array(3 items)
   publications => '35788,35350,34703,35242' (23 chars)
   libraryUrl => '' (0 chars)
   layout => '0' (1 chars)

array(4 items)
   0 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=35788, pid=124)
      originalId => protected35788 (integer)
      authors => protected'Riou, J.; Fesser, A.; Wagner, M.; Schneider, K.; Güdel-
         Krempaska, N.; Ort, C.; Julian, T. R.; Stadler, T.;
          Munday, J. D.' (176 chars)
      title => protected'Determinants and spatio-temporal structure of variability in wastewater SARS
         -CoV-2 viral load measurements in Switzerland: key insights for future surve
         illance efforts' (167 chars)
      journal => protected'PLoS Water' (10 chars)
      year => protected2025 (integer)
      volume => protected4 (integer)
      issue => protected'11 November' (11 chars)
      startpage => protected'e0000453 (17 pp.)' (17 chars)
      otherpage => protected'' (0 chars)
      categories => protected'' (0 chars)
      description => protected'<em>Background:</em> Wastewater-based surveillance (WBS) has emerged as a va
         luable tool for monitoring the circulation of infectious pathogens in the po
         pulation, offering a complement to classical surveillance methods. However, 
         the interpretation of WBS data is often challenged by substantial variabilit
         y and bias in viral load measurements, which can stem from differences in la
         boratory protocols, population demographics, and in-sewer fate and transport
          processes.<br /><em>Methods and findings:</em> We analysed 23,025 wastewate
         r samples collected between February 2022 and November 2023 from 118 wastewa
         ter treatment plants (WWTPs) across Switzerland. Samples of influent wastewa
         ter were processed by 8 independent laboratories using distinct concentratio
         n, extraction and quantification methods to estimate SARS-CoV-2 concentratio
         ns. Concentrations were converted to daily viral loads using wastewater flow
          rates, and normalized by the size of the population served by the treatment
          plant. To characterise the contributions of different sources of variation,
          we applied a Bayesian modelling framework, incorporating fixed effects and 
         spatio–temporal random effects to separate the contributions of laboratory
          protocols, demographic factors, and geographic structure to the observed va
         riability in SARS-CoV-2 viral loads. Our analysis revealed that laboratory-s
         pecific biases were substantial, and that local demographic characteristics 
         (particularly the age structure of the catchment population) also influenced
          viral load estimates. Adjusting for these sources of bias improved the reli
         ability of interpretations based on viral loads, as indicated by an increase
         d correlation with regional COVID-19 hospitalization data (from 0.55 for raw
          data to 0.77 for adjusted temporal trends). Dynamic time warping clustering
          of the adjusted temporal trends uncovered distinct geographic clusters, hig
         hlighting persistent spatial structures that evolved over successive epidemi
         c phases.<br /><em>Concl...' (2682 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'10.1371/journal.pwat.0000453' (28 chars)
      uid => protected35788 (integer)
      _localizedUid => protected35788 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected35788 (integer)modified
      pid => protected124 (integer)
   1 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=35350, pid=124)
      originalId => protected35350 (integer)
      authors => protected'Pitton,&nbsp;M.; McLeod,&nbsp;R.&nbsp;E.; Caduff,&nbsp;L.; Dauletova,&nbsp;A
         .; de Korne-Elenbaas,&nbsp;J.; Gan,&nbsp;C.; Hablützel,&nbsp;C.; Holschneid
         er,&nbsp;A.; Kang,&nbsp;S.; Loustalot,&nbsp;G.; Schmidhalter,&nbsp;P.; Schne
         ider,&nbsp;L.; Wettlauffer,&nbsp;A.; Yordanova,&nbsp;D.; Julian,&nbsp;T.&nbs
         p;R.; Ort,&nbsp;C.' (322 chars)
      title => protected'A six-plex digital PCR assay for monitoring respiratory viruses in wastewate
         r' (77 chars)
      journal => protected'Nature Water' (12 chars)
      year => protected2025 (integer)
      volume => protected3 (integer)
      issue => protected'' (0 chars)
      startpage => protected'1174' (4 chars)
      otherpage => protected'1186' (4 chars)
      categories => protected'' (0 chars)
      description => protected'Wastewater surveillance can track trends in multiple pathogens simultaneousl
         y by leveraging efficient laboratory processing. In Switzerland, wastewater 
         surveillance of four respiratory pathogens is conducted at 14 locations repr
         esenting 2.3 million people. Trends in respiratory diseases are tracked usin
         g a six-plex digital PCR assay targeting influenza A, influenza B, respirato
         ry syncytial virus and SARS-CoV-2 N1 and N2 regions and murine hepatitis vir
         us for recovery efficiency control. Wastewater data were integrated with dis
         ease data from two reporting systems, and comparisons from July 2023 to July
          2024 showed strong agreement for most targets. Lower correspondence for inf
         luenza B highlighted challenges in tracking disease dynamics during seasons 
         without pronounced outbreaks. Wastewater monitoring further revealed that ta
         rgeting N1 or N2 led to divergent estimates of SARS-CoV-2 loads, highlightin
         g the impact of mutations in assay target regions. The study emphasizes the 
         importance of an integrated wastewater monitoring programme as a complementa
         ry tool for public health surveillance, demonstrating clear concordance with
          clinical data.' (1155 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'10.1038/s44221-025-00503-x' (26 chars)
      uid => protected35350 (integer)
      _localizedUid => protected35350 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected35350 (integer)modified
      pid => protected124 (integer)
   2 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=34703, pid=124)
      originalId => protected34703 (integer)
      authors => protected'Baumgartner,&nbsp;S.; Salvisberg,&nbsp;M.; Schmidhalter,&nbsp;P.; Julian,&nb
         sp;T.&nbsp;R.; Ort,&nbsp;C.; Singer,&nbsp;H.' (120 chars)
      title => protected'Insights into respiratory illness at the population level through parallel a
         nalysis of pharmaceutical and viral markers in wastewater' (133 chars)
      journal => protected'Nature Water' (12 chars)
      year => protected2025 (integer)
      volume => protected3 (integer)
      issue => protected'' (0 chars)
      startpage => protected'580' (3 chars)
      otherpage => protected'589' (3 chars)
      categories => protected'' (0 chars)
      description => protected'Wastewater as a medium contains information on both circulating pathogens an
         d drug consumption at the population level. This study combines tracking of 
         respiratory viruses and quantification of pharmaceuticals as untargeted indi
         cators of symptoms related to acute respiratory infections and influenza-lik
         e illnesses such as coughing, fever and pain. From January 2021 to June 2024
         , raw wastewater samples from ten locations covering 23% of the Swiss popula
         tion were analysed. This encompassed 15 pharmaceuticals and four priority re
         spiratory viruses including severe acute respiratory syndrome coronavirus vi
         rus-2 (SARS-CoV-2), respiratory syncytial virus (RSV), influenza A and influ
         enza B viruses. The pharmaceutical compounds dextromethorphan, pheniramine, 
         clarithromycin, acetaminophen and codeine showed a strong correlation with r
         espiratory virus loads in wastewater. This enabled the estimation of pathoge
         n-specific and cumulative symptom treatment in the population. In 2021 and 2
         024, notable increases in pharmaceutical loads without corresponding increas
         es in viral loads signalled high community symptoms linked to unsurveilled p
         athogens. This study demonstrates that pharmaceutical surveillance can infor
         m respiratory disease burden and highlights the value of integrated surveill
         ance for assessing emerging public health threats beyond those routinely mon
         itored.' (1375 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'10.1038/s44221-025-00437-4' (26 chars)
      uid => protected34703 (integer)
      _localizedUid => protected34703 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected34703 (integer)modified
      pid => protected124 (integer)
   3 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=35242, pid=124)
      originalId => protected35242 (integer)
      authors => protected'Baumgartner,&nbsp;S.; Ceppi,&nbsp;E.; Longrée,&nbsp;P.; Salvisberg,&nbsp;M.
         ; Singer,&nbsp;H.; Ort,&nbsp;C.' (107 chars)
      title => protected'Intraday trends of chemical biomarkers in wastewater monitored through autom
         ated real-time surveillance' (103 chars)
      journal => protected'Water Research' (14 chars)
      year => protected2025 (integer)
      volume => protected287 (integer)
      issue => protected'' (0 chars)
      startpage => protected'124337 (12 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'illicit drugs; online monitoring; population markers; signaling data; wastew
         ater-based surveillance' (99 chars)
      description => protected'Wastewater-based epidemiology is an established approach for monitoring popu
         lation-level illicit drug use. Standard methods rely on 24-hour composite sa
         mples collected from treatment plant influent. While suitable for assessing 
         long-term consumption patterns, composite samples obscure diurnal informatio
         n on daily load dynamics - valuable for understanding substance sources and 
         normalization strategies. Currently, the required, high manual effort limits
          the feasibility of diurnal monitoring applications.<br />In this study, we 
         deployed a transportable, automated liquid chromatography-mass spectrometry 
         system (MS<sup>2</sup>Field) at the influent of the wastewater treatment pla
         nt in Zurich, Switzerland (serving 471,000 people). Over an eight-week perio
         d from July to September 2023 - including school holidays and two major publ
         ic events - samples were analyzed every 36 min (1909 total). Loads of five e
         xogenous population biomarkers, six common illicit drug markers, and the coc
         aine adulterant levamisole were quantified. In parallel, hourly mobile phone
          data were used to estimate <em>de facto</em> population size and demographi
         c composition in the catchment.<br />Automated analysis enabled the detectio
         n of consistent, weekday-specific intraday patterns for individual substance
         s. Population biomarkers exhibited distinct temporal profiles: paraxanthine 
         remained stable during working hours, while candesartan declined even at pea
         k population times - highlighting implications for normalization, particular
         ly with day visitors. Mobile phone-based occupancy data aligned with general
          trends but diverged during large events, likely due to mobile toilet use, w
         hereas chemical markers directly reflected the contributing population.<br /
         >During the special events, MDMA, ketamine, and amphetamine loads rose with 
         distinct profiles - one event showing a gradual overnight rise, the other a 
         morning peak. Daily MDMA loads were 20-fold higher during one event than typ
         ical weekends. Ketamine ...' (2520 chars)
      serialnumber => protected'0043-1354' (9 chars)
      doi => protected'10.1016/j.watres.2025.124337' (28 chars)
      uid => protected35242 (integer)
      _localizedUid => protected35242 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected35242 (integer)modified
      pid => protected124 (integer)