Мониторинг безрецептурных лекарств и лекарств для лечения covid-19 дополняет контроль за сточными водами на наличие вируса sars cov-2

Когда можно перестать быть заразным COVID-19?

Принятие профилактических мер при посещении общественных мест и общении с другими после выздоровления от COVID-19 необходимо. Вы можете помочь снизить распространение вируса, принимая необходимые меры предосторожности.

Понимание инкубационного периода COVID-19

Важность вакцинации

Получив вакцину от COVID-19, вашему иммунитету учится распознавать вирус как чужеродный элемент и бороться с ним. Исследования показывают, что вакцины от COVID-19 могут значительно снизить вероятность заражения вирусом. Но если вы все же заразитесь после вакцинации, вакцина все равно поможет вам защититься от тяжелого течения болезни или госпитализации.

Защита после вакцинации

Важно отметить, что вы получите оптимальную защиту только через 2 недели после получения второй дозы двухдозовой вакцины. Это потому, что вашему организму требуется примерно 2 недели, чтобы создать защиту от вируса. И потому что инкубационный период короче, чем время между дозами, возможно заразиться COVID-19 до или сразу после вакцинации, так как вашему организму не хватило времени на укрепление иммунитета. Если это произошло, Центры по контролю и профилактике заболеваний рекомендуют подождать, пока вы полностью не выздоровеете, чтобы пройти вакцинацию.

Изоляция и тестирование

Если у вас есть симптомы, необходимо изолироваться от других людей в вашем доме. Выберите отдельную комнату или область для проживания и, по возможности, используйте другую ванную комнату.

Центры по контролю и профилактике заболеваний сократили рекомендуемое время изоляции для людей с COVID-19 без симптомов до 5 дней. По истечении этого периода они рекомендуют носить маску в присутствии других еще 5 дней.

Если вы были в контакте с человеком с COVID-19, симптомы могут проявиться через 3-5 дней. Если вы находитесь в общественных местах или находитесь в близком контакте с людьми, важно быть внимательным и соблюдать меры предосторожности. Активная вакцинация против COVID-19 поможет снизить вероятность тяжелого заболевания.

Статистика заболеваемости

Ниже представлена статистика развития пандемии коронавируса Covid-19 в России:

Дата	Заболело	Выздоровело	Умерло
01.01.22	1000	800	20
10.01.22	1500	1200	25
20.01.22	2000	1600	30

Пожалуйста, следите за рекомендациями специалистов и принимайте все необходимые меры для соблюдения безопасности и здоровья.

На 25 февраля 2024 в России зафиксировано 23 014 969 случаев заражения коронавирусом Covid-19. За последние сутки число зараженных выросло на 23 014 969 человек.Общее число смертей от коронавирусной инфекции в России составляет 400 023 человека, сегодня зафиксировано 400 023 случая смерти.В активной фазе болезни находятся 156 638 человек, из них 0 в критическом состоянии. Уровень летальности: 1.74%.Подтвержденных случаев полного излечения от вируса на сегодня, 25 февраля 2024 в России: 22 458 308.

На графике представлены значения подтвержденных случаев заражения коронавирусом Covid-19 в России по дням от начала сбора официальной статистической информации.

* Нулевые значения означают отсутствие данных

Статистика заражений коронавирусом Covid-19 в России

График выявленных случаев заражения коронавирусом Covid-19 в России по датам.

Статистика смертей от коронавируса Covid-19 в России

График официально зарегистрированных смертей с подтвержденным диагнозом коронавирус Covid-19 в России по датам.

Статистика заражений коронавирусом по странам мира на сегодня, 25 февраля 2024

Быстрый доступ к статистике:

Страны мира

COVID-19 surveillance data will be updated every Thursday by 5:00 p.m. unless otherwise noted. Wastewater data will be updated with all data that is available to CDH at the time of posting.

This graph represents all wastewater sample results available to CDH. The samples were collected from participating water renewal facilities in CDH jurisdiction. Gaps in the wastewater surveillance data lines indicate days where no samples were collected, or technical issues with samples at the lab occurred and the results were left out.

This graph represents the most recent wastewater concentrations for participating water renewal facilities over a rolling six-month period. Gaps in the wastewater surveillance data lines indicate days where no samples were collected or technical issues with samples at the lab occurred and the results were left out.

This graph represents the total number of long-term care facilities (LTCFs) experiencing active COVID-19 outbreaks in CDH jurisdiction. An outbreak is declared when two or more COVID-19 cases among residents or staff members in a single facility are identified and reported to CDH. Boise County has no LTCFs and is not represented in the graph.

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Materials and methods

Wastewater samples were collected from a WWTP located in Suffolk County, NY. The WWTP serves ~330,000 people in its sewer catchment and treats ~30.5 million gallons of wastewater daily. Untreated raw sewage influent was collected via autosampler every 15 min to make up a 24-h composite sample, refrigerated upon collection to <6 °C. The 24-h composite sample was subsampled into a 500-ml polypropylene bottle, stored in a cooler with ice packs, and then transferred to our lab (~1 h drive) for subsequent analyses. Viral analysis was performed immediately upon sample receipt and the entire procedure was completed within a day. The remaining sample was then split into two aliquots and stored at −80 °C without adding any preservatives, one for drug analysis and the other as an archived sample. Suspended particles in the samples for drug analysis were removed through vacuum filtration (1 µm glass fiber) prior to freezing. The subsequent drug analysis was performed within 3 weeks. Sampling was initiated on June 2020, with daily sampling from June 3 to June 9, weekly sampling from June 9 to July 7, and biweekly sampling from July 7 to December 22, 2020, and twice weekly sampling from January 2021 through January 6, 2022.

Detection and quantification of SARS-CoV-2 RNA

Twenty-four-hour composite samples of raw sewage were centrifuged at 4200 rpm for 30 min at 4 °C in order to remove large particles and debris before polyethylene glycol (PEG) precipitation. To evaluate the viral recovery rates from wastewater, bovine coronavirus (BCoV), which belongs to the same genus as SARS-CoV-2, was spiked into the supernatant. The viral particles in 40 ml of samples were precipitated with PEG 8000 (Millipore Sigma, Burlington, MA) and NaCl (5 M, Millipore Sigma, Burlington, MA) and then incubated overnight at 4 °C. RNA from the PEG-precipitated wastewater was extracted by Qiagen QIAamp DSP viral RNA mini kit (Qiagen, Hilden, Germany) according to manufacturer’s instructions and eluted in 100 µl by nuclease-free water. The concentrations of RNA were measured by NanoDrop One Spectrophotometer (Thermo Fisher Scientific, Waltham, MA). All RNA samples were stored at −80 °C and subjected to cDNA synthesis within the same day of RNA extraction to avoid losses associated with storing and freezing and thawing RNA extracts.

Detection of COVID-19 treatment drugs and other pharmaceuticals

Due to the low concentration of COVID-19 treatment drugs, SPE was required to concentrate the sample for detection. In contrast, other pharmaceuticals were measured by direct injection after dilution. In brief, a 100-ml sample was transferred out and spiked with a surrogate standard (hydroxychloroquine-D4) to trace the extraction yield prior to SPE. After conditioning the SPE cartridge (Waters Oasis HLB, 200 mg, 6 cc) with methanol and deionized water, the whole sample was loaded onto the cartridge, and after which the cartridge was eluted sequentially with 4 ml methanol, resulting in ~25-fold preconcentration. Extracts were stored at −20 °C until analysis. Prior to analysis, the extract was diluted with deionized water (50:50 MeOH: H2O) and spiked with the internal standard. For other pharmaceuticals, a 100-µl sample was taken out and diluted 10-times with deionized water and methanol to constitute a final concentration of 10% methanol. The isotopically labeled internal standards were then added before analysis. The detailed information for the surrogates and internal standards is shown in Table S1.

Detection and quantification of the target compounds in extracts were carried out using an Agilent 6495B triple-quadrupole mass spectrometer (LC-MS/MS) with an electrospray ionization source in positive ion mode (ESI+), using Multiple Reaction Monitoring (MRM) to monitor the precursor ions and product ions (Table S1). The detailed instrumental conditions are shown in Table S2.

Stability of COVID-19 treatment and OTC drugs in wastewater

We also received reports from Stony Brook University Hospital of daily hospitalized cases, and milligram of COVID-19 treatment drugs, hydroxychloroquine and remdesivir prescribed daily beginning Oct 3, 2020 to the present. This hospital is not physically located in the catchment area but is the closest level 1 Trauma center to the catchment area, and receives patients from the catchment area. These data, therefore, are not used as proxies for the amount of remdesivir or hydroxychloroquine in the catchment area, but rather are useful for understanding temporal trends in prescriptions of these treatment drugs in the region. As shown in Fig. S1, remdesivir usage can reflect the case trend in the hospital, whereas hydroxychloroquine usage remains relatively stable over time.

Population correction

where m = number of substances (e.g., viral or/and chemical concentrations) and n = number of lags (observations before the focal day) used for prediction. We considered three specific sets of models. For each set of models, we tested possible combinations of variables according to Data Exploration and Cross Correlation (see SI). The choice of priors was examined by predictive simulations in the SI, and the posterior distributions of parameters were estimated using Markov Chain Monte Carlo (MCMC). We retained the model with the best predictive performance based on the Watanabe–Akaike Information Criterion (WAIC). Two general rules were also applied in the modeling. First, we used consecutive lags because the change of predictor variables was more likely to have gradual effects on our outcome variable. Second, to avoid overfitting, we maintained at least ten observations for each predictor included in the models, as 111 observations were present in our sample.

Results and discussion

Beyond antiviral drugs, 13 out of 26 pharmaceuticals assessed were detected (DF = 100%, n = 111) in all wastewater samples during this period. Acetaminophen (mean = 83.6 µg/l, range: 19.5–237 µg/l) and caffeine (mean = 88.2 µg/l, range: 48.2–148 µg/l) and its metabolite paraxanthine (mean = 23.1 µg/l, range: 12.6–37.8 µg/l) were the most abundant chemicals in wastewater samples. A summary of statistics of the viral RNA and all the drugs measured in this study is listed in Table S3.

Correlations and temporal trend of viral genes and biomarkers in sewage samples

Fig. 1: Summary of the Pearson correlation coefficient (R) between variables (n = 111).

Only significant correlations (p < 0.05) were shown. All the variables were normalized by caffeine, except for reported confirmed cases.

Full size image

Fig. 2: Temporal trend of SARS-CoV-2 virus and COVID-treatment drugs in the sewer catchment area from June 2020 to January 2022.

a shows the daily reported confirmed cases and the normalized virus concentration in WW over time. b–f are the normalized concentrations of COVID-treatment drugs and their metabolites detected in WW over time. The green line in (b–f) represents a moving average trendline.

Fig. 3: Temporal trend of pharmaceuticals in the sewer catchment area from June 2020 to January 2022.

The normalized concentrations of twelve pharmaceuticals with 100% detection frequency versus time are shown in (a–l). All the concentrations are normalized to caffeine. The red line in (a–l) represents a moving average trendline.

Fig. 4: Correlations between the confirmed cases and acetaminophen.

Three different stages of prevailing viral variants are presented in different colors.

Forecasting model using viral gene and biomarker data

Fig. 5: Confirmed case predictions using viral gene and biomarker data from June 2020 to January 2022.

A different set of predictors is used in each panel: (a) virus, (b) virus and desethylhydroxychloroquine, and (c) virus, acetaminophen, and desethylhydroxychloroquine. The red lines and their respective black dashed lines in each panel represent the posterior predictions with a 95% confidence interval. The blue lines show the real confirmed cases.

We used an additional dataset of viral concentrations to validate our model. The data were collected from January 2022 through May 2022, consisting of 39 measurements. However, because analysis of COVID-treatment and OTC drugs were not available from this period, we were only able to validate our first model with the prediction solely by viral RNA concentrations. We used viral concentrations from this new dataset as inputs to predict confirmed cases in the same period (Fig. 6). It should be noted that this dataset was not used for model development. The shaded area in Fig. 6 indicates that the model captured the confirmed case trend quite well, with the exception of the peak in cases. The difference between the predicted and reported cases around the peak might be due to measurement uncertainties or other human factors. Nonetheless, considering the model can be continuously calibrated by new datasets and its purpose for out-sample prediction, it should have utility for future use.

Fig. 6: Model validation using ongoing viral data collected after January 2022 (shaded area).

The model was developed and trained with the data collected from June 2020 to January 2022 (unshaded area).

Study limitations

One major limitation for applying WBE during an emerging pandemic could be the immediate availability of analytical standards for treatment drugs and references for viral lineages, which is critical for rapid method development with accuracy and precision. For example, the isotopically labeled remdesivir was not commercially available during the time the analysis was performed in this study.

The purpose of modeling and prediction is to use available data to infer the unknown. Out-of-sample possibilities are therefore considered in modeling to reduce the chances of overfitting. That said, there might be some limitations to the model. In a Bayesian context, these limitations can arise from mistaken assumptions about the underlying infection process, such as the distributions of parameters and the selection of priors. Measurement errors of substances within and across research sites, as well as human factors like state- or borough-wise policies, could undermine the accuracy of predictions. Fortunately, thanks to the flexibility of Bayesian models, new datasets from different areas and times can be utilized to calibrate the model.

Conclusions

Monitoring of viral RNA, COVID-treatment drugs, and other pharmaceuticals in wastewater samples over a period of ~20 months in Suffolk County, NY, revealed that viral gene copies, across different variant prevailing periods, reflected the time series of COVID-19 confirmed cases in the sewer catchment area with a calculated lead time of 3–4 days. Antiviral drugs and their metabolites were detected with varying frequencies in wastewater samples. The rationale for monitoring COVID-19 treatment drugs in wastewater was to understand treatment of patients in the community. However, the stability of these drugs was low in wastewater and, hence, suggested that these drugs were not ideal biomarkers. However, acetaminophen (OTC) and desethylhydroxychloroquine were significant correlated with the viral concentrations in wastewater and acetaminophen was also correlated with the prevalence of COVID-19 in the community. Acetaminophen exhibited a short-to-non-existent lead time (0-to-2 days) ahead of the virus and reported cases, which agreed with the symptom progression of COVID-19. Acetaminophen is abundant in wastewater and can be analyzed with minimum sample preparation compared to viral RNA analysis. Since acetaminophen and other similar OTC drugs are not specific to COVID treatment, their variations in wastewater may inform important changes in population health within the sewershed. We suspect other viral outbreaks with similar symptoms may also be revealed by monitoring these OTC drugs in wastewater. Using the viral RNA and pharmaceuticals data, we developed Bayesian models to predict the confirmed cases (infected individuals) within the catchment area. The models were capable of reproducing the temporal trend of the confirmed cases from June 2020 to January 2022 and accurately predicting COVID-19 cases in the community using viral loads in wastewater from January to May 2022.

Data availability

The data generated and analyzed in this study are available upon reasonable request.

Acknowledgements

We would like to acknowledge the support received from the Suffolk County Department of Health Services and the participating wastewater treatment facility operators in collecting wastewater samples analyzed in this study.

Funding

This work was partly supported by a grant to the Center for Clean Water Technology at Stony Brook University from the New York State Department of Health and the Suffolk County Department of Health Services. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.

Author information

C-SL: designed experiments, methodology, acquired data, led writing of original draft and revisions; MW: assisted in the design of experiments, methodology, acquired data, and helped write the original draft; DN: methodology, acquired data, and helped write the original draft; Y-TL: model development, data analysis, review and edit; JM: data curation, review and edit; SC: data curation, review and edit; CJG: conceptualization, review and editing, funding acquisition; AKV: project conception, data interpretation, writing, review and editing manuscript, funding acquisition.

Corresponding author

Correspondence to Arjun K. Venkatesan.

Ethics declarations

The authors declare no competing interests.

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About this article

Lee, CS., Wang, M., Nanjappa, D. et al. Monitoring of over-the-counter (OTC) and COVID-19 treatment drugs complement wastewater surveillance of SARS-CoV-2. J Expo Sci Environ Epidemiol (2023). https://doi.org/10.1038/s41370-023-00613-2

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COVID-19 Treatment Options

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