Sentinel Syndromic Surveillance Research Articles

Shifting Patterns of Influenza Circulation during the COVID-19 Pandemic, Senegal.

Historically low levels of seasonal influenza circulation were reported during the first years of the COVID-19 pandemic and were mainly attributed to implementation of nonpharmaceutical interventions. In tropical regions, influenza's seasonality differs largely, and data on this topic are scarce. We analyzed data from Senegal's sentinel syndromic surveillance network before and after the start of the COVID-19 pandemic to assess changes in influenza circulation. We found that influenza shows year-round circulation in Senegal and has 2 distinct epidemic peaks: during January-March and during the rainy season in August-October. During 2021-2022, the expected January-March influenza peak completely disappeared, corresponding to periods of active SARS-CoV-2 circulation. We noted an unexpected influenza epidemic peak during May-July 2022. The observed reciprocal circulation of SARS-CoV-2 and influenza suggests that factors such as viral interference might be at play and should be further investigated in tropical settings.

Re-Introduction of West Nile Virus Lineage 1 in Senegal from Europe and Subsequent Circulation in Human and Mosquito Populations between 2012 and 2021.

West Nile virus (WNV) is a virus of the Japanese encephalitis antigenic complex and belongs to the family Flaviviridae of the genus flavivirus. The virus can cause infection in humans which in most cases is asymptomatic, however symptomatic cases exist and the disease can be severe causing encephalitis and meningoencephalitis. The virus is maintained in an enzootic cycle involving mosquitoes and birds, humans and other mammals such as horses can be accidental hosts. A mosquito-based arbovirus surveillance system and the sentinel syndromic surveillance network (4S) have been in place since 1988 and 2015 respectively, to better understand the transmission dynamics of arboviruses including WNV in Senegal. Arthropod and human samples have been collected from the field and analysed at Institut Pasteur de Dakar using different methods including RT-PCR, ELISA, plaque reduction neutralization test and viral isolation. RT-PCR positive samples have been analysed by Next Generation Sequencing. From 2012 to 2021, 7912 samples have been analysed and WNV positive cases have been detected, 20 human cases (19 IgM and 1 RT-PCR positive cases) and 41 mosquito pools. Phylogenetic analyzes of the sequences of complete genomes obtained showed the circulation of lineage 1a, with all these recent strains from Senegal identical to each other and very close to strains isolated from horse in France in 2015, Italy and Spain. Our data showed lineage 1a endemicity in Senegal as previously described, with circulation of WNV in humans and mosquitoes. Phylogenetic analyzes carried out with the genome sequences obtained also revealed exchanges of WNV strains between Europe and Senegal which could be possible via migratory birds. The surveillance systems that have enabled the detection of WNV in humans and arthropods should be extended to animals in a one-health approach to better prepare for global health threats.

Detection of Rift Valley Fever Virus Lineage H From South Africa Through the Syndromic Sentinel Surveillance Network in Senegal.

BackgroundRift Valley fever virus (RVFV) is an arbovirus that causes epizootics and epidemics among livestock population and humans. Our surveillance system has revealed multiple emergences and re-emergences of RVFV in West Africa over the last decade.MethodsThe Sentinel Syndromic Surveillance Network in Senegal (4S) has been implemented since 2011. Samples from human suspected arbovirus infection in 4S sentinel sites were sent to Institut Pasteur de Dakar (IPD), where arbovirus diagnosis by enzyme-linked immunosorbent assay (ELISA), real-time reverse transcription polymerase chain reaction (RT-PCR), and virus isolation was performed. Overall, IPD has received a total of 1149 samples from arboviral suspected patients through the 4S network from January to December 2020. These samples were screened for 7 arboviruses including RVFV. Whole-genome sequencing of positive RVFV samples by RT-PCR was performed using the Illumina Miseq platform followed by genome assembly. Phylogenetic analyses were performed using MEGA X.ResultsOut of the 1149 arbovirus suspected cases, 4 RVFV-positive samples were detected with RT-PCR while 5 RVFV-positive samples were detected by ELISA. Complete genome sequences were obtained for 3 strains among the 4 positive samples by RT-PCR. Phylogenetic analyses indicated an emergence of a virus first described in South Africa during a major outbreak.ConclusionsThis strong surveillance system allowed the detection of an RVFV outbreak in Senegal in 2020. The obtained genomes clustered with strains from South Africa belonging to lineage H. This calls for implementation of a strong surveillance system for wild animals, humans, and livestock simultaneously in all African countries.

Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic.

This study describes the development of a pilot sentinel school absence syndromic surveillance system. Using data from a sample of schools in England the capability of this system to monitor the impact of disease on school absences in school-aged children is shown, using the coronavirus disease 2019 (COVID-19) pandemic period as an example. Data were obtained from an online app service used by schools and parents to report their children absent, including reasons/symptoms relating to absence. For 2019 and 2020, data were aggregated into daily counts of 'total' and 'cough' absence reports. There was a large increase in the number of absence reports in March 2020 compared to March 2019, corresponding to the first wave of the COVID-19 pandemic in England. Absence numbers then fell rapidly and remained low from late March 2020 until August 2020, while lockdown was in place in England. Compared to 2019, there was a large increase in the number of absence reports in September 2020 when schools re-opened in England, although the peak number of absences was smaller than in March 2020. This information can help provide context around the absence levels in schools associated with COVID-19. Also, the system has the potential for further development to monitor the impact of other conditions on school absence, e.g. gastrointestinal infections.

An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses

Coronavirus Disease 2019 has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is a significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019–2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.

Revising rates of asymptomatic Zika virus infection based on sentinel surveillance data from French Overseas Territories

French Polynesia and the French Territories of the Americas (FTAs) have experienced outbreaks of Zika virus (ZIKV) infection. These territories used similar sentinel syndromic surveillance to follow the epidemics. However, the surveillance system only takes into account consulting patients diagnosed with ZIKV disease, while non-consulting cases, as well as asymptomatic cases, are not taken into account. In the French territories under study, the ratio of consulting to non-consulting patients was found to likely be as low as 1/3 to 1/4, and rough estimates of the ZIKV asymptomatic infections indicated a lower rate than previously reported (i.e., not more than half).

Suitability of sentinel abattoirs for syndromic surveillance using provincially inspected bovine abattoir condemnation data.

BackgroundSentinel surveillance has previously been used to monitor and identify disease outbreaks in both human and animal contexts. Three approaches for the selection of sentinel sites are proposed and evaluated regarding their ability to capture overall respiratory disease trends using provincial abattoir condemnation data from all abattoirs open throughout the study for use in a sentinel syndromic surveillance system.ResultsAll three sentinel selection criteria approaches resulted in the identification of sentinel abattoirs that captured overall temporal trends in condemnation rates similar to those reported by the full set of abattoirs. However, all selection approaches tended to overestimate the condemnation rates of the full dataset by 1.4 to as high as 3.8 times for cows, heifers and steers. Given the results, the selection approach using abattoirs open all weeks had the closest approximation of temporal trends when compared to the full set of abattoirs.ConclusionsSentinel abattoirs show promise for integration into a food animal syndromic surveillance system using Ontario provincial abattoir condemnation data. While all selection approaches tended to overestimate the condemnation rates of the full dataset to some degree, the abattoirs open all weeks selection approach appeared to best capture the overall seasonal and temporal trends of the full dataset and would be the most suitable approach for sentinel abattoir selection.

Establishing an emergency department syndromic surveillance system to support the London 2012 Olympic and Paralympic Games

BackgroundThe London 2012 Olympic and Paralympic Games is a mass gathering event that will present a major public health challenge. The Health Protection Agency, in collaboration with the College of...