Evidence map›Paper›PMID 37295226›Full record

ArticleWater research2023

Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants.

Péter Polcz, Kálmán Tornai, János Juhász, György Cserey, György Surján, Tamás Pándics, Eszter Róka, Márta Vargha, István Z Reguly, Attila Csikász-Nagy and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Water research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 13 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 1 country.

Péter PolczNational Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary. Electronic address: polcz.peter@itk.ppke.hu.
Kálmán TornaiNational Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary.
János JuhászNational Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary; Institute of Medical Microbiology, Semmelweis University, Üllői út 26, Budapest, H-1085, Hungary.
György CsereyNational Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary.
György SurjánDepartment of Public Health Laboratory, National Public Health Centre, Albert Flórián út 2-6, Budapest, H-1097, Hungary; Department of Digital Health Sciences, Semmelweis University, Üllői út 26, Budapest, H-1085, Hungary.
Tamás PándicsDepartment of Public Health Laboratory, National Public Health Centre, Albert Flórián út 2-6, Budapest, H-1097, Hungary; Department of Public Health Sciences, Faculty of Health Sciences, Semmelweis University, Vas utca 17, Budapest, H-1088, Hungary.
Eszter RókaDepartment of Public Health Laboratory, National Public Health Centre, Albert Flórián út 2-6, Budapest, H-1097, Hungary.
Márta VarghaDepartment of Public Health Laboratory, National Public Health Centre, Albert Flórián út 2-6, Budapest, H-1097, Hungary.
István Z RegulyNational Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary.
Attila Csikász-NagyNational Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary.
Sándor PongorNational Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary.
Gábor SzederkényiNational Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary.
Pázmány Péter Catholic University · HUSemmelweis University · HUNational Public Health and Medical Officer Service · HU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

(MOTIVATION): Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance. (METHOD): In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic. (RESULTS): Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022. (APPLICABILITY): The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

Indexed as

COVID-19WastewaterCOVID-19 TestingDisease OutbreaksHumansHungaryImmune EvasionPandemicsWastewaterDynamical systemEpidemic modelOptimizationParameter estimationWastewater-based epidemiology

Identifiers

PMID37295226
PMCPMC10265948
OpenAlexW4378191474

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.