information is available in areas with a large number of cases, such as Tokyo and Osaka, and in facilities that see a large number of cases. On the other hand, it may be difficult for smaller facilities or facilities that do not see many COVID-19 cases. Information from registry studies would be useful in making more effective treatments available earlier and more widely. We believe that further use of COVIREGI-JP would promote standardization of treatment.

Nonparametric method for estimation of forecasting models using small samples

The coronavirus epidemic has stimulated a surge of research in the field of forecasting the epidemic curve based on various mathematical models. To predict the time series that predicts the number of patients, different models are used, both differential and machine learning models. Differential models for predicting the epidemic curve depend on a number of unpredictable factors. This often results in inaccurate predictions. In contrast to these approaches, machine learning models that predict time series based on training samples show higher reliability. In both cases, the problem arises of testing the hypothesis about the homogeneity of errors on training samples. The paper describes the application of the Klyushin-Petunin test to test the homogeneity of two samples and compares its effectiveness with the widely used the Wilcoxon test and the Diebold-Mariano test and the using the example of three methods for predicting the COVID-19 epidemic curve based on data on the number of cases in a certain period in Germany, Japan, South Korea and Ukraine. The efficiency and usefulness of the proposed nonparametric approach is demonstrated. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

First wave of the COVID-19 pandemic in Madrid: handling the unexpected in a tertiary hospital.

Introduction: The COVID-19 pandemic was declared on March 11th, 2020. By the end of January, the first imported cases were detected in Spain and, by March, the number of cases was growing exponentially, causing the implementation of a national lockdown. Madrid has been one of the most affected regions in terms of both cases and deaths. The aim of this study is to describe the epidemic curve and the epidemiological features and outcomes of COVID-19 patients hospitalized in La Paz University Hospital, a tertiary hospital located in Madrid. Methods: We included confirmed and probable COVID-19 cases admitted to our centre from February 26th to June 1st, 2020. We studied trends in hospitalization and ICU admissions using joinpoint regression analysis. Results: A sample of 2970 patients was obtained. Median age was 70 years old (IQR 55-82) and 54.8% of them were male. ICU admission rate was 8.7% with a mortality rate of 45.7%. Global CFR was 21.8%. Median time from symptom onset to death was 14 days (IQR 9-22). Conclusions: We detected an admissions peak on March 21st followed by a descending trend, matching national and regional data. Age and sex distribution were comparable to further series nationally and in western countries.

Impact of the COVID-19 pandemic on racial and ethnic minorities in Japan.

This study compared clinico-epidemiological characteristics between Japanese and non-Japanese coronavirus disease 2019 (COVID-19) patients under the pandemic in Japan. We retrospectively analysed nationwide data of hospitalised COVID-19 patients before 31 March 2021. Epidemic curves were constructed to identify the case distribution over time. A total of 28 093 patients were Japanese and 1335 patients were non-Japanese. The major racial and ethnic minorities were East Asians (n = 521), South Asians (n = 260) and Latin Americans (n = 270). Non-Japanese patients were younger and more likely to travel to COVID-19 endemic countries (7.7%), had meals with other people (17.8%), stayed in crowded places (17.9%) and worked mainly in restaurants (6.6%) and service facilities in nightlife businesses (5.2%). In the matched cohorts, we found no clear disparities in time to admission and clinical prognoses. The epidemic curve for non-Japanese patients showed a small peak in the first wave and no definite waves for the second or third waves. Racial and ethnic minorities were at less risk of severe disease but were at a greater risk of COVID-19 exposure; however, the healthcare system in Japan may provide them with equal opportunities to access inpatient care with Japanese. Further research on their social determinants of health in Japan is required.

Impact of the SARS-CoV-2 pandemic on healthcare workers (HCWs) in Tunisia from 22 February 2020 to 31 October 2021

Introduction/ Background: COVID-19 has rapidly spread throughout worldwide. HCW being at the frontline, are presumed to be a high-risk population. The objective of this study is to assess the impact of the SARS-CoV-2 infection on HCWs. Methods: We conducted a prospective nationwide cohort study from 22 February 2020 to 31 October 2021, on COVID-19 among HCWs diagnosed by RT-PCR and Rapid Antigen Test, and reported to the National Observatory of New and Emerging Diseases (ONMNE) and followed by regional unit. The following indicators were calculated: cumulative incidence rate (CI), case fatality ratio and relative risk (RR). Descriptive statistics were performed using frequencies, means and proportions. And a Chi-square test were used to determine the association between SARS-CoV-2 infection and different exposures. Results: 13876 cases and 125 deaths of COVID-19 among HCWs were reported representing 1.9% and 0.5% of total infection and deaths respectively, giving a CI of 12300/100000 and a specific case fatality ratio of 1.3%. The mean age was 41y, most cases were females (70.6%), nurses (55.1%), from university hospitals (42.9%) and working at COVID-19 units (32.6%). The epidemic curve showed: The lowest CI was noted during the first phase of the pandemic (41/100000). The RR was 160 time greater during the early stages of the second phase (6694/100000). Most fatal cases occurred in males (3.4%) and within physicians. Impact: The study allowed to determine the occupation category and the working area most at risk for the SARS-CoV-2 infection in Tunisia, which will permit to refine the response against COVID-19. Conclusion: The widespread use of PPE helped control the infection rate among HCWs. The late decrease of the incidence rate can be attributed to the massive vaccination campaign implemented since week 12, 2021 privileging HCWs. Strategies to protect HCWs should prioritize providing adequate PPE as well as testing, surveillance and vaccination.

Predicting Pandemic Curve Distribution Using Statistical Models

This article discusses the distribution of pandemic in the world and pandemic curve in Jordan and how the science of probability and statistics predict when active cases tend to zero by determining the shape of epidemic curve and relating it to a special probability distribution that has specific measures and properties. At the beginning of the outbreak of any virus in a society, reliable data describing it and its spread will be rare, hence researchers set up statistical models that have the ability to predict the spreads’ shape, where the prospected people hosting such viruses will go to and the likelihood of transmitting it to places they travel. Those models use known statistical measures that estimate the probability of disease transmission from infected people to others. In addition, the factors related to roads and people’s movement, taking into consideration, public health interventions, such as wearing masks, closing places of people’s aggregations like schools, universities mosques and churches and quarantine make difference in numbers of infected people. The fundamental differences between the “Spanish flu” that attacked the world a hundred years ago and “Coronavirus” the world facing since the beginning of the current year 2020 is the amount of huge data concluded from scientific studies and reports related to virology and epidemiology.

ModInterv: An automated online software for modeling epidemics.

The COVID-19 pandemic has proven the importance of mathematical tools to understand the evolution of epidemic outbreaks and provide reliable information to the general public and health authorities. In this perspective, we have developed ModInterv, an online software that applies growth models to monitor the evolution of the COVID-19 epidemic in locations chosen by the user among countries worldwide or states and cities in the USA or Brazil. This paper describes the software capabilities and its use both in recent research works and by technical committees assisting government authorities. Possible applications to other epidemics are also briefly discussed.

PREVENTION OF SEASONAL INFLUENZA IN THE CONTEXT OF COVID-19 PANDEMIC

The registered incidence of influenza and acute respiratory diseases (ARD) in Bulgaria remains low in the epidemic period of time during 2020-2021, in the absence of a clear epidemic curve. For the first year during the winter season, influenza viruses were not detected. Continuation of restrictive and preventive measures related to the COVID-19 pandemic and the increase in influenza vaccination coverage enable maintaining the influenza activity at low levels during the winter season. It is required to perform a thorough assessment of the effectiveness of preventive and anti-epidemic measures and the possibilities for their implementation in the following seasons to limiting the spread of influenza and ARD.

Analysis of Particulate Matter Concentration Changes before, during, and Post COVID-19 Lockdown: A Case Study from Victoria, Mexico

The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM2.5 and PM10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM2.5 and PM10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM2.5 and −51.61% for PM10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM2.5 and 100.00% for PM10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM2.5 and PM10, respectively. On the other hand, when comparing the concentrations of PM2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM10, a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs (x2 = 1.204) and between the DL-2021 and DL-2019 pairs (x2 = 0.372), with a p < 0.000 for PM2.5, and the contrast between pairs of PM10 (DL) showed a significant difference between all pairs with p < 0.01. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Incidence of [18F]FDG PET/CT lung findings suspected for COVID-19 in asymptomatic oncological patients after the first pandemic peak in Italy

Background-Aim: The SARS-CoV-2 infection was declared a global pandemic in March 2020 and initially had a wide diffusion in Northern Italy, especially in area of Bergamo. From December 2019 to May 2020 (peak of pandemic period), an increase of interstitial pneumonia cases was observed at [18F]FDG PET/CT in oncological patients in Bergamo area. Further, a significant increase of PETpositive interstitial lung alterations was found with higher incidence compared to the pre-pandemic period. These observations suggested the use of [18F]FDG PET/CT as a sentinel method to identify suspected cases of COVID-19 in asymptomatic oncological patients. This observational prospective multicentric study aimed at confirming the usefulness of [18F]FDG PET/CT in the occasional diagnosis of COVID-19. Methods: [18F]FDG PET/CT performed in oncological patients from May 2020 to January 2021 (post pandemic peak period) in 8 Nuclear Medicine Departments in Lombardy (Humanitas Gavazzeni Bergamo, IRCCS Humanitas Rozzano, IRCCS San Raffaele Milano, Ospedale San Gerardo Monza, IRCCS Ca'Granda Ospedale Maggiore Milano, IRCCS Istituto Nazionale Tumori, Milano) and Tuscany (Azienda USL Nord Ovest Livorno, Azienda Ospedaliera Universitaria Pisana, Pisa) were assessed. The PCR test was proposed to all patients with lung alterations suspected for COVID-19 to confirm the diagnosis of SARS-CoV-2 infection. Results: Overall 19814 patients were studied with [18F]FDG PET/CT for various oncological diseases in the Nuclear Medicine Departments of the Centers involved in the study. We identified 54 out of 19,814 (0.27%) [18F]FDG PET/CT with lung alterations suspected for COVID-19 interstitial pneumonia. 45/54 suspected patients underwent rhinopharyngeal swab for confirmation. 9 patients with lung imaging alterations did not undergo PCR confirmation. PCR test detected 11 positive cases (24% of patients who underwent PCR test). The incidence of PCR confirmed COVID-19 was 11 out of 19,814 (0.06%). Conclusions: Among oncological asymptomatic patients who underwent PET/CT, we identified by PCR test very few cases of COVID-19 pneumonia. This could result from two main reasons: firstly, the low incidence of COVID-19 in the examined period, as confirmed by the epidemiological curve, and, secondly, the effectiveness of environmental and personal hygiene, social distancing, the use of personal protective equipment. However, although PET/CT imaging is not a reference test for the diagnosis of COVID-19 pneumonia, this study demonstrated that [18F]FDG PET/CT can identify lung manifestations of COVID-19.

MAIN SARS-CoV-2 CIRCULATING VARIANTS in SPAIN during the FIRST YEAR of the PANDEMIC

Background: Spain has been one of the main epicenters for Covid-19 in Europe. The country is divided into 17 Autonomous Communities (AC) and two Autonomous Cities (ACi). This study aims to describe the epidemiology of SARS-CoV-2 in Spain across 3 study periods established from the beginning of the pandemic to the third epidemiologic wave, after analyzing genomes from all AC/ACi from February 2020 to March 2021. Methods: All 14,256 available partial and complete Spanish SARS-CoV-2 human genomic sequences deposited in the GISAID repository (https://www. gisaid.org/) until 21 March 2021 were downloaded in nucleotides and classified according to the AC/ACi and to the epidemiological week by collection date. The sequences were assigned to the genetic lineages according to Pangolin COVID-19 Lineage Assigner (https://pangolin.cog-uk.io/). Epiweeks were grouped into three main periods adjusted to the Spanish epidemic curve, as informed in the National Epidemiological Surveillance Network (RENAVE, https://cnecovid.isciii. es). The first period comprised from the beginning of the pandemic to the end of the first state of emergency (June 2020). The second period included the second epidemic wave (June-December 2020), and the third period covered the third wave (December 2020-March 2021). Only AC with at least 10 sequences for each period were described in the results. The two ACi were considered together. Results: Before the national lockdown (14 March 2020), 11 SARS-CoV-2 lineages were circulating in Spain with A.2 lineage predominance. During the lockdown the SARS-CoV-2 variant diversity increased, decreasing during the confinement. During this period, B.1 was the main circulating variant. During summer 2020, B.1.177 became the main circulating variant. The third wave was characterized by the introduction and fast spread of the B.1.1.7 or Alpha Variant of Concern. Conclusion: The reduction of diversity during the lockdown suggests this measure was effective in reducing the import of SARS-CoV-2 lineages. After the opening of borders within Europe during summer 2020, the variant diversity increased again and B.1.177 became the predominant variant, suggesting that despite the efforts to avoid SARS-CoV-2 spread between countries, travel restrictions during summer 2020 were not sufficient to control viral spreading. The variant distribution was heterogeneous among the AC and periods, reflecting different incidence and sequencing capacities across AC.

Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves.

OBJECTIVES: The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. METHODS: We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. RESULTS: We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. CONCLUSION: Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.

A Whole Genome Sequencing Analysis of a Multi-unit Long-term Care Facility COVID-19 Outbreak

Background. The coronavirus disease (COVID-19) pandemic has affected residents in long-term care facilities (LTCF) significantly. Understanding transmission dynamics in this setting is crucial to control the spread of COVID-19 in this population. Using whole genome sequencing (WGS) of SARS-CoV-2, we aimed to delineate the points of introduction and transmission pathways in a large LTCF in Quebec, Canada. Methods. Between 2020-10-28 and 2021-01-09, COVID-19 cases occurred in 102 residents and 111 HCW at a 387-bed LTCF;cases were distributed in 11 units on 6 floors. As part of outbreak analysis, SARS-CoV-2 isolates underwent WGS using the Oxford Nanopore Minion and the Artic V3 protocol. Lineage attribution and sequence types (ST, within 3 mutations) were assigned based on Pangolin classification and variant analysis. Epidemiologic data including date of positive PCR test, resident room number and HCW work location were collected. Self-reported high-risk exposures were collected by HCW questionnaire via phone interview after consent. Cases and their ST, geo-temporal relations and HCW-reported exposures were examined via network plots and geography-based epidemic curves to infer points of introduction and paths of transmission. Results. Of 170 isolates available from 100/102 residents and 70/111 HCW, 130 (76.4%) were successfully sequenced. Phylogenetic analysis revealed 7 separate introductions to the LTCF. Grouping of ST by units was observed, with temporal appearance of ST supporting HCW introduction in 7/11 units. Proportion of phone interview completion was low at 35% (26/70). Few HCW recalled specific high-risk exposures. Recalled exposures supported by genetic linkage revealed potential between-unit introductions from HCW-to-HCW transmission at work and outside the workplace (e.g. carpooling). On one unit, a wandering resident was identified as a likely source of transmission to other residents (Figure 1). Network plot of cases clustered by geographic unit, colour-coded by sequence type. Circles represent residents;addition signs represent healthcare workers. Blue lines represent identified high-risk exposures. Node labels represent floor and unit identifiers;2 units per floor. Conclusion. We demonstrate the complex genomic epidemiology of a multi-unit LTCF outbreak, putting into evidence the importance of a multi-faceted approach to limit transmission. This analysis highlights the utility of using WGS to uncover unsuspected transmission routes, such as HCW contact outside work, which can prompt new infection control measures.

A Year with COVID19 - Experience from the Front Line in a Large Infectious Disease (ID) Clinical Practice

Background. ID Care (IDC) is a large, 43 physician, 74 provider, practice that treats patients in 16 acute care hospitals (ACH) and 120 skilled nursing facilities (SNF) in NJ. March 4, 2021 was the first day a patient with COVID19 seen by IDC. Over the subsequent year IDC evaluated, treated, and tested over 23,000 persons for COVID19. Patients were seen in 2 distinct times - wave 1 (W1) March 5-August 31 and wave 2 (W2) September 1 to March 4. We compare the experience of these 2 waves and report on the year of COVID19 at IDC. Methods. The administrative data base for IDC was queried for demographic, visit and testing information. A survey of providers was performed to capture incidence of COVID19 and vaccination rates. Daily census logs were used to create epi curves. Comparisons between waves were performed using student T Test or X2. Results. Table 1 provides the comparisons between waves. More patients were seen in W2, however, the number of visits per patient was less, consistent with a shorter length of stay. Fewer patients were seen in SNF in W2 compared to W1. The age and sex distribution between the waves were the same. A total of 8741 molecular tests were performed. Test positivity peaked the week of December 31 at 6.99% and dropped to 0% by May 1 consistent with vaccination and the NJ epidemic curve. During the year of COVID19, 6/74 (8%) clinicians were infected with SARSCoV2. All recovered. Infections in providers were not clearly work-related exposures. 73/74 clinicians were vaccinated. Conclusion. The year of COVID19 occurred in 2 distinct waves. W1 was short and intense. The age and gender distributions were the same between the waves. Even though wave 2 was numerically greater, the cases in SNF were statistically less than the first wave likely from improved IP practice initiated in W1. The numbers of visits per patient, a surrogate for LOS, was statistically less in W2. The decline in test positivity paralleled deployment of vaccination. Despite an intensity of exposure of 158 patients/provider or 1198 visits/provider to SARSCoV2 infected persons only 8% of the clinician staff were infected. ID clinical practice can use electronic databases to help describe regional outbreaks of transmissible disease giving additional perspective across the care continuum. A more usable standard tool would enhance this capacity.