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May F, Ginige S, Firman E, Li YS, Soonarane YK, Smoll N, Hunter I, Pery B, Macfarlane B, Bladen T, Allen T, Green T, Walker J, Slinko V, Stickley M, Khandaker G, Anuradha S, Wattiaux A. Estimating the incidence of COVID-19, influenza and respiratory syncytial virus infection in three regions of Queensland, Australia, winter 2022: findings from a novel longitudinal testing-based sentinel surveillance programme. BMJ Open 2024; 14:e081793. [PMID: 38653507 PMCID: PMC11043701 DOI: 10.1136/bmjopen-2023-081793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
OBJECTIVE The 2022 Australian winter was the first time that COVID-19, influenza and respiratory syncytial virus (RSV) were circulating in the population together, after two winters of physical distancing, quarantine and borders closed to international travellers. We developed a novel surveillance system to estimate the incidence of COVID-19, influenza and RSV in three regions of Queensland, Australia. DESIGN We implemented a longitudinal testing-based sentinel surveillance programme. Participants were provided with self-collection nasal swabs to be dropped off at a safe location at their workplace each week. Swabs were tested for SARS-CoV-2 by PCR. Symptomatic participants attended COVID-19 respiratory clinics to be tested by multiplex PCR for SARS-CoV-2, influenza A and B and RSV. Rapid antigen test (RAT) results reported by participants were included in the analysis. SETTING AND PARTICIPANTS Between 4 April 2022 and 3 October 2022, 578 adults were recruited via their workplace. Due to rolling recruitment, withdrawals and completion due to positive COVID-19 results, the maximum number enrolled in any week was 423 people. RESULTS A total of 4290 tests were included. Participation rates varied across the period ranging from 25.9% to 72.1% of enrolled participants. The total positivity of COVID-19 was 3.3%, with few influenza or RSV cases detected. Widespread use of RAT may have resulted in few symptomatic participants attending respiratory clinics. The weekly positivity rate of SARS-CoV-2 detected during the programme correlated with the incidence of notified cases in the corresponding communities. CONCLUSION This testing-based surveillance programme could estimate disease trends and be a useful tool in settings where testing is less common or accessible. Difficulties with recruitment meant the study was underpowered. The frontline sentinel nature of workplaces meant participants were not representative of the general population but were high-risk groups providing early warning of disease.
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Affiliation(s)
- Fiona May
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
| | - Shamila Ginige
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
| | - Elise Firman
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
| | - Yee Sum Li
- Metro South Public Health Unit, Metro South Hospital and Health Service, Woolloongabba, Queensland, Australia
| | - Yudish Kumar Soonarane
- Metro South Public Health Unit, Metro South Hospital and Health Service, Woolloongabba, Queensland, Australia
| | - Nicolas Smoll
- Central Queensland Public Health Unit, Central Queensland Hospital and Health Service, Rockhampton, Queensland, Australia
| | - Ian Hunter
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
| | - Brielle Pery
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
| | - Bonnie Macfarlane
- Metro South Public Health Unit, Metro South Hospital and Health Service, Woolloongabba, Queensland, Australia
| | - Tracy Bladen
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
| | - Terresa Allen
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
| | - Trevor Green
- Metro South Public Health Unit, Metro South Hospital and Health Service, Woolloongabba, Queensland, Australia
| | - Jacina Walker
- Central Queensland Public Health Unit, Central Queensland Hospital and Health Service, Rockhampton, Queensland, Australia
| | - Vicki Slinko
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
- School of Public Health, The University of Queensland, Herston, Queensland, Australia
| | - Mark Stickley
- Metro South Public Health Unit, Metro South Hospital and Health Service, Woolloongabba, Queensland, Australia
| | - Gulam Khandaker
- Central Queensland Public Health Unit, Central Queensland Hospital and Health Service, Rockhampton, Queensland, Australia
| | - Satyamurthy Anuradha
- Metro South Public Health Unit, Metro South Hospital and Health Service, Woolloongabba, Queensland, Australia
- School of Public Health, The University of Queensland, Herston, Queensland, Australia
| | - Andre Wattiaux
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
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Akano A, Sadauki AH, Adelabu AM, Malgwi A, Fagbola M, Ogunbode O, Usman A, Ameh C, Balogun MS, Ilori E, Badaru S, Adetunji A, Adebayo A, Mba N, Iniobong A, Eze E, Akerele I, Grema B, Sodipo O, Enemuo E, Ochu C, Ihekweazu C, Adetifa I. Epidemiology of influenza in Nigeria: A secondary analysis of the sentinel surveillance data in Nigeria from 2010 - 2020. J Infect Public Health 2024; 17:495-502. [PMID: 38290192 DOI: 10.1016/j.jiph.2023.12.021] [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: 06/26/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Influenza is a leading cause of morbidity and mortality globally. Little is known of the true burden and epidemiology of influenza in Africa. Nigeria has a sentinel surveillance system for influenza virus (IFV). This study seeks to describe the epidemiological characteristics of influenza cases in Nigeria through secondary data analysis of the sentinel surveillance data from 2010 to 2020. METHODOLOGY A retrospective secondary data analysis of data collected from patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) in the four Nigeria Influenza Sentinel Surveillance sites from January 2010 to December 2020. Data was cleaned and analyzed using Microsoft Excel and Epi info 7.2 for frequencies and proportions. The results of the analysis were summarized in tables and charts. RESULTS A total of 13,828 suspected cases of influenza were recorded at the sentinel sites during the study period. About 10.3% (1421/13,828) of these tested positive for IFV of which 1243 (87.5%) were ILI patients, 175 (12.3%) SARI patients, and 3 (0.2%) novel H1N1 patients. Males accounted for 54.2% (770/1421) of the confirmed cases. The median age of confirmed cases was 3 years (range: <1month-97 years). Children 0-4 years accounted for 69.3% (985/1421) of all cases. The predominant subtypes were B lineage not determined (32.3%), A/H1N1 pdm09 (28.8%) and A/H3 (23.0%). There were periods of sustained transmission in most years with 2011 having the highest number of cases. Overall, there were more cases around January to March and August to November. Heart disease and chronic shortness of breath were the most common co-morbidities identified among confirmed cases. CONCLUSION Influenza remains a significant cause of respiratory illness, especially among children aged less than 4 years. Influenza cases occur all year round with irregular seasonality in Nigeria. Children less than 4 years and those with co-morbidities should be prioritized for vaccination. Vaccine composition in the country should take cognizance of the prevailing strains which are type B (lineage not determined), A/H1N1 pdm09 and A/H3.
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Affiliation(s)
- Adejoke Akano
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria; Nigeria Field Epidemiology and Laboratory Training Programme, Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria.
| | - Aisha Habib Sadauki
- Nigeria Field Epidemiology and Laboratory Training Programme, Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Adeyemi Mark Adelabu
- Nigeria Field Epidemiology and Laboratory Training Programme, Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Arhyel Malgwi
- Nigeria Field Epidemiology and Laboratory Training Programme, Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Motunrayo Fagbola
- Nigeria Field Epidemiology and Laboratory Training Programme, Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Oladipo Ogunbode
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | | | | | | | - Elsie Ilori
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Sikiru Badaru
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Adewusi Adetunji
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Adedeji Adebayo
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Nwando Mba
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Akanimo Iniobong
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Emmanuel Eze
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | | | | | | | - Emeka Enemuo
- Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra, Nigeria
| | - Chinwe Ochu
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Chikwe Ihekweazu
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Ifedayo Adetifa
- Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria
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Delecroix C, van Nes EH, van de Leemput IA, Rotbarth R, Scheffer M, ten Bosch Q. The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002253. [PMID: 37815958 PMCID: PMC10564242 DOI: 10.1371/journal.pgph.0002253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/12/2023] [Indexed: 10/12/2023]
Abstract
To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach.
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Affiliation(s)
- Clara Delecroix
- Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
- Quantitative Veterinary Epidemiology, Wageningen University, Wageningen, The Netherlands
| | - Egbert H. van Nes
- Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
| | | | - Ronny Rotbarth
- Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
| | - Marten Scheffer
- Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
| | - Quirine ten Bosch
- Quantitative Veterinary Epidemiology, Wageningen University, Wageningen, The Netherlands
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Vega-Alonso T, Lozano-Alonso JE, Ordax-Díez A. Comprehensive surveillance of acute respiratory infections during the COVID-19 pandemic: a methodological approach using sentinel networks, Castilla y León, Spain, January 2020 to May 2022. Euro Surveill 2023; 28:2200638. [PMID: 37227298 PMCID: PMC10283458 DOI: 10.2807/1560-7917.es.2023.28.21.2200638] [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: 08/05/2022] [Accepted: 02/14/2023] [Indexed: 05/26/2023] Open
Abstract
BackgroundSince 1996, epidemiological surveillance of acute respiratory infections (ARI) in Spain has been limited to seasonal influenza, respiratory syncytial virus (RSV) and potential pandemic viruses. The COVID-19 pandemic provides opportunities to adapt existing systems for extended surveillance to capture a broader range of ARI.AimTo describe how the Influenza Sentinel Surveillance System of Castilla y León, Spain was rapidly adapted in 2020 to comprehensive sentinel surveillance for ARI, including influenza and COVID-19.MethodsUsing principles and methods of the health sentinel network, we integrated electronic medical record data from 68 basic surveillance units, covering 2.6% of the regional population between January 2020 to May 2022. We tested sentinel and non-sentinel samples sent weekly to the laboratory network for SARS-CoV-2, influenza viruses and other respiratory pathogens. The moving epidemic method (MEM) was used to calculate epidemic thresholds.ResultsARI incidence was estimated at 18,942 cases per 100,000 in 2020/21 and 45,223 in 2021/22, with similar seasonal fold increases by type of respiratory disease. Incidence of influenza-like illness was negligible in 2020/21 but a 5-week epidemic was detected by MEM in 2021/22. Epidemic thresholds for ARI and COVID-19 were estimated at 459.4 and 191.3 cases per 100,000 population, respectively. More than 5,000 samples were tested against a panel of respiratory viruses in 2021/22.ConclusionExtracting data from electronic medical records reported by trained professionals, combined with a standardised microbiological information system, is a feasible and useful method to adapt influenza sentinel reports to comprehensive ARI surveillance in the post-COVID-19 era.
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Affiliation(s)
- Tomás Vega-Alonso
- Regional Public Health Directorate, Regional Health Ministry, Valladolid, Spain
| | | | - Ana Ordax-Díez
- Regional Public Health Directorate, Regional Health Ministry, Valladolid, Spain
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Kelly ME, Gharpure R, Shivji S, Matonya M, Moshi S, Mwafulango A, Mwalongo V, Mghamba J, Simba A, Balajee SA, Gatei W, Mponela M, Saguti G, Whistler T, Moremi N, Mmbaga V. Etiologies of influenza-like illness and severe acute respiratory infections in Tanzania, 2017-2019. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0000906. [PMID: 36962965 PMCID: PMC10021583 DOI: 10.1371/journal.pgph.0000906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 11/30/2022] [Indexed: 02/11/2023]
Abstract
In 2016, Tanzania expanded sentinel surveillance for influenza-like illness (ILI) and severe acute respiratory infection (SARI) to include testing for non-influenza respiratory viruses (NIRVs) and additional respiratory pathogens at 9 sentinel sites. During 2017-2019, respiratory specimens from 2730 cases underwent expanded testing: 2475 specimens (90.7%) were tested using a U.S. Centers for Disease Control and Prevention (CDC)-developed assay covering 7 NIRVs (respiratory syncytial virus [RSV], rhinovirus, adenovirus, human metapneumovirus, parainfluenza virus 1, 2, and 3) and influenza A and B viruses. Additionally, 255 specimens (9.3%) were tested using the Fast-Track Diagnostics Respiratory Pathogens 33 (FTD-33) kit which covered the mentioned viruses and additional viral, bacterial, and fungal pathogens. Influenza viruses were identified in 7.5% of all specimens; however, use of the CDC assay and FTD-33 kit increased the number of specimens with a pathogen identified to 61.8% and 91.5%, respectively. Among the 9 common viruses between the CDC assay and FTD-33 kit, the most identified pathogens were RSV (22.9%), rhinovirus (21.8%), and adenovirus (14.0%); multi-pathogen co-detections were common. Odds of hospitalization (SARI vs. ILI) varied by sex, age, geographic zone, year of diagnosis, and pathogen identified; hospitalized illnesses were most common among children under the age of 5 years. The greatest number of specimens were submitted for testing during December-April, coinciding with rainy seasons in Tanzania, and several viral pathogens demonstrated seasonal variation (RSV, human metapneumovirus, influenza A and B, and parainfluenza viruses). This study demonstrates that expanding an existing influenza platform to include additional respiratory pathogens can provide valuable insight into the etiology, incidence, severity, and geographic/temporal patterns of respiratory illness. Continued respiratory surveillance in Tanzania, and globally, can provide valuable data, particularly in the context of emerging respiratory pathogens such as SARS-CoV-2, and guide public health interventions to reduce the burden of respiratory illnesses.
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Affiliation(s)
| | - Radhika Gharpure
- U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Sabrina Shivji
- U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | | | | | | | | | | | - Azma Simba
- Ministry of Health, Dar es Salaam, Tanzania
| | - S. Arunmozhi Balajee
- U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Wangeci Gatei
- U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
- U.S. Centers for Disease Control and Prevention, Dar es Salaam, Tanzania
| | - Marcelina Mponela
- U.S. Centers for Disease Control and Prevention, Dar es Salaam, Tanzania
| | - Grace Saguti
- World Health Organization, Dar es Salaam, Tanzania
| | - Toni Whistler
- U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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Shedura VJ, Hussein AK, Nyanga SK, Kamori D, Mchau GJ. Evaluation of the influenza-like illness sentinel surveillance system: A national perspective in Tanzania from January to December 2019. PLoS One 2023; 18:e0283043. [PMID: 36940206 PMCID: PMC10027206 DOI: 10.1371/journal.pone.0283043] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 02/28/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND The World Health Organization (WHO) recommends periodic evaluations of influenza surveillance systems to identify areas for improvement and provide evidence of data reliability for policymaking. However, data on the performance of established influenza surveillance systems are limited in Africa, including Tanzania. We aimed to assess the usefulness of the Influenza surveillance system in Tanzania and to ascertain if the system meets its objectives, including; estimating the burden of disease caused by the Influenza virus in Tanzania and identifying any circulating viral strains with pandemic potential. METHODOLOGY From March to April 2021, we collected retrospective data through a review of the Tanzania National Influenza Surveillance System electronic forms for 2019. Furthermore, we interviewed the surveillance personnel about the system's description and operating procedures. Case definition (ILI-Influenza Like Illness and SARI-Severe Acute Respiratory Illness), results, and demographic characteristics of each patient were obtained from the Laboratory Information System (Disa*Lab) at Tanzania National Influenza Center. The United States Centers for disease control and prevention updated guidelines for evaluating public health surveillance systems were used to evaluate the system's attributes. Additionally, the system's performance indicators (including turnaround time) were obtained by evaluating Surveillance system attributes, each being scored on a scale of 1 to 5 (very poor to excellent performance). RESULTS A total of 1731 nasopharyngeal and oropharyngeal samples were collected from each suspected influenza case in 2019 from fourteen (14/14) sentinel sites of the influenza surveillance system in Tanzania. Laboratory-confirmed cases were 21.5% (373/1731) with a predictive value positive of 21.7%. The majority of patients (76.1%) tested positive for Influenza A. Thirty-seven percent of patients' results met the required turnaround time, and 40% of case-based forms were incompletely filled. Although the accuracy of the data was good (100%), the consistency of the data was below (77%) the established target of ≥ 95%. CONCLUSION The overall system performance was satisfactory in conforming with its objectives and generating accurate data, with an average performance of 100%. The system's complexity contributed to the reduced consistency of data from sentinel sites to the National Public Health Laboratory of Tanzania. Improvement in the use of the available data could be made to inform and promote preventive measures, especially among the most vulnerable population. Increasing sentinel sites would increase population coverage and the level of system representativeness.
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Affiliation(s)
- Vulstan James Shedura
- Muhimbili University of Health and Allied Sciences, School of Public Health and Social Sciences (SPHSS), Dar es Salaam, Tanzania
| | - Ally Kassim Hussein
- Tanzania Field Epidemiology and Laboratory Training Program (TFELTP), Dar es Salaam, Tanzania
| | | | - Doreen Kamori
- Muhimbili University of Health and Allied Sciences, School of Public Health and Social Sciences (SPHSS), Dar es Salaam, Tanzania
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Ginige S, Firman E, Li YS, Soonarane Y, Smoll N, May F, Hunter I, Pery B, Macfarlane B, Bladen T, Allen T, Lennon M, Walker J, Slinko V, Stickley M, Khandaker G, Anuradha S, Wattiaux A. Protocol for a winter sentinel surveillance program of notifiable respiratory viruses in Queensland. PLoS One 2022; 17:e0277895. [PMID: 36441699 PMCID: PMC9704554 DOI: 10.1371/journal.pone.0277895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/06/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND With the reduction in access to polymerase chain reaction (PCR) testing and changes in testing guidelines in Australia, a reduced number of people are seeking testing for coronavirus disease (COVID-19), limiting the opportunity to monitor disease transmission. Knowledge of community transmission of COVID-19 and other respiratory viruses is essential to better predict subsequent surges in cases during the pandemic to alert health services, protect vulnerable populations and enhance public health measures. We describe a methodology for a testing-based sentinel surveillance program to monitor disease in the community for early signal detection of SARS-CoV-2 and other respiratory viruses. METHODS/DESIGN A longitudinal active testing-based sentinel surveillance program for respiratory viruses (including SARS-CoV-2, influenza A, influenza B and Respiratory Syncytial Virus) will be implemented in some regions of Queensland. Adults will be eligible for enrolment if they are part of specific community groups at increased risk of exposure and have not had a COVID-19 infection in the last 13 weeks. Recruitment via workplaces will occur in-person, via email and through online advertisement. Asymptomatic participants will be tested via PCR for SARS-CoV-2 infection by weekly self-collected nasal swabs. In addition, symptomatic participants will be asked to seek SARS-CoV-2 and additional respiratory virus PCR testing at nominated COVID-19 testing sites. SARS-CoV-2 and respiratory virus prevalence data will be analysed weekly and at the end of the study period. DISCUSSION Once implemented, this surveillance program will determine the weekly prevalence of COVID-19 and other respiratory viruses in the broader community by testing a representative sample of adults, with an aim to detect early changes in the baseline positivity rate. This information is essential to define the epidemiology of SARS-CoV-2 in the community in near-real time to inform public health control measures and prepare health services and other stakeholders for a rise in service demand.
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Affiliation(s)
- Shamila Ginige
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
| | - Elise Firman
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
| | - Yee Sum Li
- Metro South Public Health Unit, Metro South Hospital and Health and Service, Brisbane, Australia
| | - Yudish Soonarane
- Metro South Public Health Unit, Metro South Hospital and Health and Service, Brisbane, Australia
| | - Nicolas Smoll
- Central Queensland Public Health Unit, Central Queensland Hospital and Health Service, Rockhampton, Australia
| | - Fiona May
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
| | - Ian Hunter
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
| | - Brielle Pery
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
| | - Bonnie Macfarlane
- Metro South Public Health Unit, Metro South Hospital and Health and Service, Brisbane, Australia
| | - Tracy Bladen
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
| | - Terresa Allen
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
| | - Melinda Lennon
- Metro South Public Health Unit, Metro South Hospital and Health and Service, Brisbane, Australia
| | - Jacina Walker
- Central Queensland Public Health Unit, Central Queensland Hospital and Health Service, Rockhampton, Australia
| | - Vicki Slinko
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
| | - Mark Stickley
- Metro South Public Health Unit, Metro South Hospital and Health and Service, Brisbane, Australia
| | - Gulam Khandaker
- Central Queensland Public Health Unit, Central Queensland Hospital and Health Service, Rockhampton, Australia
| | - Satyamurthy Anuradha
- Metro South Public Health Unit, Metro South Hospital and Health and Service, Brisbane, Australia
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
| | - Andre Wattiaux
- Gold Coast Public Health Unit, Gold Coast Hospital and Health Service, Cararra, Australia
- * E-mail:
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8
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Umeozuru CM, Usman AB, Olorukooba AA, Abdullahi IN, John DJ, Lawal LA, Uwazie CC, Balogun MS. Performance of COVID-19 case-based surveillance system in FCT, Nigeria, March 2020 –January 2021. PLoS One 2022; 17:e0264839. [PMID: 35421123 PMCID: PMC9009682 DOI: 10.1371/journal.pone.0264839] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 02/17/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction The emergence of novel SARS-CoV-2 has caused a pandemic of Coronavirus Disease 19 (COVID-19) which has spread exponentially worldwide. A robust surveillance system is essential for correct estimation of the disease burden and containment of the pandemic. We evaluated the performance of COVID-19 case-based surveillance system in FCT, Nigeria and assessed its key attributes. Methods We used a cross-sectional study design, comprising a survey, key informant interview, record review and secondary data analysis. A self-administered, semi-structured questionnaire was administered to key stakeholders to assess the attributes and process of operation of the surveillance system using CDC’s Updated Guidelines for Evaluation of Public Health Surveillance System 2001. Data collected alongside surveillance data from March 2020 to January 2021 were analyzed and summarized using descriptive statistics. Results Out of 69,338 suspected cases, 12,595 tested positive with RT-PCR with a positive predictive value (PPV) of 18%. Healthcare workers were identified as high-risk group with a prevalence of 23.5%. About 82% respondents perceived the system to be simple, 85.5% posited that the system was flexible and easily accommodates changes, 71.4% reported that the system was acceptable and expressed willingness to continue participation. Representativeness of the system was 93%, stability 40%, data quality 56.2% and timeliness 45.5%, estimated result turnaround time (TAT) was suboptimal. Conclusion The system was found to be useful, simple, flexible, sensitive, acceptable, with good representativeness but the stability, data quality and timeliness was poor. The system meets initial surveillance objectives but rapid expansion of sample collection and testing sites, improvement of TAT, sustainable funding, improvement of electronic database, continuous provision of logistics, supplies and additional trainings are needed to address identified weaknesses, optimize the system performance and meet increasing need of case detection in the wake of rapidly spreading pandemic. More risk-group persons should be tested to improve surveillance effectiveness.
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Affiliation(s)
- Chikodi Modesta Umeozuru
- Nigeria Field Epidemiology and Laboratory Training Program, Abuja, Nigeria
- African Field Epidemiology Network (AFENET), Abuja, Nigeria
- * E-mail:
| | | | | | - Idris Nasir Abdullahi
- Department of Medical Laboratory Science, Ahmadu Bello University, Zaria, Kaduna State, Nigeria
| | - Doris Japhet John
- Department of Public Health, Federal Capital Territory Administration, Abuja, Nigeria
| | - Lukman Ademola Lawal
- Department of Public Health, Federal Capital Territory Administration, Abuja, Nigeria
| | - Charles Chukwudi Uwazie
- Nigeria Field Epidemiology and Laboratory Training Program, Abuja, Nigeria
- African Field Epidemiology Network (AFENET), Abuja, Nigeria
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Abstract
Influenza is a common respiratory infection that causes considerable morbidity and mortality worldwide each year. In recent years, along with the improvement in computational resources, there have been a number of important developments in the science of influenza surveillance and forecasting. Influenza surveillance systems have been improved by synthesizing multiple sources of information. Influenza forecasting has developed into an active field, with annual challenges in the United States that have stimulated improved methodologies. Work continues on the optimal approaches to assimilating surveillance data and information on relevant driving factors to improve estimates of the current situation (nowcasting) and to forecast future dynamics.
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Affiliation(s)
- Sheikh Taslim Ali
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China;
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China;
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Hu X, Zhou W, Zhang L, Lv J, Yan B, Zhou Y, Hu W, Dong Y, Chen B, Liu M, Cao J, Xu F, Li L. Implementing sequencing-based surveillance in developing countries: findings from a pilot rollout for hepatitis A in China. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1119. [PMID: 34430560 PMCID: PMC8350710 DOI: 10.21037/atm-21-1193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 06/10/2021] [Indexed: 01/15/2023]
Abstract
Background The emergence of SARS-Cov2 variants has highlighted the need to implement sequencing-based surveillance in developing countries for early response to mutant viruses of concern. However, limited information on how to implement sequencing-based surveillance is available, and the feasibility and performance of this new type of surveillance are still in question. Methods To understand the challenges with the implementation and to promote sequencing-based surveillance, we reported findings from a pilot for hepatitis A (HepA) in five sentinel provinces in China as an example of sequencing-based surveillance implementation. The performance of the surveillance system was evaluated by indicators related to acceptability, data quality, simplicity, utility, and timeliness. We use a scale from 1 to 3 was used to provide a score for each aspect. Results During the pilot, 306 cases of HepA were reported, and 49.79% of samples were available for sequencing. Eleven genomic clusters were found, of which seven clusters were potentially related to a foodborne outbreak oyster based on identical viral sequence and epidemiologic investigations. The greatest strength of the system was its simplicity (Score: 2.63). The acceptability (Score: 2.0) and utility (Score: 2.33) were modest, but data quality (Score: 1.75) and timeliness (Score: 1.75) were the main challenges. Conclusions Overall, the system performed satisfactorily and proved to be useful for virological characterization of cases and early outbreak detection, with a great potential for scale-up. Further efforts are required to address financial and human resource constraints and inadequate support among physicians. Education should be given to health care professionals to improve the data quality. The establishment of decentralized surveillance networks can be an approach to improve timeliness for emerging infections.
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Affiliation(s)
- Xiaotong Hu
- The First Affiliated Hospital, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Medicine, Zhejiang University, Hangzhou, China
| | - Wenting Zhou
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Li Zhang
- Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Jingjing Lv
- Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Bingyu Yan
- Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Yang Zhou
- Zhejiang Provincial Center for Disease Control and Prevention, Dept. of Immunization Program, Division of Immunization Surveillance & Evaluation, Hangzhou, China
| | - Weijun Hu
- Immunization Program Department, Shaanxi Provincial Center for Disease Control and Prevention, Xi'an, China
| | - Yuanyuan Dong
- Immunization Program Department, Shaanxi Provincial Center for Disease Control and Prevention, Xi'an, China
| | - Biyu Chen
- Hainan Center for Disease Control and Prevention, Haikou, China
| | - Man Liu
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, China
| | - Jingyuan Cao
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Fujie Xu
- The First Affiliated Hospital, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Medicine, Zhejiang University, Hangzhou, China.,China Country Office, Bill& Melinda Gates Foundation, Beijing, China
| | - Lanjuan Li
- The First Affiliated Hospital, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Medicine, Zhejiang University, Hangzhou, China
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11
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Alkholidy GG, Anam LS, Almahaqri AH, Khader Y. Performance of the Severe Acute Respiratory Illness Sentinel Surveillance System in Yemen: Mixed Methods Evaluation Study. JMIR Public Health Surveill 2021; 7:e27621. [PMID: 34255695 PMCID: PMC8304118 DOI: 10.2196/27621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 04/25/2021] [Accepted: 05/15/2021] [Indexed: 11/30/2022] Open
Abstract
Background The national severe acute respiratory illness (SARI) surveillance system in Yemen was established in 2010 to monitor SARI occurrence in humans and provide a foundation for detecting SARI outbreaks. Objective To ensure that the objectives of national surveillance are being met, this study aimed to examine the level of usefulness and the performance of the SARI surveillance system in Yemen. Methods The updated Centers for Disease Control and Prevention guidelines were used for the purposes of our evaluation. Related documents and reports were reviewed. Data were collected from 4 central-level managers and stakeholders and from 10 focal points at 4 sentinel sites by using a semistructured questionnaire. For each attribute, percent scores were calculated and ranked as follows: very poor (≤20%), poor (20%-40%), average (40%-60%), good (60%-80%), and excellent (>80%). Results As rated by the evaluators, the SARI surveillance system achieved its objectives. The system’s flexibility (percent score: 86%) and acceptability (percent score: 82%) were rated as “excellent,” and simplicity (percent score: 74%) and stability (percent score: 75%) were rated as “good.” The percent score for timeliness was 23% in 2018, which indicated poor timeliness. The overall data quality percent score of the SARI system was 98.5%. Despite its many strengths, the SARI system has some weaknesses. For example, it depends on irregular external financial support. Conclusions The SARI surveillance system was useful in estimating morbidity and mortality, monitoring the trends of the disease, and promoting research for informing prevention and control measures. The overall performance of the SARI surveillance system was good. We recommend expanding the system by promoting private health facilities’ (eg, private hospitals and private health centers) engagement in SARI surveillance, establishing an electronic database at central and peripheral sites, and providing the National Central Public Health Laboratory with the reagents needed for disease confirmation.
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Affiliation(s)
- Ghamdan Gamal Alkholidy
- Yemen Field Epidemiology Training Program, Ministry of Public Health and Population, Sana'a, Yemen
| | - Labiba Saeed Anam
- Yemen Field Epidemiology Training Program, Ministry of Public Health and Population, Sana'a, Yemen
| | - Ali Hamoud Almahaqri
- National Influenza Control Program, Ministry of Public Health and Population, Sana'a, Yemen
| | - Yousef Khader
- Department of Community Medicine, Public Health and Family Medicine, Faculty of Medicine, Jordan University of Science & Technology, Irbidjord, Jordan
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12
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Lagare A, Rajatonirina S, Testa J, Mamadou S. The epidemiology of seasonal influenza after the 2009 influenza pandemic in Africa: a systematic review. Afr Health Sci 2020; 20:1514-1536. [PMID: 34394213 PMCID: PMC8351825 DOI: 10.4314/ahs.v20i4.5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Background Influenza infection is a serious public health problem that causes an estimated 3 to 5 million cases and 250,000 deaths worldwide every year. The epidemiology of influenza is well-documented in high- and middle-income countries, however minimal effort had been made to understand the epidemiology, burden and seasonality of influenza in Africa. This study aims to assess the state of knowledge of seasonal influenza epidemiology in Africa and identify potential data gaps for policy formulation following the 2009 pandemic. Method We reviewed articles from Africa published into four databases namely: MEDLINE (PubMed), Google Scholar, Cochrane Library and Scientific Research Publishing from 2010 to 2019. Results We screened titles and abstracts of 2070 studies of which 311 were selected for full content evaluation and 199 studies were considered. Selected articles varied substantially on the basis of the topics they addressed covering the field of influenza surveillance (n=80); influenza risk factors and co-morbidities (n=15); influenza burden (n=37); influenza vaccination (n=40); influenza and other respiratory pathogens (n=22) and influenza diagnosis (n=5). Conclusion Significant progress has been made since the last pandemic in understanding the influenza epidemiology in Africa. However, efforts still remain for most countries to have sufficient data to allow countries to prioritize strategies for influenza prevention and control.
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Affiliation(s)
- Adamou Lagare
- Centre de Recherche Médicale et Sanitaire (CERMES), Niamey, Niger
| | | | - Jean Testa
- Centre de Recherche Médicale et Sanitaire (CERMES), Niamey, Niger
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Monamele CG, Messanga Essengue LL, Ripa Njankouo M, Munshili Njifon HL, Tchatchueng J, Tejiokem MC, Njouom R. Evaluation of a mobile health approach to improve the Early Warning System of influenza surveillance in Cameroon. Influenza Other Respir Viruses 2020; 14:491-498. [PMID: 32410384 PMCID: PMC7431645 DOI: 10.1111/irv.12747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 04/12/2020] [Indexed: 11/28/2022] Open
Abstract
Background Rapid reporting of surveillance data is essential to better inform national prevention and control strategies. Objectives We compare the newly implemented smartphone‐based system to the former paper‐based and short message service (SMS) for collecting influenza epidemiological data in Cameroon. Methods Of the 13 sites which collect data from persons with influenza‐like illness (ILI), six sites send data through the EWS, while seven sites make use of the paper‐based system and SMS. We used four criteria for the comparison of the data collection tools: completeness, timeliness, conformity and cost. Results Regarding the different collection tools, data sent by the EWS were significantly more complete (97.6% vs 81.6% vs 44.8%), prompt (74.4% vs n/a vs 60.7%) and of better quality (93.7% vs 76.1% vs 84.0%) than data sent by the paper‐based system and SMS, respectively. The average cost of sending a datum by a sentinel site per week was higher for the forms (5.0 USD) than for the EWS (0.9 USD) and SMS (0.1 USD). The number of outpatient visits and subsequently all surveillance data decreased across the years 2017‐2019 together with the influenza positivity rate from 30.7% to 28.3%. Contrarily, the proportion of influenza‐associated ILI to outpatient load was highest in the year 2019 (0.37 per 100 persons vs 0.28 and 0.26 in the other 2 years). Conclusion All sentinel sites and even other disease surveillance systems are expected to use this tool in the near term future due to its satisfactory performance and cost.
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Affiliation(s)
| | | | | | | | - Jules Tchatchueng
- Laboratory of Epidemiology, Centre Pasteur of Cameroon, Yaoundé, Cameroon
| | | | - Richard Njouom
- Laboratory of Virology, Centre Pasteur of Cameroon, Yaoundé, Cameroon
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Simusika P, Tempia S, Chentulo E, Polansky L, Mazaba ML, Ndumba I, Mbewe QK, Monze M. An evaluation of the Zambia influenza sentinel surveillance system, 2011-2017. BMC Health Serv Res 2020; 20:35. [PMID: 31931793 PMCID: PMC6958603 DOI: 10.1186/s12913-019-4884-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 12/30/2019] [Indexed: 08/21/2023] Open
Abstract
Background Over the past decade, influenza surveillance has been established in several African countries including Zambia. However, information on the on data quality and reliability of established influenza surveillance systems in Africa are limited. Such information would enable countries to assess the performance of their surveillance systems, identify shortfalls for improvement and provide evidence of data reliability for policy making and public health interventions. Methods We used the Centers for Disease Control and Prevention guidelines to evaluate the performance of the influenza surveillance system (ISS) in Zambia during 2011–2017 using 9 attributes: (i) data quality and completeness, (ii) timeliness, (iii) representativeness, (iv) flexibility, (v) simplicity, (vi) acceptability, (vii) stability, (viii) utility, and (ix) sustainability. Each attribute was evaluated using pre-defined indicators. For each indicator we obtained the proportion (expressed as percentage) of the outcome of interest over the total. A scale from 1 to 3 was used to provide a score for each attribute as follows: < 60% (as obtained in the calculation above) scored 1 (weak performance); 60–79% scored 2 (moderate performance); ≥80% scored 3 (good performance). An overall score for each attribute and the ISS was obtained by averaging the scores of all evaluated attributes. Results The overall mean score for the ISS in Zambia was 2.6. Key strengths of the system were the quality of data generated (score: 2.9), its flexibility (score: 3.0) especially to monitor viral pathogens other than influenza viruses, its simplicity (score: 2.8), acceptability (score: 3.0) and stability (score: 2.6) over the review period and its relatively low cost ($310,000 per annum). Identified weaknesses related mainly to geographic representativeness (score: 2.0), timeliness (score: 2.5), especially in shipment of samples from remote sites, and sustainability (score: 1.0) in the absence of external funds. Conclusions The system performed moderately well in our evaluation. Key improvements would include improvements in the timeliness of samples shipments and geographical coverage. However, these improvements would result in increased cost and logistical complexity. The ISSS in Zambia is largely reliant on external funds and the acceptability of maintaining the surveillance system through national funds would require evaluation.
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Affiliation(s)
- Paul Simusika
- National Influenza Center, Virology Laboratory, University Teaching Hospital, Lusaka, Zambia.
| | - Stefano Tempia
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.,Influenza Program, Centers for Disease Control and Prevention, Pretoria, South Africa.,MassGenics, Duluth, GA, USA
| | - Edward Chentulo
- National Influenza Center, Virology Laboratory, University Teaching Hospital, Lusaka, Zambia
| | - Lauren Polansky
- Influenza Program, Centers for Disease Control and Prevention, Pretoria, South Africa
| | - Mazyanga Lucy Mazaba
- National Influenza Center, Virology Laboratory, University Teaching Hospital, Lusaka, Zambia
| | - Idah Ndumba
- National Influenza Center, Virology Laboratory, University Teaching Hospital, Lusaka, Zambia
| | - Quinn K Mbewe
- National Influenza Center, Virology Laboratory, University Teaching Hospital, Lusaka, Zambia
| | - Mwaka Monze
- National Influenza Center, Virology Laboratory, University Teaching Hospital, Lusaka, Zambia.
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