Epidemiology and Infection

Immunity to SARS-CoV-2 is building up globally, but will this be sufficient to prevent future COVID-19 epidemics in the face of variants and waning immunity? Manaus, Brazil offers a concerning glimpse of what may come: six months after the majority of the citys population experienced primary infection, a second wave with a new strain resulted in more deaths than the first wave. Current hypotheses for this surge rely on prior immunity waning due to time and antigenic distance. Here we show this hypothesis predicts a severe endemic state. We propose an alternative hypothesis in which individuals infected in the first wave lose protection against transmission but retain immunity against severe disease and show this hypothesis is equally compatible with existing data. In this scenario, the increased number of deaths is due to an increased infection fatality ratio (IFR) for primary infections with the new variant. This alternative predicts a mild endemic state will be reached within decades. Collecting data on the severity of reinfections and infections post-vaccination as a function of time and antigenic distance from the original exposure is crucial for optimizing control strategies.

AO_SCPLOWBSTRACTC_SCPLOWA recent study by Bendavid et al. claimed that the rate of infection of COVID-19 in Santa Clara county was between 2.49% and 4.16%, 50-85 times higher than the number of officially confirmed cases. The statistical methodology used in that study overestimates of rate of infection given the available data. We jointly estimate the sensitivity and specificity of the test kit along with rate of infection with a simple Bayesian model, arriving at lower estimates of the rate of COVID-19 in Santa Clara county. Re-analyzing their data, we find that the rate of infection was likely between 0.27% and 3.21%.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection was a major public health challenge globally and in Ghana. To prepare better for future pandemics, an evidence-based understanding of the determinants of the coronavirus disease is essential to inform public health guidelines and surveillance. Thus, we identified the factors for SARS-CoV-2 infection in Hohoe Municipality. We conducted a facility-based, sex and age-matched (1:2) case-control study. Cases were persons with a laboratory-confirmed SARS-CoV-2 infection by Reverse Transcription Polymerase Chain Reaction (RT-PCR) or rapid antigen test, while controls tested negative with the same techniques. Data on sociodemographic, clinical, and exposure-related factors were collected through structured interviews. We employed a conditional regression model to establish the factors independently associated with SARS-CoV-2 infection using Stata version 17.0. All statistical tests were two-sided, and a p-value <0.05 was considered statistically significant. A total of 234 participants were enrolled (78 cases, 156 controls). The mean age of the cases and controls was 39.7{+/-}(14.6) and 39.4{+/-}(14.4) years, respectively. Moderate/high levels of social interaction increased the odds of infection (aOR=3.00, 95% CI:1.05-8.56, p=0.040). Having no underlying health condition (aOR=0.25, 95% CI:0.09-0.65, p=0.004) and regular physical activity or exercise (aOR=0.18, 95% CI:0.04-0.70, p=0.014) reduced the risk of infection. Moderate/high level of social interaction was associated with increased odds of SARS-CoV-2 infection, and having no underlying condition and frequent exercise/physical activity was protective. Public health interventions should therefore prioritize strengthening community awareness about the risks of close social interactions and the benefits of healthy lifestyles, including regular physical activity.

The publicly available data on COVID-19 cases provides an opportunity to better understand this new disease. However, strong attention needs to be paid to the limitations of the data to avoid making inaccurate conclusions. This article, which focuses on the relationship between the weather and COVID-19, raises the concern that the same factors influencing the spread of the disease might also affect the number of tests performed and who gets tested. For example, weather conditions impact the prevalence of respiratory diseases with symptoms similar to COVID-19, and this will likely influence the number of tests performed. This general limitation could severely undermine any similar analysis using existing COVID-19 data or similar epidemiological data, which could, therefore, mislead decision-makers on questions of great policy relevance. One Sentence Summary: Measurement issues in the currently available data on confirmed COVID-19 cases undermine the analyses of the drivers of the spread of the disease.

Two papers authored by the same research group were published in academic journals in October 2023, both of which simulate counterfactual COVID-19 cases and deaths using transmission models. One paper estimates that the COVID-19 cases and deaths from Feb 17 to Nov 30, 2021 in Japan would have been as many as 63.3 million and 364 thousand respectively had the vaccination not been implemented, where the 95% confidence interval is claimed to be less than 1% of the estimated value. It also claims that the cases and deaths could have been reduced by 54% and 48% respectively had the vaccination been implemented 14 days earlier. The other paper estimates that the number of cases in early 2022, Tokyo would have been larger than the number of populations in the age group under 49 in the absence of the vaccination program. In this paper, we reexamine the results given by these papers to find that the simulation results do not explain the real-world data in Japan including prefectures with early/late vaccination schedules. The cause of discrepancy is identified as low reliability of model parameters that immensely affect the simulation results of case and death counts. Leaders of public healthcare are required to discern the reliability and credibility of simulation studies and to prepare for variety of possible scenarios when reliable predictions are not available.

Since the first analysis was published on 7 April 2021 the PCR test positivity rate has dropped significantly below the then estimated false positive rate (FPR) of 1.16% using the exponential decay to FPR model. Therefore, the estimate has been rejected and a new model was developed. Using the ONS infection surveys assumption (PCR FPR rate below 0.1%) the new model splits the test time series data into two periods based on a change in transmissibility that coincides with the reopening of England schools on 8 March. The new model provides for two base levels of exponential decay (for each periods transmissibility) combined with a single decay rate increase dependent on vaccination. Because the FPR is relatively insignificant compared to current PCR test positives, it cannot be statistically separated using currently available England epidemic time series data by the non-linear least squares estimation technique. Therefore, the FPR factor is temporarily dropped in the least squares regression. The new model is stable in that it reasonably predicts through the most current available data (25 April) the future test prevalence using parameters estimated with 29 March data. Thus far, the estimate parameters remain within their original confidence intervals as successive days are added to the time series. Of potential usefulness is the current estimate for change in decay rate per mean vaccination rate, currently estimated at approximately 10.7% (CI: 8.8% - 12.6%). The estimate should be used with caution as other unforeseen factors could cause the model to misestimate.

Various studies have looked into the impact of the COVID-19 vaccine on large populations. However, very few studies have looked into the remote setting of hospitals where vaccination is challenging due to social structure, myths, and misconceptions. There is a consensus that elevated inflammatory markers such as CRP, ferritin, D-dimer correlate with increased severity of COVID-19 and are associated with worse outcomes. In the present study, through retrospective meta-analysis, we have looked into [~]20 months of SARS-COV2 infected patients with known mortality status and identified predictors of mortality concerning their comorbidities, various clinical parameters, inflammatory markers, superimposed infections, length of hospitalization, length of mechanical ventilation and ICU stay. Studies with larger sample sizes have covered the outcomes through epidemiological, social, and survey-based analysis--however, most studies cover larger cohorts from tertiary medical centers. In the present study, we assessed the outcome of non-vaccinated COVID 19 patients in a remote setting for 20 months from January 1, 2020, to August 30, 2021, at CHI Mercy Health in Roseburg, Oregon. We also included two vaccinated patients from September 2021 to add to the power of our cohort. The study will provide a comprehensive methodology and deep insight into multi-dimensional data in the unvaccinated group, translational biomarkers of mortality, and state-of-art to conduct such studies in various remote hospitals.

WHAT WAS ALREADY KNOWN?O_LIVariants influence COVID-19 severity: Variants such as Delta and Gamma were already associated with increased transmissibility and lethality. C_LIO_LIVaccination has a protective role against severe forms of the disease: Previous data showed reduced mortality with vaccines, especially after completing the primary series. C_LIO_LIRegional inequalities impact outcomes: Regions with lower healthcare infrastructure, especially in the North and Northeast, face higher fatality rates. C_LI WHAT IS NEW?O_LIThree major waves of SARS due to COVID-19, with marked differences by region and variant: The second wave was the deadliest, and Omicron, though less pathogenic, had widespread circulation and three distinct peaks. C_LIO_LIDirect impact of sociodemographic and economic factors on outcomes: Lethality was higher among illiterate individuals, Black and Brown populations, and rural residents. C_LIO_LIRobust effect of booster vaccination on survival: Individuals with a first or second booster were up to 60% less likely to die and had four times higher survival at 40 days compared to unvaccinated individuals. C_LI The COVID-19 crisis exposed deep-rooted inequalities in Brazil, such as the concentration of healthcare resources in metropolitan areas. Despite the availability of effective vaccines, full immunization coverage was not achieved, allowing cases to progress to severe acute respiratory syndrome (SARS). This study aimed to analyse the progression of mild, moderate, and severe cases between January 2020 and December 2024, covering a five-year period, taking into account regional, social, and variant-related differences, vaccination, and factors associated with SARS due to COVID-19 in Brazil. A total of 47,547,814 influenza-like illness (mild and moderate) cases, 2,127,427 SARS cases due to COVID-19, 533,966,291 vaccination records, and data on circulating variants were analysed. Among the SARS cases, 1,171,801 were confirmed by PCR; 777,672 patients recovered, and 394,129 died, resulting in a case fatality rate of 33.63%. Brazil experienced three major waves of SARS due to COVID-19, with the second wave being the deadliest across all regions. The Gamma and Omicron variants were the most persistent and impactful. The transition between variants influenced the regional dynamics of the pandemic, although little variation was observed in the proportion of circulating variants across regions. The study highlights the importance of continuous monitoring, genomic surveillance, and vaccination coverage to anticipate and mitigate future pandemic waves.

Epidemiologic measures quantifying the causative or the preventive effect of a particular agent with respect to a given disease are frequently used, but the set of assumptions on which they rest, and the consequences of these assumptions, are not widely understood. We present a rigorous derivation of these measures from the sufficient-causes model of disease occurrence and from the definition of causation as the bringing forward of the occurrence time of an event. This exercise brings out the fact that an understanding of the assumptions underpinning all measures of effect, and of the extent to which they may or may not be met, is necessary to their prudent interpretation. We also introduce a new measure, discarding 1) the sufficient-causes model and 2) the assumption that the agent can only be either causative or preventive, relative to a given disease, but not both. Some may consider this more acceptable than having to decide, on slim or no evidence, that the agent has only one kind of effect on the disease. In any case, I submit that epidemiology should eventually discard the concept of causation, as has been done in some other basic sciences, and replace it with the adequate modeling of disease-producing processes, in individuals and populations.

The resurgence of pertussis (whooping cough) becomes a serious problem in many countries including the UK. Differentiation of the accumulated monthly numbers of pertussis cases registered in England in 2023 and 2024 revealed two waves of the epidemic before and after October 2023. Identification of parameters of SIR (susceptible-infectious-removed) model allowed calculating the numbers of infectious persons and reproduction rates. The accumulated and daily numbers of cases and the duration of the first wave were predicted. If the influence of second wave will be not very significant, the new cases will stop to appear in the end of August 2025 after reaching the figure of 5.8 thousand. The maximum of average daily numbers of new cases is expected to be around 51 on 9-10 May 2024. Since the effective reproduction number is very close to its critical value 1.0, the probably of new outbreaks is very high. May be the, increase of percentage of vaccinated people could decrease this probability.

Over the course of the pandemic, COVID-19 affected health, the economy and quality of life in Brazil. The worst years for the country were the first and second. There were delays in vaccine purchases for political reasons at the time. The northern region of the country had a higher mortality rate compared to other regions, associated with local vulnerabilities and fragility of surveillance due to geographic and population characteristics. This study aims to investigate the clinical profile, comorbidities, and outcome of unvaccinated people hospitalized for COVID-19 in the state of Para in 2022. Retrospective cohort epidemiological study, with data from the national epidemiological surveillance of acute and severe respiratory syndromes. Cases reported in 2022 with vaccinated yes or no field and completed doses were included. Only closed cases cure or death were included. We performed a chi-square test on categorical variables and a Mann-Whitney test on numerical variables. We compared vaccinated VS non-vaccinated; we performed the Odds Ratio in the significant variables. We used the SPSS 20.0 software. The study worked with 2,634 cases of COVID-19 hospitalized in the study period, confirmed by RT-PCR (851/32.30%) and (1,784/67.70%) rapid antigen test. The lethality was (778/29.53%), and those vaccinated with two doses were (1,473/55.90%) and those unvaccinated with no dose (1,162/44.10%). Death represents p-<0.001 (HR 1.306 - CI 1.124/1.517) higher risk of the event occurring in the unvaccinated cases, followed by male sex p-0.004 (HR 1.188 - CI 1.058/1.334).. The first cohort in Brazil and in the north of the country to evaluate the clinical profile, comorbidities, and outcome of COVID-19 in hospitalized patients in this Amazon region, which is a region characterized by local vulnerability factors unique to the other regions of Brazil, showed that the unvaccinated were males, younger, with fewer comorbidities, and that they were associated the deaths.

COVID-19 has spread to most countries in the world. Puzzlingly, the impact of the disease is different in different countries. These differences are attributed to differences in cultural norms, mitigation efforts, and health infrastructure. Here we propose that national differences in COVID- 19 impact could be partially explained by the different national policies respect to Bacillus Calmette-Guerin (BCG) childhood vaccination. BCG vaccination has been reported to offer broad protection to respiratory infections. We compared large number of countries BCG vaccination policies with the morbidity and mortality for COVID-19. We found that countries without universal policies of BCG vaccination (Italy, Nederland, USA) have been more severely affected compared to countries with universal and long-standing BCG policies. Countries that have a late start of universal BCG policy (Iran, 1984) had high mortality, consistent with the idea that BCG protects the vaccinated elderly population. We also found that BCG vaccination also reduced the number of reported COVID-19 cases in a country. The combination of reduced morbidity and mortality makes BCG vaccination a potential new tool in the fight against COVID-19.

Clostridioides difficile stands as the leading cause of hospital acquired enteric infection in developed countries. In Argentina, the epidemiology of Clostridioides difficile infection (CDI) is currently poorly characterized. Therefore, we conducted a retrospective case-control study evaluating the prevalence of CDI in 249 stool samples collected between 2019 and 2023 in the Northwest region of Buenos Aires. The presence of C. difficile was detected by combining three techniques (EIA, PCR and toxigenic culture) in a diagnostic algorithm. Clinical and demographic data from patients was also analyzed to identify CDI-associated risk factors. 1 in 5 patients presented C. difficile as the etiological agent of diarrhea and the 80% of CDI+ cases carried toxigenic strains, with a third of cases acquired in the community. Age [&ge;]69 years, previous use of antibiotics, previous hospitalization and previous episodes of CDI emerged as predisposing factors for CDI in our study cohort. Blood parameters such as an elevated number of leukocytes and platelets, a decreased basophil count, and an increased urea concentration were identified as indicators of CDI. We also carried out a systematic review and a meta-analysis where we contrasted our results with 39 studies selected from different countries around the world. At the global level, the meta-analysis highlighted advanced age, previous consumption of antibiotics and previous hospitalization as CDI risk factors and the leukocyte count as an indicator of CDI. These results emphasize the importance of epidemiological studies and reveal crucial information for healthcare decision-making regarding CDI. O_FIG O_LINKSMALLFIG WIDTH=139 HEIGHT=200 SRC="FIGDIR/small/24306385v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@d5f16org.highwire.dtl.DTLVardef@1ddabbdorg.highwire.dtl.DTLVardef@361edeorg.highwire.dtl.DTLVardef@124a9f6_HPS_FORMAT_FIGEXP M_FIG C_FIG

Since late December 2019 a new epidemic outbreak has emerged from Whuhan, China. Rapidly the new coronavirus has spread worldwide. China CDC has reported results of a descriptive exploratory analysis of all cases diagnosed until the 11th February 2020, presenting the epidemiologic curves and geo-temporal spread of COVID-19 along with case fatality rate according to some baseline characteristics, such as age, gender and several well-established high prevalence comorbidities. Despite this, we intend to increase even further the predictive value of that manuscript by presenting the odds ratio for mortality due to COVID-19 adjusted for the presence of those comorbidities and baseline characteristics such as age and gender. Besides, we present a way to determine the risk of each particular patient, given his characteristics. We found that age is the variable that presents higher risk of COVID-19 mortality, where 60 or older patients have an OR = 18.8161 (CI95%[7.1997; 41.5517]). Regarding comorbidities, cardiovascular disease appears to be the riskiest (OR= 12.8328 CI95%[10.2736; 15.8643], along with chronic respiratory disease (OR=7.7925 CI95%[5.5446; 10.4319]). Males are more likely to die from COVID-19 (OR=1.8518 (CI95%[1.5996; 2.1270]). Some limitations such as the lack of information about the correct prevalence of gender per age or about comorbidities per age and gender or the assumption of independence between risk factors are expected to have a small impact on results. A final point of paramount importance is that the equation presented here can be used to determine the probability of dying from COVID-19 for a particular patient, given its age interval, gender and comorbidities associated.

In a disease, where all infected persons show symptoms, it is reasonable to calculate mortality by case to fatality rate CFR. Deaths follow infections by a certain time lag. However, in the Covid-19 pandemic many infectious patients show no or hardly any symptoms. The reported infections and deaths do not run parallel, but diverge with the volume of tests. Our investigations for Germany, USA and UK indicate that deaths do not follow the number of infections, but the positive rate of tests, multiplied by a constant factor F and shifted by about two weeks. These test adjusted results of mortality allow for the estimation of the number of deaths of Covid-19 about two weeks ahead, even in a sharply rising state of the pandemic. This gives medical authorities two weeks of time to plan for resources.

Household contact studies are frequently used in tuberculosis transmission research, and models based on them often focus on transmission within the household. This contradicts recent research which suggests the transmission may be more likely to happen outside the household than within the household in high burden settings where these studies are frequently conducted. Consequently, most models would lead to biased estimates and misleading public health interventions. There is a strong need for developing models that allow concurrent estimation of household and extra-household transmission. In this study, we develop a random directed graph model for tuberculosis transmission, which permits users to concurrently build models for both household and extra-household transmission. Furthermore, our model can estimate the relative frequency of household transmission versus extra-household transmission and consistently produce unbiased estimates for risk factors, regardless of whether community controls are available. We illustrate our approach with a household contact study conducted in Vitoria, Brazil, and our results indicate that extra-household transmission can account for 63% to 98% of M. tuberculosis infections detected during such a study.

SummaryIn Germany and other countries, a second wave of corona infections has been observed since July 2020, after the first wave has subsided. We have investigated both waves by a modified SIR-SI infection model, adapted to the data to the Robert-Koch-Institute (RKI) or the Johns-Hopkins-University (JHU). The first wave is characterized by the SIR model: in a perfect lockdown only a small part of the society is infected and the infections end after a certain time. The SI part considers the incompleteness of any lockdown: at the end of the first wave infections do not completely go down to zero, but continue to rise again, but only slowly due to mouth protection, hygiene and distance keeping. During this first wave the number of deceased people follows the number of infected persons with a fixed time interval and percentage: mostly symptomatic ill people have been tested. This applied to nearly all countries observed, with different intervals and percentages. In the present second wave, the number of daily infections has risen again significantly in some countries, and it may be questioned whether this is due to the increased number of tests. The answer may be given by looking at the daily number of deaths. In Germany, Austria, Italy, Great Britain and others this number has still remained at a constant level for six weeks. In these countries a second wave of died people has not yet arrived. The increased number of tests include obviously mostly asymptomatically infected persons, who do not fall ill or die from coronavirus. However, in some countries, like USA or Israel, the second wave did arrive. The numbers of infected and deceased people both have grown. A real second wave is a permanent threat to all countries.

This study investigates symptom associations in SARS-CoV-2 positive individuals based on vaccination status. Unvaccinated individuals exhibited significantly higher odds of experiencing severe symptoms, including fever > 38.5{square}{degrees}C (logOR = 3.64, 95% CI: 1.52-5.77, p = 0.0008), rhinitis (logOR = 2.94, 95% CI: 0.82-5.05, p = 0.0065), headache (logOR = 2.17, 95% CI: 0.06-4.28, p = 0.0436), and myalgia (logOR = 3.25, 95% CI: 1.17-5.34, p = 0.0023). Conversely, unvaccinated individuals were less likely to report cough (logOR = -2.44, 95% CI: -4.53 to -0.34, p = 0.0226), potentially reflecting behavioral factors related to vaccine hesitancy. Among vaccinated participants, symptom profiles varied by vaccine type. Both Oxford-AstraZeneca and Pfizer-BNT162b2 vaccines were associated with increased odds of loss of smell (logOR = 0.81 and 1.47, respectively) and loss of taste (logOR = 0.75 for Oxford-AstraZeneca). Additionally, the Oxford-AstraZeneca vaccine was linked to dyspnea (logOR = 0.85, 95% CI: 0.02-1.69, p = 0.0449). These findings suggest that vaccination reduces the likelihood of severe systemic and respiratory symptoms while influencing specific symptom manifestations in breakthrough cases. Ongoing research is essential to understand vaccine-specific immune responses and their impact on clinical outcomes.

We estimated risk of death in vaccinated compared to unvaccinated COVID-19 cases. Cases vaccinated with 1 dose of BNT162b2 had 44% reduced risk of death, 55% with 1 dose of ChAdOx1, and 69% with 2 doses of BNT162b2. This is on top of the protection provided against becoming a case.

The article provides an estimate of the size and duration of the Covid-19 epidemic in August 2020 for the European Union (EU), the United States (US), and the World using a multistage logistical epidemiological model.