Machine learning mathematical models for incidence estimation during pandemics DOI Creative Commons
Oscar Fajardo-Fontiveros, Mattia Mattei, Giulio Burgio

и другие.

PLoS Computational Biology, Год журнала: 2024, Номер 20(12), С. e1012687 - e1012687

Опубликована: Дек. 23, 2024

Accurate estimates of the incidence infectious diseases are key for control epidemics. However, healthcare systems often unable to test population exhaustively, especially when asymptomatic and paucisymptomatic cases widespread; this leads significant systematic under-reporting real incidence. Here, we propose a machine learning approach estimate pandemic in real-time, using reported overall rate. In particular, use Bayesian symbolic regression automatically learn closed-form mathematical models that most parsimoniously describe We develop validate our COVID-19 values nine different countries, confirming their ability accurately predict daily Remarkably, despite differences epidemic trajectories dynamics across find single model all countries offers more parsimonious description is predictive actual compared separate each country. Our results show potential real-time models, providing valuable tool public health decision-makers.

Язык: Английский

State-space modelling using wastewater virus and epidemiological data to estimate reported COVID-19 cases and the potential infection numbers DOI
Syun-suke Kadoya, Yubing Li,

Yilei Wang

и другие.

Journal of The Royal Society Interface, Год журнала: 2025, Номер 22(222)

Опубликована: Янв. 1, 2025

The current situation of COVID-19 measures makes it difficult to accurately assess the prevalence SARS-CoV-2 due a decrease in reporting rates, leading missed initial transmission events and subsequent outbreaks. There is growing recognition that wastewater virus data assist estimating potential infections, including asymptomatic unreported infections. Understanding hidden behind reported cases critical for decision-making when choosing appropriate social intervention measures. However, models implicitly assume homogeneity human behaviour, such as shedding patterns within population, making challenging predict emergence new variants variant-specific or parameters. This can result predictions with considerable uncertainty. In this study, we established state-space model based on viral load both infection numbers. Our using showed high goodness-of-fit case numbers despite dataset waves two distinct variants. Furthermore, successfully provided estimates infection, reflecting superspreading nature transmission. study supports notion surveillance modelling have effectively

Язык: Английский

Процитировано

2

Urban wastewater-based epidemiology for multi-viral pathogen surveillance in the Valencian region, Spain DOI Creative Commons
Inés Girón‐Guzmán, Enric Cuevas‐Ferrando, Regino Barranquero

и другие.

Water Research, Год журнала: 2024, Номер 255, С. 121463 - 121463

Опубликована: Март 16, 2024

Wastewater-based epidemiology (WBE) has lately arised as a promising tool for monitoring and tracking viral pathogens in communities. In this study, we analysed WBE's role multi-pathogen surveillance strategy to detect the presence of several illness causative agents. Thus, an epidemiological study was conducted from October 2021 February 2023 estimate weekly levels Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Syncytial virus (RSV), Influenza A (IAV) influent wastewater samples (n = 69). parallel, one-year (October 2022) performed assess pathogenic human enteric viruses. Besides, proposed fecal contamination indicators crAssphage Pepper mild mottle (PMMoV) also assessed, along with plaque counting somatic coliphages. Genetic material rotavirus (RV), astrovirus (HAStV), norovirus genogroup I (GI) GII found almost all samples, while hepatitis E viruses (HAV HEV) only tested positive 3.77 % 22.64 respectively. No seasonal patterns were overall viruses, although RVs had peak prevalence winter months. All SARS-CoV-2 RNA, mean concentration 5.43 log genome copies per liter (log GC/L). The circulating variants concern (VOCs) by both duplex RT-qPCR next generation sequencing (NGS). Both techniques reliably showed how dominant VOC transitioned Delta Omicron during two weeks Spain December 2021. RSV IAV peaked months concentrations 6.40 4.10 GC/L, Moreover, three selected respiratory strongly correlated reported clinical data when normalised physico-chemical parameters presented weaker correlations normalising sewage or coliphages titers. Finally, predictive models generated each virus, confirming high reliability on WBE early-warning system communities system. Overall, presents optimal reflecting circulation diseases trends within area, its value stands out due public health interest.

Язык: Английский

Процитировано

10

Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks DOI Creative Commons
Tin Phan, Samantha Brozak, Bruce Pell

и другие.

Water Research, Год журнала: 2023, Номер 243, С. 120372 - 120372

Опубликована: Июль 16, 2023

Язык: Английский

Процитировано

8

Estimating the effective reproduction number of COVID-19 from population-wide wastewater data: An application in Kagawa, Japan DOI Creative Commons
Yuta Okada, Hiroshi Nishiura

Infectious Disease Modelling, Год журнала: 2024, Номер 9(3), С. 645 - 656

Опубликована: Апрель 3, 2024

Although epidemiological surveillance of COVID-19 has been gradually downgraded globally, the transmission continues. It is critical to quantify dynamics using multiple datasets including wastewater virus concentration data. Herein, we propose a comprehensive method for estimating effective reproduction number The data, which were collected twice week, analyzed daily incidence data obtained from Takamatsu, Japan between January 2022 and September 2022. We estimated shedding load distribution (SLD) as function time since date infection, model employing delay distribution, assumed follow gamma multiplied by scaling factor. also examined models that accounted temporal smoothness viral measurement smoothed patterns was best fit (WAIC = 2795.8), yielded mean SLD 3.46 days (95% CrI: 3.01–3.95 days). Using this SLD, reconstructed incidence, enabled computation number. posterior draws parameters directly, or prior subsequent analyses, first used concentrations in wastewater, well infection counts infection. In approach, incorporated weekly reported case proxy reporting. Both approaches estimations epidemic curve twice-weekly Adding count reduced uncertainty conclude are still valuable source information inferring COVID-19, inferential performance enhanced when those combined with

Язык: Английский

Процитировано

2

Wastewater Surveillance to Confirm Differences in Influenza A Infection between Michigan, USA, and Ontario, Canada, September 2022–March 2023 DOI Creative Commons
Ryland Corchis-Scott, Mackenzie Beach, Qiudi Geng

и другие.

Emerging infectious diseases, Год журнала: 2024, Номер 30(8)

Опубликована: Июль 30, 2024

Wastewater surveillance is an effective way to track the prevalence of infectious agents within a community and, potentially, spread pathogens between jurisdictions. We conducted retrospective wastewater study 2022-23 influenza season in 2 communities, Detroit, Michigan, USA, and Windsor-Essex, Ontario, Canada, that form North America's largest cross-border conurbation. observed positive relationship influenza-related hospitalizations A virus (IAV) signal Windsor-Essex (ρ = 0.785; p<0.001) association Michigan IAV for Detroit 0.769; p<0.001). Time-lagged cross correlation qualitative examination monitored sewersheds showed peak was delayed behind by 3 weeks. reflects regional differences infection dynamics which may be influenced many factors, including timing vaccine administration

Язык: Английский

Процитировано

1

Watching the guards: A data-driven method to trigger warnings in national wastewater surveillance networks DOI Creative Commons

Lluís Bosch,

Josep Pueyo‐Ros, Marc Comas‐Cufí

и другие.

Journal of Water and Health, Год журнала: 2024, Номер 22(7), С. 1209 - 1221

Опубликована: Июнь 12, 2024

ABSTRACT Surveillance networks have been established in many countries worldwide to monitor SARS-CoV-2 sewage and estimate the communal prevalence of COVID-19 cases. Despite their popularity, gaining a rapid understanding how infectious diseases spread across territory covered by network is difficult because factors involved. To improve detection warning signals within territory, we propose apply principal component analysis (PCA) screen time-series data generated from wastewater treatment plants (WWTPs) under surveillance. Our allows us identify single WWTPs deviating normal behavior as well deviations cluster (indicative an intermunicipal outbreak). approach illustrated through dataset Catalan Network Sewage (SARSAIGUA). Using 10 components, captured 78.6% variance original 51 variables (WWTPs). identified exceedance Q-statistic threshold evidence anomalous performance WWTP, T2-statistic sign outbreak. provides comprehensive picture pandemic, enabling decision-makers make informed decisions better manage future pandemics.

Язык: Английский

Процитировано

0

Effect of SARS-CoV-2 shedding rate distribution of individuals during their disease days on the estimation of the number of infected people. Application of wastewater-based epidemiology to the city of Thessaloniki, Greece DOI Creative Commons
Margaritis Kostoglou, Maria Petala, Thodoris D. Karapantsios

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175724 - 175724

Опубликована: Авг. 23, 2024

During the COVID-19 pandemic, wastewater-based epidemiology has proved to be an important tool for monitoring spread of a disease in population. Indeed, wastewater surveillance was successfully used as complementary approach support public health schemes and decision-making policies. An essential feature estimation transmission using data is distribution viral shedding rate individuals their personal human wastes function days infection. Several candidate shapes this have been proposed literature SARS-CoV-2. The purpose present work explore examine significance on analyzing SARS-CoV-2 data. For purpose, simple model employed applying medical city Thessaloniki during period Omicron variant domination 2022. are normalized with respect total virus then basic features investigated. Detailed analysis reveals that main parameter determining results difference between day maximum infection reporting. Since latter not part shape, major affecting number infected people initial day. On contrary, duration (total days) well exact shape by far less important. incorporation such models conventional epidemiological - based recorded data- may improve predictions outbreaks.

Язык: Английский

Процитировано

0

Under-Reporting of SARS-CoV-2 Infections in 27 Countries, 2020–2022 DOI
Mustapha M. Mustapha, Kanae Togo, Hannah R. Volkman

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Wastewater-based effective reproduction number and prediction under the absence of shedding information DOI Creative Commons
Hiroki Ando, Kelly A. Reynolds

Environment International, Год журнала: 2024, Номер 194, С. 109128 - 109128

Опубликована: Ноя. 14, 2024

Estimating effective reproduction number (R

Язык: Английский

Процитировано

0

Machine learning mathematical models for incidence estimation during pandemics DOI Creative Commons
Oscar Fajardo-Fontiveros, Mattia Mattei, Giulio Burgio

и другие.

PLoS Computational Biology, Год журнала: 2024, Номер 20(12), С. e1012687 - e1012687

Опубликована: Дек. 23, 2024

Accurate estimates of the incidence infectious diseases are key for control epidemics. However, healthcare systems often unable to test population exhaustively, especially when asymptomatic and paucisymptomatic cases widespread; this leads significant systematic under-reporting real incidence. Here, we propose a machine learning approach estimate pandemic in real-time, using reported overall rate. In particular, use Bayesian symbolic regression automatically learn closed-form mathematical models that most parsimoniously describe We develop validate our COVID-19 values nine different countries, confirming their ability accurately predict daily Remarkably, despite differences epidemic trajectories dynamics across find single model all countries offers more parsimonious description is predictive actual compared separate each country. Our results show potential real-time models, providing valuable tool public health decision-makers.

Язык: Английский

Процитировано

0