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Influenza viruses are prone to mutation, necessitating long-term and systematic monitoring to identify their epidemic patterns and mutations promptly, and to inform the selection of vaccine strains and disease prevention and control strategies [13]. The results showed that both ILI% and the positive rate of influenza virus detection in Zhejiang Province in 2020 decreased significantly compared with that before the COVID-19 pandemic, remaining at a low epidemic level throughout the year without obvious seasonality, which was similar to the national influenza epidemic situation during the same period [14]. This may be attributed to the implementation of stringent nonpharmaceutical interventions (NPIs) against the COVID-19 in China from January 20, 2020, and the adoption of various public health prevention and control measures, which effectively prevented and controlled the COVID-19 outbreak and curtailed the spread of influenza [15, 16]. In 2021, the COVID-19 epidemic in China stabilized, and social production and life gradually returned, people’s awareness of prevention and control waned, leading to a resurgence in the influenza epidemic, with a peak in winter and spring. The influenza epidemic in 2022 featured three peaks in July, December, and March of the following year, with the positive rate of influenza virus nucleic acid detection similar to that of 2019 before the COVID-19 epidemic. The July peak may be related to factors such as the geographical location of Zhejiang Province [17]. Zhejiang Province is located in the southeast coastal area, during summer, it is often affected by typhoons and the rainy season, resulting in a decrease in temperature, which provides ideal conditions for the survival and spread of influenza viruses. Additionally, July is the summer vacation period in China, increased population mobility activities such as traveling and gatherings have accelerated the spread of influenza virus. ILI% showed a rapid upward trend from November 2022, peaking in December, but the positive detection rate of influenza virus during the same period was very low, suggesting that the rapid increase in ILI% maybe associated with the rapid and high-intensity spread of the COVID-19, and the majority of ILI patients were infected with SARS-CoV-2 [18]. Research findings reveal that in the context of the COVID-19 pandemic, influenza surveillance should integrate the consistency between ILI% and the positive detection rate of influenza virus, and simultaneously test for both influenza virus and SARS-CoV-2 in ILI and SARI samples to monitor the impact of COVID-19. In early 2023, the number of ILI cases in Zhejiang Province increased rapidly and peaked in March, primarily driven by A (H1N1) influenza viruses. This may be related to the easing of COVID-19-related NPIs of China on January 8, 2023, and the gradual resumption of social order and travel activities following the adjustment of various control measures. Additionally, the immunological gap related to the COVID-19 [19], the reduced immune function caused by SARS-CoV-2 [20, 21], and seasonal factors [22] are also important reasons for the post-COVID-19 influenza outbreak. Similar “triple outbreaks” of influenza, respiratory syncytial virus (RSV), and the COVID-19 have been observed in Europe and the United States since the release of the COVID-19 pandemic [23]. From a population distribution perspective, ILI cases in Zhejiang Province were predominantly concentrated in children under 5 years old, with fewer cases among the elderly. This is because children, due to their weaker immune systems, are a high-risk group for influenza infection and typically seek medical attention for symptoms such as fever and cough, whereas the elderly may seek treatment for other symptoms [24].

Etiological monitoring results showed that the positive rate of influenza virus varied across different years, with different dominant strains, exhibiting a general pattern of alternating A and B types. The primary reason may be that when a particular strain is circulating, the population gains some immunity, reducing the likelihood of reinfection with the same strain in subsequent years. Influenza virus positive rates were highest in children under 15 years old. Among younger populations, the proportions of influenza A and B were higher, while in older populations, the proportion of A (H3N2) was higher and the proportion of influenza B was lower. These findings are consistent with previous studies [25]. Notably, influenza B (Yamagata) cases were detected in 2018–2019 but were nearly absent during the COVID-19 pandemic, which was basically in line with the global epidemic trend of influenza B (Yamagata) [26]. This possibly related to the inherent vulnerability of this lineage. Indeed, the B (Yamagata) lineage has low antigenic diversity [27], lower effective reproduction number, and a shorter transmission chain compared with the B (Victoria) lineage [26], making non-pharmaceutical interventions more effective in controlling the B (Yamagata) lineage. Furthermore, the previously long-lived clades of B (Yamagata) went extinct maybe another reason for the decline of B (Yamagata) [28]. Lastly, in 2008–2019, the B (Yamagata) lineage demonstrated greater global movement than B (Victoria) lineage [28]. The B (Yamagata) lineage may have already been at a low prevalence cycle at the beginning of COVID-19 pandemic.

Influenza surveillance is conducted globally according to the ILI definition recommended by WHO. Theoretically, the positive rate of influenza virus nucleic acid detection should be positively correlated with the number of ILI cases. However, this study found that after the pandemic, the positive rate curve of influenza virus nucleic acid detection in the 0–4 years group did not align with the ILI curve, with more pronounced peaks and troughs. In particular, in 2020, after the implementation of strict prevention and control measures for the COVID-19 epidemic, the correlation between ILI number and the influenza virus positive rate has no significant difference in children under 15 years old, suggesting that children and infants often have multiple respiratory pathogen infections, the current definition of ILI may not be suitable for influenza surveillance in children.

After the COVID-19 pandemic, respiratory syncytial virus, adenovirus, mycoplasma pneumoniae, and other respiratory infectious diseases have superimposed in China [29], presenting new challenges for influenza prevention and control. Influenza vaccination remains the most effective means for preventing influenza, and efforts should be made to improve vaccination rates through optimized vaccination services and policy advocacy, particularly focusing on key groups such as healthcare workers, the elderly, young children, and individuals with chronic diseases. Public health authorities should enhance the dissemination of influenza prevention and control knowledge, timely issue influenza epidemic warnings and risk assessments, and raise public awareness of self-prevention and control. Emphasizing the mechanism of multi-disease prevention and control for respiratory infectious diseases and incorporating respiratory multi-pathogen surveillance into the influenza sentinel surveillance network is crucial for the precise prevention and control of influenza and other respiratory infectious diseases.

To ensure the quality and level of influenza network surveillance, the Zhejiang Provincial Center for Disease Control and Prevention annually evaluated the quality of influenza surveillance work of collaborating laboratories and sentinel hospitals in various cities every year. The evaluation mainly includes the completeness and timeliness of the monitoring data report, pathogen detection, specimen collection, and other related aspects. The influenza monitoring tasks in all cities were all completed satisfactorily. However, our study still has some limitations. First, we did not test for any pathogens other than influenza, which prevented us from ruling out other viral, bacterial, and fungal pathogens that could cause ILI. So, it is difficult to determine whether the number of ILI cases is caused by SARS-CoV-2. Second, due to the unavailability of influenza vaccination rates across years and age groups of Zhejiang province, leading us unable to conform the exact impact of the COVID-19 pandemic on influenza.

A Europe-wide analysis reveals how geography, culture, and environment shape the risk of Toxoplasma gondii infection, offering new insights for public health and prevention.

Review: Systematic review and modelling of Toxoplasma gondii seroprevalence in humans, Europe, 2000 to 2021. Image Credit: fotovapl / Shutterstock

In a recent study published in the journal Eurosurveillance, researchers analyzed the seroprevalence of Toxoplasma gondii in Europe.

T. gondii is a zoonotic protozoan parasite that infects humans and warm-blooded vertebrates, with felids as its definitive host. When ingested, it replicates in the felid’s intestine and sheds oocysts into the environment via feces, which can sporulate and survive for prolonged periods. Ingestion of sporulated oocysts can lead to the formation of tissue cysts in susceptible hosts.

Bradyzoites in tissue cysts are infectious, allowing transmission from infected hosts through the consumption of raw or undercooked meat. Humans can acquire infections via the consumption of raw/undercooked meat or through environmental exposure. Besides, placental transmission to the fetus is another route, causing congenital infection leading to stillbirth or abortion. Organ transplantation and blood transfusion also transmit T. gondii.

T. gondii infection is usually asymptomatic or causes non-specific, self-limiting symptoms in humans, but it can also manifest as ocular toxoplasmosis. Although severe acute toxoplasmosis is rare, it can present as polymyositis, retinitis, myocarditis, encephalitis, pneumonitis, or hepatitis, and occurs in individuals with weakened immunity.

About the study

In the present study, researchers analyzed the seroprevalence of T. gondii in Europe. First, a literature search was conducted within the Embase database using relevant terms that covered human seroprevalence and risk factors of T. gondii infection in 41 European countries, with a publication period ranging from January 2000 to May 2021.

Studies were eligible if they reported original data and evaluated risk factors and/or seroprevalence of T. gondii infection. Meta-analyses, reviews, studies not reporting original data, and those evaluating T. gondii prevalence in specific risk groups were excluded from the analysis. Identified records were assessed for eligibility by 17 scientists from 12 countries.

Titles and abstracts were screened, followed by full-text analysis and data extraction. Extracted data included study design, population, period, serological tests, and results. Seroprevalence data were harmonized and categorized for modeling, and countries were stratified into northern, eastern, western, southwestern, and southeastern European regions.

The team developed a Bayesian hierarchical model to estimate the age-dependent seroprevalence of T. gondii. The susceptible-infected-susceptible framework was adopted, wherein individuals transition from the seronegative (susceptible) to the seropositive (infected) state, with reversion to the seronegative status also possible.

The authors noted that partial pooling across countries was used to compensate for sparse data in some regions, which increased uncertainty in those estimates.

Findings

In total, the literature search yielded 1,822 records. Following deduplication and title/abstract screening, full texts of 367 publications were analyzed. Of these, 69 studies reported seroprevalence data and 22 provided risk factor data. Seroprevalence data were obtained for 25 countries. The United Kingdom (UK) was analyzed separately due to its markedly lower seroprevalence compared to Western Europe, and its estimates were based on only three studies.

Eastern Europe had the highest mean T. gondii seroprevalence at 50%, followed by western, southeastern, and southwestern regions at 48%, 45%, and 38%, respectively. The UK and northern Europe had markedly lower seroprevalence (18%).

Seroprevalence estimates increased with age, from approximately 13%–16% in people aged ≤25 to 52–68% in those aged >50 in eastern to southwestern Europe. In the UK and northern Europe, estimates rose from 4% to 26–27%.

The UK and northern Europe had the lowest force of infection, i.e., the rate at which infection is acquired, followed by southwestern and southeastern Europe. The western and eastern regions had the highest forces of infection.

The median age at infection was 44 years in Eastern Europe, meaning that half the population had become infected by that age, while the corresponding figure in Western Europe was 47 years. In contrast, the mean waiting time to infection was 64 years in Eastern Europe, but exceeded 250 years in the UK and Northern Europe. However, the distribution was skewed, and about 10% of individuals were already infected by their mid-twenties in these low-force-of-infection regions.

Although the estimated rate of reversion was extremely low (9 × 10⁻⁴), the wide uncertainty interval suggests that some individuals may lose detectable antibodies within decades, indicating that infection does not always guarantee lifelong protection.

Conclusions

In summary, the study modeled age-dependent prevalence of T. gondii in the European population. The findings highlight substantial differences in seroprevalence across geographic regions in Europe. Seroprevalence was highest in the eastern, western, and southeastern regions, and lowest in northern Europe and the UK. The prevalence ranged from 13% to 43% in people aged 25–50, with notable regional differences.

Eastern and Western Europe had the highest rates of infection, implying that individuals became infected at a younger age compared to other regions. This meant that half of Western and Eastern Europe’s population was expected to be infected by age 47, compared to unreachable ages exceeding 170 years in the UK and northern Europe.

The authors emphasized that estimates may be affected by heterogeneity in diagnostic test performance and unmeasured factors, such as sex, which were not accounted for in the model. They also noted that 16 countries lacked eligible data, that regional differences may reflect cultural habits such as consumption of raw or undercooked meat and varying environmental exposures (e.g., soil or unwashed produce), and that human infection trends parallel those seen in animals, supporting a “One Health” perspective. 

The authors suggested that regional differences might stem from cultural practices, such as eating raw or undercooked meat, and environmental exposures like contact with contaminated soil or unwashed produce. They also observed that human infection patterns mirror those seen in animals, emphasizing a ‘One Health’ connection between the environment, livestock, and people.

The paper concluded with a recommendation to adopt standardized templates for reporting seroepidemiological data to improve comparability and future meta-analyses.