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Castro Blanco E, Dalmau Llorca MR, Aguilar Martín C, Carrasco-Querol N, Gonçalves AQ, Hernández Rojas Z, Coma E, Fernández-Sáez J. A Predictive Model of the Start of Annual Influenza Epidemics. Microorganisms 2024; 12:1257. [PMID: 39065025 PMCID: PMC11278734 DOI: 10.3390/microorganisms12071257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 06/14/2024] [Accepted: 06/17/2024] [Indexed: 07/28/2024] Open
Abstract
Influenza is a respiratory disease that causes annual epidemics during cold seasons. These epidemics increase pressure on healthcare systems, sometimes provoking their collapse. For this reason, a tool is needed to predict when an influenza epidemic will occur so that the healthcare system has time to prepare for it. This study therefore aims to develop a statistical model capable of predicting the onset of influenza epidemics in Catalonia, Spain. Influenza seasons from 2011 to 2017 were used for model training, and those from 2017 to 2018 were used for validation. Logistic regression, Support Vector Machine, and Random Forest models were used to predict the onset of the influenza epidemic. The logistic regression model was able to predict the start of influenza epidemics at least one week in advance, based on clinical diagnosis rates of various respiratory diseases and meteorological variables. This model achieved the best punctual estimates for two of three performance metrics. The most important variables in the model were the principal components of bronchiolitis rates and mean temperature. The onset of influenza epidemics can be predicted from clinical diagnosis rates of various respiratory diseases and meteorological variables. Future research should determine whether predictive models play a key role in preventing influenza.
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Affiliation(s)
- Elisabet Castro Blanco
- Primary Care Intervention Evaluation Research Group (GAVINA Research Group), IDIAPJGol Terres de l’Ebre, 43500 Tortosa, Spain; (E.C.B.); (N.C.-Q.); (A.Q.G.); (Z.H.R.); (J.F.-S.)
- Campus Terres de l’Ebre, Universitat Rovira i Virgili, 43500 Tortosa, Spain
- Terres de l’Ebre Research Support Unit, Foundation University Institute for Primary Health Care Research Jordi Gol i Gurina (IDIAPJGol), 43500 Tortosa, Spain
| | - Maria Rosa Dalmau Llorca
- Primary Care Intervention Evaluation Research Group (GAVINA Research Group), IDIAPJGol Terres de l’Ebre, 43500 Tortosa, Spain; (E.C.B.); (N.C.-Q.); (A.Q.G.); (Z.H.R.); (J.F.-S.)
- Campus Terres de l’Ebre, Universitat Rovira i Virgili, 43500 Tortosa, Spain
- Servei d’Atenció Primària Terres de l’Ebre, Institut Català de la Salut, 43500 Tortosa, Spain
| | - Carina Aguilar Martín
- Primary Care Intervention Evaluation Research Group (GAVINA Research Group), IDIAPJGol Terres de l’Ebre, 43500 Tortosa, Spain; (E.C.B.); (N.C.-Q.); (A.Q.G.); (Z.H.R.); (J.F.-S.)
- Terres de l’Ebre Research Support Unit, Foundation University Institute for Primary Health Care Research Jordi Gol i Gurina (IDIAPJGol), 43500 Tortosa, Spain
- Unitat d’Avaluació, Direcció d’Atenció Primària Terres de l’Ebre, Institut Català de la Salut, 43500 Tortosa, Spain
| | - Noèlia Carrasco-Querol
- Primary Care Intervention Evaluation Research Group (GAVINA Research Group), IDIAPJGol Terres de l’Ebre, 43500 Tortosa, Spain; (E.C.B.); (N.C.-Q.); (A.Q.G.); (Z.H.R.); (J.F.-S.)
- Terres de l’Ebre Research Support Unit, Foundation University Institute for Primary Health Care Research Jordi Gol i Gurina (IDIAPJGol), 43500 Tortosa, Spain
| | - Alessandra Queiroga Gonçalves
- Primary Care Intervention Evaluation Research Group (GAVINA Research Group), IDIAPJGol Terres de l’Ebre, 43500 Tortosa, Spain; (E.C.B.); (N.C.-Q.); (A.Q.G.); (Z.H.R.); (J.F.-S.)
- Terres de l’Ebre Research Support Unit, Foundation University Institute for Primary Health Care Research Jordi Gol i Gurina (IDIAPJGol), 43500 Tortosa, Spain
| | - Zojaina Hernández Rojas
- Primary Care Intervention Evaluation Research Group (GAVINA Research Group), IDIAPJGol Terres de l’Ebre, 43500 Tortosa, Spain; (E.C.B.); (N.C.-Q.); (A.Q.G.); (Z.H.R.); (J.F.-S.)
- Servei d’Atenció Primària Terres de l’Ebre, Institut Català de la Salut, 43500 Tortosa, Spain
| | - Ermengol Coma
- Primary Healthcare Information Systems, Health Institute of Catalonia, 08007 Catalonia, Spain;
| | - José Fernández-Sáez
- Primary Care Intervention Evaluation Research Group (GAVINA Research Group), IDIAPJGol Terres de l’Ebre, 43500 Tortosa, Spain; (E.C.B.); (N.C.-Q.); (A.Q.G.); (Z.H.R.); (J.F.-S.)
- Terres de l’Ebre Research Support Unit, Foundation University Institute for Primary Health Care Research Jordi Gol i Gurina (IDIAPJGol), 43500 Tortosa, Spain
- Servei d’Atenció Primària Terres de l’Ebre, Institut Català de la Salut, 43500 Tortosa, Spain
- Unitat de Recerca, Gerència Territorial Terres de l’Ebre, Institut Català de la Salut, 43500 Tortosa, Spain
- Unitat Docent de Medicina de Familia i Comunitària, Tortosa-Terres de l’Ebre, Institut Català de la Salut, 43500 Tortosa, Spain
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Lehto KM, Länsivaara A, Hyder R, Luomala O, Lipponen A, Hokajärvi AM, Heikinheimo A, Pitkänen T, Oikarinen S. Wastewater-based surveillance is an efficient monitoring tool for tracking influenza A in the community. WATER RESEARCH 2024; 257:121650. [PMID: 38692254 DOI: 10.1016/j.watres.2024.121650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Around the world, influenza A virus has caused severe pandemics, and the risk of future pandemics remains high. Currently, influenza A virus surveillance is based on the clinical diagnosis and reporting of disease cases. In this study, we apply wastewater-based surveillance to monitor the amount of the influenza A virus RNA at the population level. We report the influenza A virus RNA levels in 10 wastewater treatment plant catchment areas covering 40 % of the Finnish population. Altogether, 251 monthly composite influent wastewater samples (collected between February 2021 and February 2023) were analysed from supernatant fraction using influenza A virus specific RT-qPCR method. During the study period, an influenza A virus epidemic occurred in three waves in Finland. This study shows that the influenza A virus RNA can be detected from the supernatant fraction of 24 h composite influent wastewater samples. The influenza A virus RNA gene copy number in wastewater correlated with the number of confirmed disease cases in the Finnish National Infectious Diseases Register. The median Kendall's τ correlation strength was 0.636 (min= 0.486 and max=0.804) and it was statistically significant in all 10 WTTPs. Wastewater-based surveillance of the influenza A virus RNA is an independent from individual testing method and cost-efficiently reflects the circulation of the virus in the entire population. Thus, wastewater monitoring complements the available, but often too sparse, information from individual testing and improves health care and public health preparedness for influenza A virus pandemics.
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Affiliation(s)
- Kirsi-Maarit Lehto
- Tampere University, Faculty of Medicine and Health Technology, Arvo Ylpön katu 34, Tampere 33520, Finland
| | - Annika Länsivaara
- Tampere University, Faculty of Medicine and Health Technology, Arvo Ylpön katu 34, Tampere 33520, Finland
| | - Rafiqul Hyder
- Tampere University, Faculty of Medicine and Health Technology, Arvo Ylpön katu 34, Tampere 33520, Finland
| | - Oskari Luomala
- Finnish Institute for Health and Welfare, THL, Department of Health Security, Neulaniementie 4, Kuopio 70210, Finland
| | - Anssi Lipponen
- Finnish Institute for Health and Welfare, THL, Department of Health Security, Neulaniementie 4, Kuopio 70210, Finland
| | - Anna-Maria Hokajärvi
- Finnish Institute for Health and Welfare, THL, Department of Health Security, Neulaniementie 4, Kuopio 70210, Finland
| | - Annamari Heikinheimo
- Department of Food Hygiene and Environmental Health, Faculty of Veterinary Medicine, University of Helsinki, Agnes Sjöbergin katu 2, FI00014, Finland; Finnish Food Authority, Ruokavirasto, Alvar Aallon katu 5, Seinäjoki 60100, Finland
| | - Tarja Pitkänen
- Finnish Institute for Health and Welfare, THL, Department of Health Security, Neulaniementie 4, Kuopio 70210, Finland; Department of Food Hygiene and Environmental Health, Faculty of Veterinary Medicine, University of Helsinki, Agnes Sjöbergin katu 2, FI00014, Finland
| | - Sami Oikarinen
- Tampere University, Faculty of Medicine and Health Technology, Arvo Ylpön katu 34, Tampere 33520, Finland.
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Raina MacIntyre C, Lim S, Gurdasani D, Miranda M, Metcalf D, Quigley A, Hutchinson D, Burr A, Heslop DJ. Early detection of emerging infectious diseases - implications for vaccine development. Vaccine 2024; 42:1826-1830. [PMID: 37271702 DOI: 10.1016/j.vaccine.2023.05.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/04/2023] [Accepted: 05/26/2023] [Indexed: 06/06/2023]
Abstract
Vast quantities of open-source data from news reports, social media and other sources can be harnessed using artificial intelligence and machine learning, and utilised to generate valid early warning signals of emerging epidemics. Early warning signals from open-source data are not a replacement for traditional, validated disease surveillance, but provide a trigger for earlier investigation and diagnostics. This may yield earlier pathogen characterisation and genomic data, which can enable earlier vaccine development or deployment of vaccines. Early warning also provides a more feasible prospect of stamping out epidemics before they spread. There are several of such systems currently, but they are not used widely in public health practice, and only some are publicly available. Routine and widespread use of open-source intelligence, as well as training and capacity building in digital surveillance, will improve pandemic preparedness and early response capability.
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Affiliation(s)
- C Raina MacIntyre
- The Biosecurity Program, Kirby Institute, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia; College of Health Solutions and Watts College of Public Service and Community Services, Arizona State University, United States
| | - Samsung Lim
- The Biosecurity Program, Kirby Institute, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia
| | - Deepti Gurdasani
- The Biosecurity Program, Kirby Institute, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia
| | - Miguel Miranda
- The Biosecurity Program, Kirby Institute, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia
| | - David Metcalf
- The Biosecurity Program, Kirby Institute, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia
| | - Ashley Quigley
- The Biosecurity Program, Kirby Institute, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia
| | - Danielle Hutchinson
- The Biosecurity Program, Kirby Institute, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia.
| | - Allan Burr
- The Biosecurity Program, Kirby Institute, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia
| | - David J Heslop
- The School of Population Health, Faculty of Medicine and Health Sciences, University of New South Wales, New South Wales, Australia
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